research paper topics in banking and finance

Research Topics & Ideas: Finance

120+ Finance Research Topic Ideas To Fast-Track Your Project

If you’re just starting out exploring potential research topics for your finance-related dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research topic ideation process by providing a hearty list of finance-centric research topics and ideas.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . To develop a suitable education-related research topic, you’ll need to identify a clear and convincing research gap , and a viable plan of action to fill that gap.

If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, if you’d like hands-on help, consider our 1-on-1 coaching service .

Overview: Finance Research Topics

• Corporate finance topics
• Investment banking topics
• Private equity & VC
• Asset management
• Hedge funds
• Financial planning & advisory
• Quantitative finance
• Treasury management
• Financial technology (FinTech)
• Commercial banking
• International finance

Corporate Finance

These research topic ideas explore a breadth of issues ranging from the examination of capital structure to the exploration of financial strategies in mergers and acquisitions.

• Evaluating the impact of capital structure on firm performance across different industries
• Assessing the effectiveness of financial management practices in emerging markets
• A comparative analysis of the cost of capital and financial structure in multinational corporations across different regulatory environments
• Examining how integrating sustainability and CSR initiatives affect a corporation’s financial performance and brand reputation
• Analysing how rigorous financial analysis informs strategic decisions and contributes to corporate growth
• Examining the relationship between corporate governance structures and financial performance
• A comparative analysis of financing strategies among mergers and acquisitions
• Evaluating the importance of financial transparency and its impact on investor relations and trust
• Investigating the role of financial flexibility in strategic investment decisions during economic downturns
• Investigating how different dividend policies affect shareholder value and the firm’s financial performance

Investment Banking

The list below presents a series of research topics exploring the multifaceted dimensions of investment banking, with a particular focus on its evolution following the 2008 financial crisis.

• Analysing the evolution and impact of regulatory frameworks in investment banking post-2008 financial crisis
• Investigating the challenges and opportunities associated with cross-border M&As facilitated by investment banks.
• Evaluating the role of investment banks in facilitating mergers and acquisitions in emerging markets
• Analysing the transformation brought about by digital technologies in the delivery of investment banking services and its effects on efficiency and client satisfaction.
• Evaluating the role of investment banks in promoting sustainable finance and the integration of Environmental, Social, and Governance (ESG) criteria in investment decisions.
• Assessing the impact of technology on the efficiency and effectiveness of investment banking services
• Examining the effectiveness of investment banks in pricing and marketing IPOs, and the subsequent performance of these IPOs in the stock market.
• A comparative analysis of different risk management strategies employed by investment banks
• Examining the relationship between investment banking fees and corporate performance
• A comparative analysis of competitive strategies employed by leading investment banks and their impact on market share and profitability

Private Equity & Venture Capital (VC)

These research topic ideas are centred on venture capital and private equity investments, with a focus on their impact on technological startups, emerging technologies, and broader economic ecosystems.

• Investigating the determinants of successful venture capital investments in tech startups
• Analysing the trends and outcomes of venture capital funding in emerging technologies such as artificial intelligence, blockchain, or clean energy
• Assessing the performance and return on investment of different exit strategies employed by venture capital firms
• Assessing the impact of private equity investments on the financial performance of SMEs
• Analysing the role of venture capital in fostering innovation and entrepreneurship
• Evaluating the exit strategies of private equity firms: A comparative analysis
• Exploring the ethical considerations in private equity and venture capital financing
• Investigating how private equity ownership influences operational efficiency and overall business performance
• Evaluating the effectiveness of corporate governance structures in companies backed by private equity investments
• Examining how the regulatory environment in different regions affects the operations, investments and performance of private equity and venture capital firms

Asset Management

This list includes a range of research topic ideas focused on asset management, probing into the effectiveness of various strategies, the integration of technology, and the alignment with ethical principles among other key dimensions.

• Analysing the effectiveness of different asset allocation strategies in diverse economic environments
• Analysing the methodologies and effectiveness of performance attribution in asset management firms
• Assessing the impact of environmental, social, and governance (ESG) criteria on fund performance
• Examining the role of robo-advisors in modern asset management
• Evaluating how advancements in technology are reshaping portfolio management strategies within asset management firms
• Evaluating the performance persistence of mutual funds and hedge funds
• Investigating the long-term performance of portfolios managed with ethical or socially responsible investing principles
• Investigating the behavioural biases in individual and institutional investment decisions
• Examining the asset allocation strategies employed by pension funds and their impact on long-term fund performance
• Assessing the operational efficiency of asset management firms and its correlation with fund performance

Hedge Funds

Here we explore research topics related to hedge fund operations and strategies, including their implications on corporate governance, financial market stability, and regulatory compliance among other critical facets.

• Assessing the impact of hedge fund activism on corporate governance and financial performance
• Analysing the effectiveness and implications of market-neutral strategies employed by hedge funds
• Investigating how different fee structures impact the performance and investor attraction to hedge funds
• Evaluating the contribution of hedge funds to financial market liquidity and the implications for market stability
• Analysing the risk-return profile of hedge fund strategies during financial crises
• Evaluating the influence of regulatory changes on hedge fund operations and performance
• Examining the level of transparency and disclosure practices in the hedge fund industry and its impact on investor trust and regulatory compliance
• Assessing the contribution of hedge funds to systemic risk in financial markets, and the effectiveness of regulatory measures in mitigating such risks
• Examining the role of hedge funds in financial market stability
• Investigating the determinants of hedge fund success: A comparative analysis

Financial Planning and Advisory

This list explores various research topic ideas related to financial planning, focusing on the effects of financial literacy, the adoption of digital tools, taxation policies, and the role of financial advisors.

• Evaluating the impact of financial literacy on individual financial planning effectiveness
• Analysing how different taxation policies influence financial planning strategies among individuals and businesses
• Evaluating the effectiveness and user adoption of digital tools in modern financial planning practices
• Investigating the adequacy of long-term financial planning strategies in ensuring retirement security
• Assessing the role of financial education in shaping financial planning behaviour among different demographic groups
• Examining the impact of psychological biases on financial planning and decision-making, and strategies to mitigate these biases
• Assessing the behavioural factors influencing financial planning decisions
• Examining the role of financial advisors in managing retirement savings
• A comparative analysis of traditional versus robo-advisory in financial planning
• Investigating the ethics of financial advisory practices

The following list delves into research topics within the insurance sector, touching on the technological transformations, regulatory shifts, and evolving consumer behaviours among other pivotal aspects.

• Analysing the impact of technology adoption on insurance pricing and risk management
• Analysing the influence of Insurtech innovations on the competitive dynamics and consumer choices in insurance markets
• Investigating the factors affecting consumer behaviour in insurance product selection and the role of digital channels in influencing decisions
• Assessing the effect of regulatory changes on insurance product offerings
• Examining the determinants of insurance penetration in emerging markets
• Evaluating the operational efficiency of claims management processes in insurance companies and its impact on customer satisfaction
• Examining the evolution and effectiveness of risk assessment models used in insurance underwriting and their impact on pricing and coverage
• Evaluating the role of insurance in financial stability and economic development
• Investigating the impact of climate change on insurance models and products
• Exploring the challenges and opportunities in underwriting cyber insurance in the face of evolving cyber threats and regulations

Quantitative Finance

These topic ideas span the development of asset pricing models, evaluation of machine learning algorithms, and the exploration of ethical implications among other pivotal areas.

• Developing and testing new quantitative models for asset pricing
• Analysing the effectiveness and limitations of machine learning algorithms in predicting financial market movements
• Assessing the effectiveness of various risk management techniques in quantitative finance
• Evaluating the advancements in portfolio optimisation techniques and their impact on risk-adjusted returns
• Evaluating the impact of high-frequency trading on market efficiency and stability
• Investigating the influence of algorithmic trading strategies on market efficiency and liquidity
• Examining the risk parity approach in asset allocation and its effectiveness in different market conditions
• Examining the application of machine learning and artificial intelligence in quantitative financial analysis
• Investigating the ethical implications of quantitative financial innovations
• Assessing the profitability and market impact of statistical arbitrage strategies considering different market microstructures

Treasury Management

The following topic ideas explore treasury management, focusing on modernisation through technological advancements, the impact on firm liquidity, and the intertwined relationship with corporate governance among other crucial areas.

• Analysing the impact of treasury management practices on firm liquidity and profitability
• Analysing the role of automation in enhancing operational efficiency and strategic decision-making in treasury management
• Evaluating the effectiveness of various cash management strategies in multinational corporations
• Investigating the potential of blockchain technology in streamlining treasury operations and enhancing transparency
• Examining the role of treasury management in mitigating financial risks
• Evaluating the accuracy and effectiveness of various cash flow forecasting techniques employed in treasury management
• Assessing the impact of technological advancements on treasury management operations
• Examining the effectiveness of different foreign exchange risk management strategies employed by treasury managers in multinational corporations
• Assessing the impact of regulatory compliance requirements on the operational and strategic aspects of treasury management
• Investigating the relationship between treasury management and corporate governance

Financial Technology (FinTech)

The following research topic ideas explore the transformative potential of blockchain, the rise of open banking, and the burgeoning landscape of peer-to-peer lending among other focal areas.

• Evaluating the impact of blockchain technology on financial services
• Investigating the implications of open banking on consumer data privacy and financial services competition
• Assessing the role of FinTech in financial inclusion in emerging markets
• Analysing the role of peer-to-peer lending platforms in promoting financial inclusion and their impact on traditional banking systems
• Examining the cybersecurity challenges faced by FinTech firms and the regulatory measures to ensure data protection and financial stability
• Examining the regulatory challenges and opportunities in the FinTech ecosystem
• Assessing the impact of artificial intelligence on the delivery of financial services, customer experience, and operational efficiency within FinTech firms
• Analysing the adoption and impact of cryptocurrencies on traditional financial systems
• Investigating the determinants of success for FinTech startups

Commercial Banking

These topic ideas span commercial banking, encompassing digital transformation, support for small and medium-sized enterprises (SMEs), and the evolving regulatory and competitive landscape among other key themes.

• Assessing the impact of digital transformation on commercial banking services and competitiveness
• Analysing the impact of digital transformation on customer experience and operational efficiency in commercial banking
• Evaluating the role of commercial banks in supporting small and medium-sized enterprises (SMEs)
• Investigating the effectiveness of credit risk management practices and their impact on bank profitability and financial stability
• Examining the relationship between commercial banking practices and financial stability
• Evaluating the implications of open banking frameworks on the competitive landscape and service innovation in commercial banking
• Assessing how regulatory changes affect lending practices and risk appetite of commercial banks
• Examining how commercial banks are adapting their strategies in response to competition from FinTech firms and changing consumer preferences
• Analysing the impact of regulatory compliance on commercial banking operations
• Investigating the determinants of customer satisfaction and loyalty in commercial banking

International Finance

The folowing research topic ideas are centred around international finance and global economic dynamics, delving into aspects like exchange rate fluctuations, international financial regulations, and the role of international financial institutions among other pivotal areas.

• Analysing the determinants of exchange rate fluctuations and their impact on international trade
• Analysing the influence of global trade agreements on international financial flows and foreign direct investments
• Evaluating the effectiveness of international portfolio diversification strategies in mitigating risks and enhancing returns
• Evaluating the role of international financial institutions in global financial stability
• Investigating the role and implications of offshore financial centres on international financial stability and regulatory harmonisation
• Examining the impact of global financial crises on emerging market economies
• Examining the challenges and regulatory frameworks associated with cross-border banking operations
• Assessing the effectiveness of international financial regulations
• Investigating the challenges and opportunities of cross-border mergers and acquisitions

Choosing A Research Topic

These finance-related research topic ideas are starting points to guide your thinking. They are intentionally very broad and open-ended. By engaging with the currently literature in your field of interest, you’ll be able to narrow down your focus to a specific research gap .

When choosing a topic , you’ll need to take into account its originality, relevance, feasibility, and the resources you have at your disposal. Make sure to align your interest and expertise in the subject with your university program’s specific requirements. Always consult your academic advisor to ensure that your chosen topic not only meets the academic criteria but also provides a valuable contribution to the field. 

If you need a helping hand, feel free to check out our private coaching service here.

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151+ Good Banking And Finance Research Topics For Students [2024 Updated]

Are you curious about Banking and Finance Research Topics? In this blog, we explore various banking and finance-related research topics. What drives the banking sector’s resilience in the face of challenges? How do financial markets influence our economic well-being? 

Let’s find the good topics of personal finance, corporate decision-making, risk management, and more. From the fundamental principles of accounting to the latest trends in fintech, this collection of research topics spans various fields, offering a comprehensive view of the ever-evolving finance domain.

Discover the impact of digital currencies, the role of central banks, and the effectiveness of credit scoring models. Explore the importance of real estate finance and know the behavioral aspects influencing investment decisions. We also examine the intersection of finance with emerging technologies and its role in sustainable development. 

Whether you are a student researching finance or the banking sector with good research ideas about economic difficulties. These Banking and Finance Research Topics provide a gateway to understanding the pivotal role finance plays in our global society. Let’s know all about them here. 

Table of Contents

What Is Banking And Finance Research Topics?

Banking and finance research topics refer to specific questions that researchers investigate related to financial systems and institutions. These topics help explore how banks, investments, financial markets, and economic policies work.

Some examples of banking and finance research topics include:

• How new technologies like mobile apps are changing banking
• What causes stock market prices to rise and fall
• How government regulations impact financial institutions
• Why do people make certain financial decisions?
• Ways to improve risk management for banks
• The future of cryptocurrencies as an investment
• How fintech companies are competing with traditional banks

Researching these topics aims to gain a deeper understanding of the financial world. The knowledge can then be used to inform better policies, practices, and decisions related to banking and finance.

How To Find Banking And Finance Research Topics For Students?

Here are some tips for students on finding good banking and finance research topics:

• Look at current events in the banking and finance industries for inspiration. Pay attention to what’s happening with major banks, new technologies, economic policies, financial crises, and industry trends.
• Review finance publications, academic journals, magazines, and websites to discover recent research studies related to banking and see what knowledge gaps they identify that require further investigation.
• Browse research paper databases for sample banking and finance essays to find potential topics or note areas requiring additional up-to-date research.
• Align topics with your existing interests and course curriculum. If you enjoy technology, explore fintech questions. If macroeconomics fascinates you, investigate the implications of monetary policies.
• Consider meaningful real-life research questions, like how underprivileged groups are financially underserved or how developing nations can gain affordable banking access.
• Brainstorm ideas and get input from professors who will guide you in refining topics based on viability, available data sources, analytical methods, and relevance to the current finance field.

List of Good Banking And Finance Research Topics

Here are the most interesting banking and finance research topics:

Good Banking And Finance Research Topics For Students

• Comparative analysis of traditional banking vs. online banking.
• The impact of mergers and acquisitions on bank performance.
• Assessing the role of central banks in ensuring financial stability.
• Investigating the effectiveness of bank stress tests in predicting financial crises.
• Analyzing the factors influencing customer satisfaction in banking services.
• The role of blockchain technology in enhancing banking security.
• Examining the impact of interest rate fluctuations on bank profitability.
• Evaluating the role of government intervention in preventing bank failures.
• Analyzing the challenges and opportunities of Islamic banking.
• The impact of Basel III regulations on banking risk management.

Best Banking And Finance Sector Research Topics For MBA Students

• The role of the stock market in economic development.
• Examining the factors affecting stock market volatility.
• Impact of high-frequency trading on financial markets.
• Exploring the relationship between corporate governance and stock prices.
• The role of derivatives in managing financial market risks.
• Analyzing the impact of macroeconomic indicators on stock prices.
• The role of insider trading in financial markets.
• Investigating the efficiency of emerging financial markets.
• The impact of market sentiment on stock prices.
• Analyzing the role of financial analysts in shaping market perceptions.

Personal Finance-Related Research Topics

• The impact of financial literacy on personal finance management.
• Evaluating the effectiveness of budgeting tools in personal finance.
• The role of behavioral economics in understanding individual investment decisions.
• Investigating the factors influencing retirement savings decisions.
• The impact of socio-economic factors on household debt levels.
• Assessing the effectiveness of financial planning in achieving financial goals.
• The role of technology in personal financial management.
• Analyzing the impact of tax policies on personal savings.
• The relationship between education and income levels in personal finance.
• Investigating the role of psychological biases in personal investment decisions.

Corporate Banking And Finance Research Topics

• The impact of capital structure on firm profitability.
• Evaluating the role of financial leverage in corporate decision-making.
• Analyzing the factors influencing dividend payout policies.
• The impact of corporate governance on firm performance.
• Investigating the relationship between CEO compensation and firm performance.
• The role of working capital management in corporate finance.
• Analyzing the impact of exchange rate fluctuations on multinational corporations.
• The influence of financial disclosure on investor decisions.
• Evaluating the impact of corporate social responsibility on shareholder value.
• The role of venture capital in financing innovation and startups.

Risk Management Research Topics For College Students

• The impact of credit risk on financial institutions.
• Analyzing the role of derivatives in hedging financial risks.
• Evaluating the effectiveness of value-at-risk (VaR) models in risk management.
• The impact of operational risk on financial institutions.
• Exploring the relationship between risk-taking and financial performance.
• Analyzing the role of insurance in managing financial risks.
• The impact of climate change on financial risk assessment.
• Evaluating the role of stress testing in assessing systemic risk.
• The influence of cyber threats on financial institutions’ risk management.
• The role of artificial intelligence in enhancing risk management practices.

Accounting and Auditing Research Topics

• Analyzing the impact of International Financial Reporting Standards (IFRS) on financial reporting quality.
• Evaluating the role of forensic accounting in fraud detection.
• The impact of audit quality on financial statement reliability.
• Investigating the role of auditor independence in ensuring financial transparency.
• Analyzing the effectiveness of fair value accounting in financial reporting.
• The influence of accounting conservatism on financial decision-making.
• Evaluating the impact of accounting information on investment decisions.
• The role of big data analytics in modern accounting practices.
• Analyzing the challenges and opportunities of sustainability reporting.
• The impact of earnings management on financial statement reliability.

Financial Regulation and Policy Research Topics

• The role of government intervention in preventing financial crises.
• Evaluating the impact of Dodd-Frank Wall Street Reform and Consumer Protection Act.
• Analyzing the effectiveness of Basel III in regulating global banking.
• The role of regulatory bodies in promoting financial market integrity.
• Investigating the impact of tax policies on corporate financial decisions.
• Analyzing the challenges and opportunities of cross-border financial regulation.
• The role of ethics in financial decision-making and regulation.
• Evaluating the impact of monetary policy on inflation and economic growth.
• The influence of political factors on financial regulation.
• The impact of regulatory changes on financial innovation.

Real Estate Finance Related Research Topics

• Analyzing the factors influencing real estate prices and investment.
• The impact of interest rate changes on real estate markets.
• Evaluating the role of mortgage-backed securities in real estate finance.
• The influence of housing policies on real estate market dynamics.
• The role of real estate crowdfunding in property financing.
• Analyzing the impact of urbanization on real estate development.
• The role of sustainability in real estate investment decisions.
• Evaluating the impact of economic downturns on real estate values.
• The influence of demographic trends on real estate market dynamics.
• Analyzing the challenges and opportunities of real estate finance in emerging markets.

Behavioral Finance Research Paper Topics

• Investigating the role of behavioral biases in investment decisions.
• The impact of overconfidence on financial decision-making.
• Analyzing the influence of social networks on investment behavior.
• Evaluating the role of emotions in financial decision-making.
• The impact of financial news and media on investor sentiment.
• Investigating the role of heuristics in shaping financial perceptions.
• Analyzing the impact of market bubbles on investor behavior.
• The influence of framing effects on investment choices.
• Evaluating the role of financial education in mitigating behavioral biases.
• The impact of cultural factors on individual investment decisions.

Financial Technology (Fintech) Research Topics

• Analyzing the impact of robo-advisors on traditional investment advisory services.
• The role of blockchain in reshaping payment systems.
• Evaluating the potential of cryptocurrencies as a mainstream means of exchange.
• The impact of artificial intelligence on credit scoring models.
• Analyzing the challenges and opportunities of regulating fintech startups.
• The role of big data analytics in personalized financial services.
• Evaluating the impact of open banking on financial innovation.
• The influence of cybersecurity threats on fintech adoption.
• Analyzing the role of regulatory sandboxes in fostering fintech innovation.
• The impact of fintech on financial inclusion in developing economies.

Economics and Finance Sector Related Research Topics

• Investigating the relationship between economic indicators and financial markets.
• The impact of trade policies on exchange rates and international finance.
• Analyzing the role of economic sanctions in shaping financial landscapes.
• Evaluating the impact of globalization on financial stability.
• The role of monetary policy in addressing economic inequality.
• Analyzing the impact of economic recessions on financial decision-making.
• The influence of political instability on financial markets.
• The impact of demographic trends on economic and financial dynamics.
• Evaluating the role of economic forecasting in financial decision-making.
• The relationship between economic growth and financial development.

Sustainable Banking And Finance Research Topics

• Analyzing the impact of environmental, social, and governance (ESG) factors on investment decisions.
• The role of green finance in promoting sustainable development.
• Evaluating the impact of carbon pricing on financial markets.
• The influence of sustainable investing on corporate decision-making.
• Analyzing the challenges and opportunities of integrating sustainability into financial reporting.
• The role of impact investing in addressing social and environmental issues.
• Evaluating the impact of climate change on financial risk assessment.
• The influence of corporate sustainability on shareholder value.
• The role of green bonds in financing environmentally friendly projects.
• Analyzing the effectiveness of sustainable finance policies in achieving global goals.

Recent Banking And Finance Research Topics

• Investigating the potential of decentralized finance (DeFi) in traditional banking services.
• The impact of quantum computing on financial modeling and risk management.
• Analyzing the challenges and opportunities of central bank digital currencies (CBDCs).
• The role of augmented reality (AR) and virtual reality (VR) in financial services.
• The impact of 5G technology on financial transactions and services.
• Evaluating the potential of tokenization in transforming financial markets.
• Analyzing the role of artificial intelligence in credit scoring and lending decisions.
• The influence of geopolitical factors on global financial markets.
• The impact of regulatory technology (RegTech) in compliance and risk management.
• The role of smart contracts in streamlining financial transactions.

Cross-Border Finance Research Paper Topics

• Investigating the impact of exchange rate fluctuations on cross-border investments.
• The role of currency unions in promoting cross-border trade and investments.
• Analyzing the challenges and opportunities of cross-border banking operations.
• Evaluating the impact of trade agreements on cross-border financial flows.
• The influence of political and economic integration on cross-border finance.
• Analyzing the role of international financial institutions in cross-border finance.
• The impact of capital controls on cross-border investments.
• The role of cross-border financial services in promoting global economic integration.
• Evaluating the impact of cross-border financial regulations on multinational corporations.
• The influence of cross-border financial crimes on international cooperation.

Financial Education and Literacy Research Topics

• Investigating the impact of financial education programs on students’ financial literacy.
• The role of technology in enhancing financial education and literacy.
• Evaluating the effectiveness of workplace financial wellness programs.
• Analyzing the impact of cultural factors on financial literacy levels.
• The influence of family background on financial literacy.
• The impact of early financial education on long-term financial behavior.
• Analyzing the relationship between financial literacy and retirement planning.
• The role of schools and universities in promoting financial literacy.
• The influence of gender on financial literacy and decision-making.
• Evaluating the impact of online resources on improving financial literacy.

Banking and Finance in Developing Economies

• Analyzing the challenges and opportunities of financial inclusion in developing economies.
• The role of microfinance in poverty alleviation and economic development.
• Evaluating the impact of foreign aid on financial stability in developing countries.
• The influence of corruption on financial development in developing economies.
• Analyzing the role of remittances in shaping economic landscapes in developing countries.
• The impact of informal financial services on rural communities.
• Evaluating the role of government policies in promoting financial development.
• The influence of economic and political instability on financial systems in developing countries.
• The role of international financial institutions in supporting economic growth in developing economies.
• Analyzing the impact of technology adoption on financial inclusion in developing regions.

What Are Some Good Topics In The Area Of Finance And Accounting For A Ph.D. Research?

Here are some current Banking And Finance research topics for students:

Recent Project Topics On Banking And Finance PDF

Here are the most recent project topics on banking and finance pdf:

These Are the best Banking and Finance Research Topics. These topics serve as gateways to understanding the nature of banking, finance, and other research topics. As you find a good research topic, consider your interests and the current trends shaping the financial domain. Whether it’s the impact of technology on banking, the dynamics of stock markets, or the role of sustainable finance.

Engage with your coursework, delve into academic journals, and attend seminars to find the latest understandings and potential research questions. Consulting with professors and advisors offers valuable guidance, helping refine your focus. Keep an eye on industry reports and financial news for inspiration, considering contemporary challenges and emerging trends.

Remember, your research can contribute to understanding financial systems and inform real-world practices. Choose a topic that not only captivates your interest but also addresses relevant issues, and you’ll find yourself good banking and finance research topics. Happy exploring!

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Banking and Finance Dissertation Topics – Selected for Business Students

Published by Owen Ingram at January 2nd, 2023 , Revised On August 16, 2023

Looking for an interesting banking and finance research idea for your dissertation? Your search for the best finance and banking dissertation topics ends right here because, a t ResearchProspect, we help students choose the most authentic and relevant topic for their dissertation projects.

Bank taxes, financial management, financial trading, credit management, market analysis for private investors, economic research methods, the economics of money and banking, international trade and multinational business, the wellbeing of people and society, principles and practices of banking, management and cost accounting, governance and ethics in banking, investment banking, introductory econometrics, and capital investment management are among the many topics covered in banking and finance.

Without further ado, here is our selection of the besting banking and finance thesis topics and ideas.

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The following dissertation topics for banking will assist students in achieving the highest possible grades in their dissertation on banking finance:

List of Banking and Finance Dissertation Topics

• A Comprehensive Analysis of the Economic Crisis as It Relates to Banking and Finance
• A Critical Review of Standard Deviation in Business
• The Political and Economic Risks Involving National Bank Transactions
• A Study of Corporate Developments in European Countries Regarding Banking and Finance
• Security Measures Implemented in Financial Institutions Around the World
• Banking and Finance Approaches from Around the World
• An in-depth study of the World Trade Organization’s role in banking and finance
• A Study of the Relationship Between Corporate Strategy and Capital Structures
• Contrasting global, multinational banks with regional businesses
• Preventing Repetitive Economic Collapse in National and Global Finances
• The Motivations for Becoming International Expats All Over the World
• The Difference Between Islamic Banking and Other Religious Denominations in Banking and Financial Habits
• How Can Small-Scale Industries Survive the Global Banking Demands?
• A Study of the Economic Crisis’s Impact on Banking and Finance
• The Impact of the International Stock Exchange on Domestic Bank Transactions
• A 2025 Projected Report on World Trade and Banking Statistics
• How Can We Address the Issue of the Government’s Financial Deficit in Banking?
• A Comparison of Contemporary and Classic Business Models and Companies’ Banking and Financial Habits
• Which of the following should be the principal area of money investment that has arrived at the bank in the form of deposits?
• How to strike a balance between investing money in various plans to generate a profit and managing depositor trust
• What are banks’ responsibilities to their depositors, and how may such liabilities be managed without jeopardising depositor trust?
• How the new banking financing laws enacted by governments throughout the world are better protecting depositors’ rights?
• What is the terminology related to banking finance, which oversees the investment of deposited funds as well as the banks’ responsibilities to depositors?
• Explain the most recent developments in research related to the topic of banking finance
• How research in the banking finance industry assists governments and banking authorities in properly managing their finances?
• What is the most recent credit rating software that assists in determining the rewards and dangers of investing bank funds in the stock market? 
• How banking finance assists the world’s top banks in managing consumer expectations and profit?
• The negative impact of a manager’s poor management of a bank’s banking financing
• Is it feasible to conduct a banking firm without the assistance of banking finance management?
• What are the most significant aspects of banking financing that allow businesses to develop without constraints?

The importance of banking finance cannot be overstated. These are only a few of the most extensive subjects on which you may write a banking and finance dissertation. Remember that if you want to succeed in your studies, you must be able to offer reliable numbers and facts on the history and current state of banking and finance throughout the world. Otherwise, you will very certainly be unable to justify your study effectively. We hope you can take some inspiration and ideas from the above banking and finance dissertation topics .

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How to find banking and finance dissertation topics.

To find banking and finance dissertation topics:

• Follow industry news and trends.
• Study regulatory changes.
• Explore emerging technologies.
• Analyze financial markets.
• Investigate risk management.
• Consider ethical and global aspects.

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Banking and Finance Dissertation Topics (28 Examples) For Research

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Are you searching for banking and finance dissertation topics? We understand that selecting a dissertation topic is one of the biggest challenges. So, we offer a wide range of banking and finance dissertation topics and project topics on banking and finance. You can also visit our site for corporate finance dissertation topics and other business […]

Are you searching for banking and finance dissertation topics? We understand that selecting a dissertation topic is one of the biggest challenges. So, we offer a wide range of project topics on banking and finance.

Our team of writers can provide quality work on your selected banking and finance research topics. Once you select from the research topics on banking and finance, we will provide an outline, which can provide guidance on how the study should be carried out .

If you have come to this post after searching for corporate finance or finance topics, following are the seperate posts made on these topics.

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Banking and finance dissertation topics

Role of micro-loans in the modern financial industry.

Online currencies like Bitcoin brought changes in the concept of fiat currencies.

Identifying the forces causing American retail banking centres to change.

Analysing the treatment of off-balance sheet activities.

Examining the role of internet banking in society.

Evaluating how the modern economy prevents a run on the banks from happening.

To find out whether the technology can replace the role of retail banking centre.

Relationship between housing loans and the 2008 recession.

Impact of foreign direct investment on the emerging economies.

Identifying the best capital structure for a retail bank.

To study the effect of mergers and acquisition on employee’s morale and performance in the case of banks.

Evaluating the credit management and issues of bad debts in commercial banks in the UAE.

To what extent the electronic banking has affected customer satisfaction.

Portfolio management and its impact on the profitability level of banks.

Impact of interest rate on loan repayment in microfinance banks.

An appraisal of operational problems facing micro-finance banks in delta state.

Studying the impact of risk management on the profitability of banks.

Evaluation of bank lending and credit management.

Role of automated teller machine on customer satisfaction and retention.

Examining the impact of bank consolidation on operational efficiency.

Competitive strategies and changes in the banking industry.

Development of rural banking in the case of developed countries.

The effect of electronic payment systems on the behaviour and satisfaction level of customers.

How does the organisational structure affect the commercial banks and their performance?

How can banks use ratio analysis as a bank lending tool?

Evaluating the relationship between e-banking and cybercrime.

Studying the importance of credit management in the banking industry.

Problems related to loan granting and recovery.

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• Published: 18 June 2021

Financial technology and the future of banking

• Daniel Broby   ORCID: orcid.org/0000-0001-5482-0766 1  

Financial Innovation volume  7 , Article number:  47 ( 2021 ) Cite this article

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This paper presents an analytical framework that describes the business model of banks. It draws on the classical theory of banking and the literature on digital transformation. It provides an explanation for existing trends and, by extending the theory of the banking firm, it illustrates how financial intermediation will be impacted by innovative financial technology applications. It further reviews the options that established banks will have to consider in order to mitigate the threat to their profitability. Deposit taking and lending are considered in the context of the challenge made from shadow banking and the all-digital banks. The paper contributes to an understanding of the future of banking, providing a framework for scholarly empirical investigation. In the discussion, four possible strategies are proposed for market participants, (1) customer retention, (2) customer acquisition, (3) banking as a service and (4) social media payment platforms. It is concluded that, in an increasingly digital world, trust will remain at the core of banking. That said, liquidity transformation will still have an important role to play. The nature of banking and financial services, however, will change dramatically.

Introduction

The bank of the future will have several different manifestations. This paper extends theory to explain the impact of financial technology and the Internet on the nature of banking. It provides an analytical framework for academic investigation, highlighting the trends that are shaping scholarly research into these dynamics. To do this, it re-examines the nature of financial intermediation and transactions. It explains how digital banking will be structurally, as well as physically, different from the banks described in the literature to date. It does this by extending the contribution of Klein ( 1971 ), on the theory of the banking firm. It presents suggested strategies for incumbent, and challenger banks, and how banking as a service and social media payment will reshape the competitive landscape.

The banking industry has been evolving since Banca Monte dei Paschi di Siena opened its doors in 1472. Its leveraged business model has proved very scalable over time, but it is now facing new challenges. Firstly, its book to capital ratios, as documented by Berger et al ( 1995 ), have been consistently falling since 1840. This trend continues as competition has increased. In the past decade, the industry has experienced declines in profitability as measured by return on tangible equity. This is partly the result of falling leverage and fee income and partly due to the net interest margin (connected to traditional lending activity). These trends accelerated following the 2008 financial crisis. At the same time, technology has made banks more competitive. Advances in digital technology are changing the very nature of banking. Banks are now distributing services via mobile technology. A prolonged period of very low interest rates is also having an impact. To sustain their profitability, Brei et al. ( 2020 ) note that many banks have increased their emphasis on fee-generating services.

As Fama ( 1980 ) explains, a bank is an intermediary. The Internet is, however, changing the way financial service providers conduct their role. It is fundamentally changing the nature of the banking. This in turn is changing the nature of banking services, and the way those services are delivered. As a consequence, in order to compete in the changing digital landscape, banks have to adapt. The banks of the future, both incumbents and challengers, need to address liquidity transformation, data, trust, competition, and the digitalization of financial services. Against this backdrop, incumbent banks are focused on reinventing themselves. The challenger banks are, however, starting with a blank canvas. The research questions that these dynamics pose need to be investigated within the context of the theory of banking, hence the need to revise the existing analytical framework.

Banks perform payment and transfer functions for an economy. The Internet can now facilitate and even perform these functions. It is changing the way that transactions are recorded on ledgers and is facilitating both public and private digital currencies. In the past, banks operated in a world of information asymmetry between themselves and their borrowers (clients), but this is changing. This differential gave one bank an advantage over another due to its knowledge about its clients. The digital transformation that financial technology brings reduces this advantage, as this information can be digitally analyzed.

Even the nature of deposits is being transformed. Banks in the future will have to accept deposits and process transactions made in digital form, either Central Bank Digital Currencies (CBDC) or cryptocurrencies. This presents a number of issues: (1) it changes the way financial services will be delivered, (2) it requires a discussion on resilience, security and competition in payments, (3) it provides a building block for better cross border money transfers and (4) it raises the question of private and public issuance of money. Braggion et al ( 2018 ) consider whether these represent a threat to financial stability.

The academic study of banking began with Edgeworth ( 1888 ). He postulated that it is based on probability. In this respect, the nature of the business model depends on the probability that a bank will not be called upon to meet all its liabilities at the same time. This allows banks to lend more than they have in deposits. Because of the resultant mismatch between long term assets and short-term liabilities, a bank’s capital structure is very sensitive to liquidity trade-offs. This is explained by Diamond and Rajan ( 2000 ). They explain that this makes a bank a’relationship lender’. In effect, they suggest a bank is an intermediary that has borrowed from other investors.

Diamond and Rajan ( 2000 ) argue a lender can negotiate repayment obligations and that a bank benefits from its knowledge of the customer. As shall be shown, the new generation of digital challenger banks do not have the same tradeoffs or knowledge of the customer. They operate more like a broker providing a platform for banking services. This suggests that there will be more than one type of bank in the future and several different payment protocols. It also suggests that banks will have to data mine customer information to improve their understanding of a client’s financial needs.

The key focus of Diamond and Rajan ( 2000 ), however, was to position a traditional bank is an intermediary. Gurley and Shaw ( 1956 ) describe how the customer relationship means a bank can borrow funds by way of deposits (liabilities) and subsequently use them to lend or invest (assets). In facilitating this mediation, they provide a service whereby they store money and provide a mechanism to transmit money. With improvements in financial technology, however, money can be stored digitally, lenders and investors can source funds directly over the internet, and money transfer can be done digitally.

A review of financial technology and banking literature is provided by Thakor ( 2020 ). He highlights that financial service companies are now being provided by non-deposit taking contenders. This paper addresses one of the four research questions raised by his review, namely how theories of financial intermediation can be modified to accommodate banks, shadow banks, and non-intermediated solutions.

To be a bank, an entity must be authorized to accept retail deposits. A challenger bank is, therefore, still a bank in the traditional sense. It does not, however, have the costs of a branch network. A peer-to-peer lender, meanwhile, does not have a deposit base and therefore acts more like a broker. This leads to the issue that this paper addresses, namely how the banks of the future will conduct their intermediation.

In order to understand what the bank of the future will look like, it is necessary to understand the nature of the aforementioned intermediation, and the way it is changing. In this respect, there are two key types of intermediation. These are (1) quantitative asset transformation and, (2) brokerage. The latter is a common model adopted by challenger banks. Figure  1 depicts how these two types of financial intermediation match savers with borrowers. To avoid nuanced distinction between these two types of intermediation, it is common to classify banks by the services they perform. These can be grouped as either private, investment, or commercial banking. The service sub-groupings include payments, settlements, fund management, trading, treasury management, brokerage, and other agency services.

How banks act as intermediaries between lenders and borrowers. This function call also be conducted by intermediaries as brokers, for example by shadow banks. Disintermediation occurs over the internet where peer-to-peer lenders match savers to lenders

Financial technology has the ability to disintermediate the banking sector. The competitive pressures this results in will shape the banks of the future. The channels that will facilitate this are shown in Fig.  2 , namely the Internet and/or mobile devices. Challengers can participate in this by, (1) directly matching borrows with savers over the Internet and, (2) distributing white labels products. The later enables banking as a service and avoids the aforementioned liquidity mismatch.

The strategic options banks have to match lenders with borrowers. The traditional and challenger banks are in the same space, competing for business. The distributed banks use the traditional and challenger banks to white label banking services. These banks compete with payment platforms on social media. The Internet heralds an era of banking as a service

There are also physical changes that are being made in the delivery of services. Bricks and mortar branches are in decline. Mobile banking, or m-banking as Liu et al ( 2020 ) describe it, is an increasingly important distribution channel. Robotics are increasingly being used to automate customer interaction. As explained by Vishnu et al ( 2017 ), these improve efficiency and the quality of execution. They allow for increased oversight and can be built on legacy systems as well as from a blank canvas. Application programming interfaces (APIs) are bringing the same type of functionality to m-banking. They can be used to authorize third party use of banking data. How banks evolve over time is important because, according to the OECD, the activity in the financial sector represents between 20 and 30 percent of developed countries Gross Domestic Product.

In summary, financial technology has evolved to a level where online banks and banking as a service are challenging incumbents and the nature of banking mediation. Banking is rapidly transforming because of changes in such technology. At the same time, the solving of the double spending problem, whereby digital money can be cryptographically protected, has led to the possibility that paper money will become redundant at some point in the future. A theoretical framework is required to understand this evolving landscape. This is discussed next.

The theory of the banking firm: a revision

In financial theory, as eloquently explained by Fama ( 1980 ), banking provides an accounting system for transactions and a portfolio system for the storage of assets. That will not change for the banks of the future. Fama ( 1980 ) explains that their activities, in an unregulated state, fulfil the Modigliani–Miller ( 1959 ) theorem of the irrelevance of the financing decision. In practice, traditional banks compete for deposits through the interest rate they offer. This makes the transactional element dependent on the resulting debits and credits that they process, essentially making banks into bookkeeping entities fulfilling the intermediation function. Since this is done in response to competitive forces, the general equilibrium is a passive one. As such, the banking business model is vulnerable to disruption, particularly by innovation in financial technology.

A bank is an idiosyncratic corporate entity due to its ability to generate credit by leveraging its balance sheet. That balance sheet has assets on one side and liabilities on the other, like any corporate entity. The assets consist of cash, lending, financial and fixed assets. On the other side of the balance sheet are its liabilities, deposits, and debt. In this respect, a bank’s equity and its liabilities are its source of funds, and its assets are its use of funds. This is explained by Klein ( 1971 ), who notes that a bank’s equity W , borrowed funds and its deposits B is equal to its total funds F . This is the same for incumbents and challengers. This can be depicted algebraically if we let incumbents be represented by Φ and challengers represented by Γ:

Klein ( 1971 ) further explains that a bank’s equity is therefore made up of its share capital and unimpaired reserves. The latter are held by a bank to protect the bank’s deposit clients. This part is also mandated by regulation, so as to protect customers and indeed the entire banking system from systemic failure. These protective measures include other prudential requirements to hold cash reserves or other liquid assets. As shall be shown, banking services can be performed over the Internet without these protections. Banking as a service, as this phenomenon known, is expected to increase in the future. This will change the nature of the protection available to clients. It will change the way banks transform assets, explained next.

A bank’s deposits are said to be a function of the proportion of total funds obtained through the issuance of the ith deposit type and its total funds F , represented by α i . Where deposits, represented by Bs , are made in the form of Bs (i  =  1 *s n) , they generate a rate of interest. It follows that Si Bs  =  B . As such,

Therefor it can be said that,

The importance of Eq. 3 is that the balance sheet can be leveraged by the issuance of loans. It should be noted, however, that not all loans are returned to the bank in whole or part. Non-performing loans reduce the asset side of a bank’s balance sheet and act as a constraint on capital, and therefore new lending. Clearly, this is not the case with banking as a service. In that model, loans are brokered. That said, with the traditional model, an advantage of financial technology is that it facilitates the data mining of clients’ accounts. Lending can therefore be more targeted to borrowers that are more likely to repay, thereby reducing non-performing loans. Pari passu, the incumbent bank of the future will therefore have a higher risk-adjusted return on capital. In practice, however, banking as a service will bring greater competition from challengers and possible further erosion of margins. Alternatively, some banks will proactively engage in partnerships and acquisitions to maintain their customer base and address the competition.

A bank must have reserves to meet the demand of customers demanding their deposits back. The amount of these reserves is a key function of banking regulation. The Basel Committee on Banking Supervision mandates a requirement to hold various tiers of capital, so that banks have sufficient reserves to protect depositors. The Committee also imposes a framework for mitigating excessive liquidity risk and maturity transformation, through a set Liquidity Coverage Ratio and Net Stable Funding Ratio.

Recent revisions of theory, because of financial technology advances, have altered our understanding of banking intermediation. This will impact the competitive landscape and therefor shape the nature of the bank of the future. In this respect, the threat to incumbent banks comes from peer-to-peer Internet lending platforms. These perform the brokerage function of financial intermediation without the use of the aforementioned banking balance sheet. Unlike regulated deposit takers, such lending platforms do not create assets and do not perform risk and asset transformation. That said, they are reliant on investors who do not always behave in a counter cyclical way.

Financial technology in banking is not new. It has been used to facilitate electronic markets since the 1980’s. Thakor ( 2020 ) refers to three waves of application of financial innovation in banking. The advent of institutional futures markets and the changing nature of financial contracts fundamentally changed the role of banks. In response to this, academics extended the concept of a bank into an entity that either fulfills the aforementioned functions of a broker or a qualitative asset transformer. In this respect, they connect the providers and users of capital without changing the nature of the transformation of the various claims to that capital. This transformation can be in the form risk transfer or the application of leverage. The nature of trading of financial assets, however, is changing. Price discovery can now be done over the Internet and that is moving liquidity from central marketplaces (like the stock exchange) to decentralized ones.

Alongside these trends, in considering what the bank of the future will look like, it is necessary to understand the unregulated lending market that competes with traditional banks. In this part of the lending market, there has been a rise in shadow banks. The literature on these entities is covered by Adrian and Ashcraft ( 2016 ). Shadow banks have taken substantial market share from the traditional banks. They fulfil the brokerage function of banks, but regulators have only partial oversight of their risk transformation or leverage. The rise of shadow banks has been facilitated by financial technology and the originate to distribute model documented by Bord and Santos ( 2012 ). They use alternative trading systems that function as electronic communication networks. These facilitate dark pools of liquidity whereby buyers and sellers of bonds and securities trade off-exchange. Since the credit crisis of 2008, total broker dealer assets have diverged from banking assets. This illustrates the changed lending environment.

In the disintermediated market, banking as a service providers must rely on their equity and what access to funding they can attract from their online network. Without this they are unable to drive lending growth. To explain this, let I represent the online network. Extending Klein ( 1971 ), further let Ψ represent banking as a service and their total funds by F . This state is depicted as,

Theoretically, it can be shown that,

Shadow banks, and those disintermediators who bypass the banking system, have an advantage in a world where technology is ubiquitous. This becomes more apparent when costs are considered. Buchak et al. ( 2018 ) point out that shadow banks finance their originations almost entirely through securitization and what they term the originate to distribute business model. Diversifying risk in this way is good for individual banks, as banking risks can be transferred away from traditional banking balance sheets to institutional balance sheets. That said, the rise of securitization has introduced systemic risk into the banking sector.

Thus, we can see that the nature of banking capital is changing and at the same time technology is replacing labor. Let A denote the number of transactions per account at a period in time, and C denote the total cost per account per time period of providing the services of the payment mechanism. Klein ( 1971 ) points out that, if capital and labor are assumed to be part of the traditional banking model, it can be observed that,

It can therefore be observed that the total service charge per account at a period in time, represented by S, has a linear and proportional relationship to bank account activity. This is another variable that financial technology can impact. According to Klein ( 1971 ) this can be summed up in the following way,

where d is the basic bank decision variable, the service charge per transaction. Once again, in an automated and digital environment, financial technology greatly reduces d for the challenger banks. Swankie and Broby ( 2019 ) examine the impact of Artificial Intelligence on the evaluation of banking risk and conclude that it improves such variables.

Meanwhile, the traditional banking model can be expressed as a product of the number of accounts, M , and the average size of an account, N . This suggests a banks implicit yield is it rate of interest on deposits adjusted by its operating loss in each time period. This yield is generated by payment and loan services. Let R 1 depict this. These can be expressed as a fraction of total demand deposits. This is depicted by Klein ( 1971 ), if one assumes activity per account is constant, as,

As a result, whether a bank is structured with traditional labor overheads or built digitally, is extremely relevant to its profitability. The capital and labor of tradition banks, depicted as Φ i , is greater than online networks, depicted as I i . As such, the later have an advantage. This can be shown as,

What Klein (1972) failed to highlight is that the banking inherently involves leverage. Diamond and Dybving (1983) show that leverage makes bank susceptible to run on their liquidity. The literature divides these between adverse shock events, as explained by Bernanke et al ( 1996 ) or moral hazard events as explained by Demirgu¨¸c-Kunt and Detragiache ( 2002 ). This leverage builds on the balance sheet mismatch of short-term assets with long term liabilities. As such, capital and liquidity are intrinsically linked to viability and solvency.

The way capital and liquidity are managed is through credit and default management. This is done at a bank level and a supervisory level. The Basel Committee on Banking Supervision applies capital and leverage ratios, and central banks manage interest rates and other counter-cyclical measures. The various iterations of the prudential regulation of banks have moved the microeconomic theory of banking from the modeling of risk to the modeling of imperfect information. As mentioned, shadow and disintermediated services do not fall under this form or prudential regulation.

The relationship between leverage and insolvency risk crucially depends on the degree of banks total funds F and their liability structure L . In this respect, the liability structure of traditional banks is also greater than online networks which do not have the same level of available funds, depicted as,

Diamond and Dybvig ( 1983 ) observe that this liability structure is intimately tied to a traditional bank’s assets. In this respect, a bank’s ability to finance its lending at low cost and its ability to achieve repayment are key to its avoidance of insolvency. Online networks and/or brokers do not have to finance their lending, simply source it. Similarly, as brokers they do not face capital loss in the event of a default. This disintermediates the bank through the use of a peer-to-peer environment. These lenders and borrowers are introduced in digital way over the internet. Regulators have taken notice and the digital broker advantage might not last forever. As a result, the future may well see greater cooperation between these competing parties. This also because banks have valuable operational experience compared to new entrants.

It should also be observed that bank lending is either secured or unsecured. Interest on an unsecured loan is typically higher than the interest on a secured loan. In this respect, incumbent banks have an advantage as their closeness to the customer allows them to better understand the security of the assets. Berger et al ( 2005 ) further differentiate lending into transaction lending, relationship lending and credit scoring.

The evolution of the business model in a digital world

As has been demonstrated, the bank of the future in its various manifestations will be a consequence of the evolution of the current banking business model. There has been considerable scholarly investigation into the uniqueness of this business model, but less so on its changing nature. Song and Thakor ( 2010 ) are helpful in this respect and suggest that there are three aspects to this evolution, namely competition, complementary and co-evolution. Although liquidity transformation is evolving, it remains central to a bank’s role.

All the dynamics mentioned are relevant to the economy. There is considerable evidence, as outlined by Levine ( 2001 ), that market liberalization has a causal impact on economic growth. The impact of technology on productivity should prove positive and enhance the functioning of the domestic financial system. Indeed, market liberalization has already reshaped banking by increasing competition. New fee based ancillary financial services have become widespread, as has the proprietorial use of balance sheets. Risk has been securitized and even packaged into trade-able products.

Challenger banks are developing in a complementary way with the incumbents. The latter have an advantage over new entrants because they have information on their customers. The liquidity insurance model, proposed by Diamond and Dybvig ( 1983 ), explains how such banks have informational advantages over exchange markets. That said, financial technology changes these dynamics. It if facilitating the processing of financial data by third parties, explained in greater detail in the section on Open Banking.

At the same time, financial technology is facilitating banking as a service. This is where financial services are delivered by a broker over the Internet without resort to the balance sheet. This includes roboadvisory asset management, peer to peer lending, and crowd funding. Its growth will be facilitated by Open Banking as it becomes more geographically adopted. Figure  3 illustrates how these business models are disintermediating the traditional banking role and matching burrowers and savers.

The traditional view of banks ecosystem between savers and borrowers, atop the Internet which is matching savers and borrowers directly in a peer-to-peer way. The Klein ( 1971 ) theory of the banking firm does not incorporate the mirrored dynamics, and as such needs to be extended to reflect the digital innovation that impacts both borrowers and severs in a peer-to-peer environment

Meanwhile, the banking sector is co-evolving alongside a shadow banking phenomenon. Lenders and borrowers are interacting, but outside of the banking sector. This is a concern for central banks and banking regulators, as the lending is taking place in an unregulated environment. Shadow banking has grown because of financial technology, market liberalization and excess liquidity in the asset management ecosystem. Pozsar and Singh ( 2011 ) detail the non-bank/bank intersection of shadow banking. They point out that shadow banking results in reverse maturity transformation. Incumbent banks have blurred the distinction between their use of traditional (M2) liabilities and market-based shadow banking (non-M2) liabilities. This impacts the inter-generational transfers that enable a bank to achieve interest rate smoothing.

Securitization has transformed the risk in the banking sector, transferring it to asset management institutions. These include structured investment vehicles, securities lenders, asset backed commercial paper investors, credit focused hedge and money market funds. This in turn has led to greater systemic risk, the result of the nature of the non-traded liabilities of securitized pooling arrangements. This increased risk manifested itself in the 2008 credit crisis.

Commercial pressures are also shaping the banking industry. The drive for cost efficiency has made incumbent banks address their personally costs. Bank branches have been closed as technology has evolved. Branches make it easier to withdraw or transfer deposits and challenger banks are not as easily able to attract new deposits. The banking sector is therefore looking for new point of customer contact, such as supermarkets, post offices and social media platforms. These structural issues are occurring at the same time as the retail high street is also evolving. Banks have had an aggressive roll out of automated telling machines and a reduction in branches and headcount. Online digital transactions have now become the norm in most developed countries.

The financing of banks is also evolving. Traditional banks have tended to fund illiquid assets with short term and unstable liquid liabilities. This is one of the key contributors to the rise to the credit crisis of 2008. The provision of liquidity as a last resort is central to the asset transformation process. In this respect, the banking sector experienced a shock in 2008 in what is termed the credit crisis. The aforementioned liquidity mismatch resulted in the system not being able to absorb all the risks associated with subprime lending. Central banks had to resort to quantitative easing as a result of the failure of overnight funding mechanisms. The image of the entire banking sector was tarnished, and the banks of the future will have to address this.

The future must learn from the mistakes of the past. The structural weakness of the banking business model cannot be solved. That said, the latest Basel rules introduce further risk mitigation, improved leverage ratios and increased levels of capital reserve. Another lesson of the credit crisis was that there should be greater emphasis on risk culture, governance, and oversight. The independence and performance of the board, the experience and the skill set of senior management are now a greater focus of regulators. Internal controls and data analysis are increasingly more robust and efficient, with a greater focus on a banks stable funding ratio.

Meanwhile, the very nature of money is changing. A digital wallet for crypto-currencies fulfills much the same storage and transmission functions of a bank; and crypto-currencies are increasing being used for payment. Meanwhile, in Sweden, stores have the right to refuse cash and the majority of transactions are card based. This move to credit and debit cards, and the solving of the double spending problem, whereby digital money can be crypto-graphically protected, has led to the possibility that paper money could be replaced at some point in the future. Whether this might be by replacement by a CBDC, or decentralized digital offering, is of secondary importance to the requirement of banks to adapt. Whether accommodating crytpo-currencies or CBDC’s, Kou et al. ( 2021 ) recommend that banks keep focused on alternative payment and money transferring technologies.

Central banks also have to adapt. To limit disintermediation, they have to ensure that the economic design of their sponsored digital currencies focus on access for banks, interest payment relative to bank policy rate, banking holding limits and convertibility with bank deposits. All these developments have implications for banks, particularly in respect of funding, the secure storage of deposits and how digital currency interacts with traditional fiat money.

Open banking

Against the backdrop of all these trends and changes, a new dynamic is shaping the future of the banking sector. This is termed Open Banking, already briefly mentioned. This new way of handling banking data protocols introduces a secure way to give financial service companies consensual access to a bank’s customer financial information. Figure  4 illustrates how this works. Although a fairly simple concept, the implications are important for the banking industry. Essentially, a bank customer gives a regulated API permission to securely access his/her banking website. That is then used by a banking as a service entity to make direct payments and/or download financial data in order to provide a solution. It heralds an era of customer centric banking.

How Open Banking operates. The customer generates data by using his bank account. A third party provider is authorized to access that data through an API request. The bank confirms digitally that the customer has authorized the exchange of data and then fulfills the request

Open Banking was a response to the documented inertia around individual’s willingness to change bank accounts. Following the Retail Banking Review in the UK, this was addressed by lawmakers through the European Union’s Payment Services Directive II. The legislation was designed to make it easier to change banks by allowing customers to delegate authority to transfer their financial data to other parties. As a result of this, a whole host of data centric applications were conceived. Open banking adds further momentum to reshaping the future of banking.

Open Banking has a number of quite revolutionary implications. It was started so customers could change banks easily, but it resulted in some secondary considerations which are going to change the future of banking itself. It gives a clear view of bank financing. It allows aggregation of finances in one place. It also allows can give access to attractive offerings by allowing price comparisons. Open Banking API’s build a secure online financial marketplace based on data. They also allow access to a larger market in a faster way but the third-party providers for the new entrants. Open Banking allows developers to build single solutions on an API addressing very specific problems, like for example, a cash flow based credit rating.

Romānova et al. ( 2018 ) undertook a questionnaire on the Payment Services Directive II. The results suggest that Open Banking will promote competitiveness, innovation, and new product development. The initiative is associated with low costs and customer satisfaction, but that some concerns about security, privacy and risk are present. These can be mitigated, to some extent, by secure protocols and layered permission access.

Discussion: strategic options

Faced with these disruptive trends, there are four strategic options for market participants to con- sider. There are (1) a defensive customer retention strategy for incumbents, (2) an aggressive customer acquisition strategy for challenger banks (3) a banking as a service strategy for new entrants, and (4) a payments strategy for social media platforms.

Each of these strategies has to be conducted in a competitive marketplace for money demand by potential customers. Figure  5 illustrates where the first three strategies lie on the tradeoff between money demand and interest rates. The payment strategy can’t be modeled based on the supply of money. In the figure, the market settles at a rate L 2 . The incumbent banks have the capacity to meet the largest supply of these loans. The challenger banks have a constrained function but due to a lower cost base can gain excess rent through higher rates of interest. The peer-to-peer bank as a service brokers must settle for the market rate and a constrained supply offering.

The money demand M by lenders on the y axis. Interest rates on the y axis are labeled as r I and r II . The challenger banks are represented by the line labeled Γ. They have a price and technology advantage and so can lend at higher interest rates. The brokers are represented by the line labeled Ω. They are price takers, accepting the interest rate determined by the market. The same is true for the incumbents, represented by the line labeled Φ but they have a greater market share due to their customer relationships. Note that payments strategy for social media platforms is not shown on this figure as it is not affected by interest rates

Figure  5 illustrates that having a niche strategy is not counterproductive. Liu et al ( 2020 ) found that banks performing niche activities exhibit higher profitability and have lower risk. The syndication market now means that a bank making a loan does not have to be the entity that services it. This means banks in the future can better shape their risk profile and manage their lending books accordingly.

An interesting question for central banks is what the future Deposit Supply function will look like. If all three forms: open banking, traditional banking and challenger banks develop together, will the bank of the future have the same Deposit Supply function? The Klein ( 1971 ) general formulation assumes that deposits are increasing functions of implicit and explicit yields. As such, the very nature of central bank directed monetary policy may have to be revisited, as alluded to in the earlier discussion on digital money.

The client retention strategy (incumbents)

The competitive pressures suggest that incumbent banks need to focus on customer retention. Reichheld and Kenny ( 1990 ) found that the best way to do this was to focus on the retention of branch deposit customers. Obviously, another way is to provide a unique digital experience that matches the challengers.

Incumbent banks have a competitive advantage based on the information they have about their customers. Allen ( 1990 ) argues that where risk aversion is observable, information markets are viable. In other words, both bank and customer benefit from this. The strategic issue for them, therefore, becomes the retention of these customers when faced with greater competition.

Open Banking changes the dynamics of the banking information advantage. Borgogno and Colangelo ( 2020 ) suggest that the access to account (XS2A) rule that it introduced will increase competition and reduce information asymmetry. XS2A requires banks to grant access to bank account data to authorized third payment service providers.

The incumbent banks have a high-cost base and legacy IT systems. This makes it harder for them to migrate to a digital world. There are, however, also benefits from financial technology for the incumbents. These include reduced cost and greater efficiency. Financial technology can also now support platforms that allow incumbent banks to sell NPL’s. These platforms do not require the ownership of assets, they act as consolidators. The use of technology to monitor the transactions make the processing cost efficient. The unique selling point of such platforms is their centralized point of contact which results in a reduction in information asymmetry.

Incumbent banks must adapt a number of areas they got to adapt in terms of their liquidity transformation. They have to adapt the way they handle data. They must get customers to trust them in a digital world and the way that they trust them in a bricks and mortar world. It is no coincidence. When you go into a bank branch that is a great big solid building great big facade and so forth that is done deliberately so that you trust that bank with your deposit.

The risk of having rising non-performing loans needs to be managed, so customer retention should be selective. One of the puzzles in banking is why customers are regularly denied credit, rather than simply being charged a higher price for it. This credit rationing is often alleviated by collateral, but finance theory suggests value is based on the discounted sum of future cash flows. As such, it is conceivable that the bank of the future will use financial technology to provide innovative credit allocation solutions. That said, the dual risks of moral hazard and information asymmetries from the adoption of such solutions must be addressed.

Customer retention is especially important as bank competition is intensifying, as is the digitalization of financial services. Customer retention requires innovation, and that innovation has been moving at a very fast rate. Until now, banks have traditionally been hesitant about technology. More recently, mergers and acquisitions have increased quite substantially, initiated by a need to address actual or perceived weaknesses in financial technology.

The client acquisition strategy (challengers)

As intermediaries, the challenger banks are the same as incumbent banks, but designed from the outset to be digital. This gives them a cost and efficiency advantage. Anagnostopoulos ( 2018 ) suggests that the difference between challenger and traditional banks is that the former address its customers problems more directly. The challenge for such banks is customer acquisition.

Open Banking is a major advantage to challenger banks as it facilitates the changing of accounts. There is widespread dissatisfaction with many incumbent banks. Open Banking makes it easier to change accounts and also easier to get a transaction history on the client.

Customer acquisition can be improved by building trust in a brand. Historically, a bank was physically built in a very robust manner, hence the heavy architecture and grand banking halls. This was done deliberately to engender a sense of confidence in the deposit taking institution. Pure internet banks are not able to do this. As such, they must employ different strategies to convey stability. To do this, some communicate their sustainability credentials, whilst others use generational values-based advertising. Customer acquisition in a banking context is traditionally done by offering more attractive rates of interest. This is illustrated in Fig.  5 by the intersect of traditional banks with the market rate of interest, depicted where the line Γ crosses L 2 . As a result of the relationship with banking yield, teaser rates and introductory rates are common. A customer acquisition strategy has risks, as consumers with good credit can game different challenger banks by frequently changing accounts.

Most customer acquisition, however, is done based on superior service offering. The functionality of challenger banking accounts is often superior to incumbents, largely because the latter are built on legacy databases that have inter-operability issues. Having an open platform of services is a popular customer acquisition technique. The unrestricted provision of third-party products is viewed more favorably than a restricted range of products.

The banking as a service strategy (new entrants)

Banking from a customer’s perspective is the provision of a service. Customers don’t care about the maturity transformation of banking balance sheets. Banking as a service can be performed without recourse to these balance sheets. Banking products are brokered, mostly by new entrants, to individuals as services that can be subscribed to or paid on a fee basis.

There are a number banking as a service solutions including pre-paid and credit cards, lending and leasing. The banking as a service brokers are effectively those that are aggregating services from others using open banking to enable banking as a service.

The rise of banking as a service needs to be understood as these compete directly with traditional banks. As explained, some of these do this through peer-to-peer lending over the internet, others by matching borrows and sellers, conducting mediation as a loan broker. Such entities do not transform assets and do not have banking licenses. They do not have a branch network and often don not have access to deposits. This means that they have no insurance protection and can be subject to interest rate controls.

The new genre of financial technology, banking as a service provider, conduct financial services transformation without access to central bank liquidity. In a distributed digital asset world, the assets are stored on a distributed ledger rather than a traditional banking ledger. Financial technology has automated credit evaluation, savings, investments, insurance, trading, banking payments and risk management. These banking as a service offering are only as secure as the technology on which they are built.

The social media payment strategy (disintermediators and disruptors)

An intermediation bank is a conceptual idea, one created solely on a social networking site. Social media has developed a market for online goods and services. Williams ( 2018 ) estimates that there are 2.46 billion social media users. These all make and receive payments of some kind. They demand security and functionality. Importantly, they have often more clients than most banks. As such, a strategy to monetize the payments infrastructure makes sense.

All social media platforms are rich repositories of data. Such platforms are used to buy and sell things and that requires payments. Some platforms are considering evolving their own digital payment, cutting out the banks as middlemen. These include Facebook’s Diem (formerly Libra), a digital currency, and similar developments at some of the biggest technology companies. The risk with social media payment platform is that there is systemic counter-party protection. Regulators need to address this. One way to do this would be to extend payment service insurance to such platforms.

Social media as a platform moves the payment relationship from a transaction to a customer experience. The ability to use consumer desires in combination with financial data has the potential to deliver a number of new revenue opportunities. These will compete directly with the banks of the future. This will have implications for (1) the money supply, (2) the market share of traditional banks and, (3) the services that payment providers offer.

Further research

Several recommendations for research derive from both the impact of disintermediation and the four proposed strategies that will shape banking in the future. The recommendations and suggestions are based on the mentioned papers and the conclusions drawn from them.

As discussed, the nature of intermediation is changing, and this has implications for the pricing of risk. The role of interest rates in banking will have to be further reviewed. In a decentralized world based on crypto currencies the central banks do not have the same control over the money supply, This suggest the quantity theory of money and the liquidity preference theory need to be revisited. As explained, the Internet reduces much of the friction costs of intermediation. Researchers should ask how this will impact maturity transformation. It is also fair to ask whether at some point in the future there will just be one big bank. This question has already been addressed in the literature but the Internet facilities the possibility. Diamond ( 1984 ) and Ramakrishnan and Thakor ( 1984 ) suggested the answer was due to diversification and its impact on reducing monitoring costs.

Attention should be given by academics to the changing nature of banking risk. How should regulators, for example, address the moral hazard posed by challenger banks with weak balance sheets? What about deposit insurance? Should it be priced to include unregulated entities? Also, what criteria do borrowers use to choose non-banking intermediaries? The changing risk environment also poses two interesting practical questions. What will an online bank run look like, and how can it be averted? How can you establish trust in digital services?

There are also research questions related to the nature of competition. What, for example, will be the nature of cross border competition in a decentralized world? Is the credit rationing that generates competition a static or dynamic phenomena online? What is the value of combining consumer utility with banking services?

Financial intermediaries, like banks, thrive in a world of deficits and surpluses supported by information asymmetries and disconnectedness. The connectivity of the internet changes this dynamic. In this respect, the view of Schumpeter ( 1911 ) on the role of financial intermediaries needs revisiting. Lenders and borrows can be connected peer to peer via the internet.

All the dynamics mentioned change the nature of moral hazard. This needs further investigation. There has been much scholarly research on the intrinsic riskiness of the mismatch between banking assets and liabilities. This mismatch not only results in potential insolvency for a single bank but potentially for the whole system. There has, for example, been much debate on the whether a bank can be too big to fail. As a result of the riskiness of the banking model, the banks of the future will be just a liable to fail as the banks of the past.

This paper presented a revision of the theory of banking in a digital world. In this respect, it built on the work of Klein ( 1971 ). It provided an overview of the changing nature of banking intermediation, a result of the Internet and new digital business models. It presented the traditional academic view of banking and how it is evolving. It showed how this is adapted to explain digital driven disintermediation.

It was shown that the banking industry is facing several documented challenges. Risk is being taken of balance sheet, securitized, and brokered. Financial technology is digitalizing service delivery. At the same time, the very nature of intermediation is being changed due to digital currency. It is argued that the bank of the future not only has to face these competitive issues, but that technology will enhance the delivery of banking services and reduce the cost of their delivery.

The paper further presented the importance of the Open Banking revolution and how that facilitates banking as a service. Open Banking is increasing client churn and driving banking as a service. That in turn is changing the way products are delivered.

Four strategies were proposed to navigate the evolving competitive landscape. These are for incumbents to address customer retention; for challengers to peruse a low-cost digital experience; for niche players to provide banking as a service; and for social media platforms to develop payment platforms. In all these scenarios, the banks of the future will have to have digital strategies for both payments and service delivery.

It was shown that both incumbents and challengers are dependent on capital availability and borrowers credit concerns. Nothing has changed in that respect. The risks remain credit and default risk. What is clear, however, is the bank has become intrinsically linked with technology. The Internet is changing the nature of mediation. It is allowing peer to peer matching of borrowers and savers. It is facilitating new payment protocols and digital currencies. Banks need to evolve and adapt to accommodate these. Most of these questions are empirical in nature. The aim of this paper, however, was to demonstrate that an understanding of the banking model is a prerequisite to understanding how to address these and how to develop hypotheses connected with them.

In conclusion, financial technology is changing the future of banking and the way banks intermediate. It is facilitating digital money and the online transmission of financial assets. It is making banks more customer enteric and more competitive. Scholarly investigation into banking has to adapt. That said, whatever the future, trust will remain at the core of banking. Similarly, deposits and lending will continue to attract regulatory oversight.

Availability of data and materials

Diagrams are my own and the code to reproduce them is available in the supplied Latex files.

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Finance Dissertation Made Easier!

Embarking on your dissertation adventure? Look no further! Choosing the right finance dissertation topics is like laying the foundation for your research journey in finance, and we're here to light up your path. In this article, we will be diving deep into why dissertation topics in finance matter so much. We've got some golden writing tips to share with you! We're also unveiling the secret recipe for structuring a stellar finance dissertation and exploring intriguing topics across various finance sub-fields. Our buffet of finance dissertation topics will surely set your research spirit on fire!

What is a Finance Dissertation?

Finance dissertations are academic papers that delve into specific finance topics chosen by students, covering areas such as stock markets, banking, risk management, and healthcare finance. These dissertations require extensive research to create a compelling report and contribute to the student's confidence and satisfaction in the field of finance. Now, let's understand why these dissertations are so important and why choosing the right finance dissertation topics is crucial!

Why are Finance Dissertation Topics Important?

Choosing the dissertation topics for finance students is essential as it will influence the course of your research. It determines the direction and scope of your study. You must make sure that the finance dissertation topics you choose are relevant to your field of interest. Here are a few reasons why finance thesis topics are important:

1. Relevance

Opting for relevant finance thesis topics ensures that your research contributes to the existing body of knowledge and addresses contemporary issues in finance. Choosing a dissertation topic relevant to the industry can make a meaningful impact and advance understanding in your chosen area.

2. Personal Interest

Selecting finance dissertation topics that align with your interests and career goals is vital. When genuinely passionate about your research area, you are more likely to stay motivated during the dissertation process. Your interest will drive you to explore the subject thoroughly and produce high-quality work.

3. Future Opportunities

Well-chosen finance dissertation topics can open doors to various future opportunities. They can enhance your employability by showcasing your expertise in a specific finance area. They may also lead to potential research collaborations and invitations to conferences in your field of interest.

4. Academic Supervision

Your choice of topics for dissertation in finance also influences the availability of academic supervisors with expertise in your chosen area. Selecting a well-defined research area increases the likelihood of finding a supervisor to guide you effectively throughout the dissertation. Their knowledge and guidance will greatly contribute to the success of your research.

Writing Tips for Finance Dissertation

Writing a dissertation requires a lot of planning, formatting, and structuring. It starts with deciding on topics for a dissertation in finance, conducting tons of research, deciding on methods, and so on. Below are some tips to assist you along the way, and here is a blog on the 10 tips on writing a dissertation that can give you more information, should you need it!

1. Select a Manageable Topic

It is important to choose finance research topics within the given timeframe and resources. Select a research area that interests you and aligns with your career goals. This will help you stay inspired throughout the dissertation process.

2. Conduct a Thorough Literature Review

A comprehensive literature review forms the backbone of your research. After choosing the finance dissertation topics, dive deep into academic papers, books, and industry reports. Gain a solid understanding of your chosen area to identify research gaps and establish the significance of your study.

3. Define Clear Research Objectives

Clearly define your dissertation's research questions and objectives. It will provide a clear direction for your research and guide your data collection, analysis, and overall structure. Ensure your objectives are specific, measurable, achievable, relevant, and time-bound (SMART).

4. Collect and Analyse Data

Depending on your research methodology and your finance dissertation topics, collect and analyse relevant data to support your findings. It may involve conducting surveys, interviews, experiments, and analysing existing datasets. Choose appropriate statistical techniques and qualitative methods to derive meaningful insights from your data.

5. Structure and Organization

Pay attention to the structure and organisation of your dissertation. Follow a logical progression of chapters and sections, ensuring that each chapter contributes to the overall coherence of your study. Use headings, subheadings, and clear signposts to guide the reader through your work.

6. Proofread and Edit

Once you have completed the writing process, take the time to proofread and edit your dissertation carefully. Check for clarity, coherence, and proper grammar. Ensure that your arguments are well-supported, and eliminate any inconsistencies or repetitions. Pay attention to formatting, citation styles, and consistency in referencing throughout your dissertation.

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Finance Dissertation Topics

Now that you know what a finance dissertation is and why they are important, it's time to have a look at some of the best finance dissertation topics. For your convenience, we have segregated these topics into categories, including cryptocurrency, risk management, internet banking, and so many more. So, let's dive right in and explore the best finance dissertation topics:

Dissertation topics in Finance related to Cryptocurrency

1. The Impact of Regulatory Frameworks on the Volatility and Liquidity of Cryptocurrencies.

2. Exploring the Factors Influencing Cryptocurrency Adoption: A Comparative Study.

3. Assessing the Efficiency and Market Integration of Cryptocurrency Exchanges.

4. An Analysis of the Relationship between Cryptocurrency Prices and Macroeconomic Factors.

5. The Role of Initial Coin Offerings (ICOs) in Financing Startups: Opportunities and Challenges.

Dissertation topics in Finance related to Risk Management

1. The Effectiveness of Different Risk Management Strategies in Mitigating Financial Risks in Banking Institutions.

2. The Role of Derivatives in Hedging Financial Risks: A Comparative Study.

3. Analysing the Impact of Risk Management Practices on Firm Performance: A Case Study of a Specific Industry.

4. The Use of Stress Testing in Evaluating Systemic Risk: Lessons from the Global Financial Crisis.

5. Assessing the Relationship between Corporate Governance and Risk Management in Financial Institutions.

Dissertation topics in Finance related to Internet Banking

1. Customer Adoption of Internet Banking: An Empirical Study on Factors Influencing Usage.

Enhancing Security in Internet Banking: Exploring Biometric Authentication Technologies.

2. The Impact of Mobile Banking Applications on Customer Engagement and Satisfaction.

3. Evaluating the Efficiency and Effectiveness of Internet Banking Services in Emerging Markets.

4. The Role of Social Media in Shaping Customer Perception and Adoption of Internet Banking.

5. Fraud and Identity Theft are Accomplished via Internet Banking.

Dissertation topics in Finance related to Microfinance

1. The Impact of Microfinance on Poverty Alleviation: A Comparative Study of Different Models.

2. Exploring the Role of Microfinance in Empowering Women Entrepreneurs.

3. Assessing the Financial Sustainability of Microfinance Institutions in Developing Countries.

4. The Effectiveness of Microfinance in Promoting Rural Development: Evidence from a Specific Region.

5. Analysing the Relationship between Microfinance and Entrepreneurial Success: A Longitudinal Study.

Dissertation topics in Finance related to Retail and Commercial Banking

1. The Impact of Digital Transformation on Retail and Commercial Banking: A Case Study of a Specific Bank.

2. Customer Satisfaction and Loyalty in Retail Banking: An Analysis of Service Quality Dimensions.

3. Analysing the Relationship between Bank Branch Expansion and Financial Performance.

4. The Role of Fintech Startups in Disrupting Retail and Commercial Banking: Opportunities and Challenges.

5. Assessing the Impact of Mergers and Acquisitions on the Performance of Retail and Commercial Banks.

Dissertation topics in Finance related to Alternative Investment

1. The Performance and Risk Characteristics of Hedge Funds: A Comparative Analysis.

2. Exploring the Role of Private Equity in Financing and Growing Small and Medium-Sized Enterprises.

3. Analysing the Relationship between Real Estate Investments and Portfolio Diversification.

4. The Potential of Impact Investing: Evaluating the Social and Financial Returns.

5. Assessing the Risk-Return Tradeoff in Cryptocurrency Investments: A Comparative Study.

Dissertation topics in Finance related to International Affairs

1. The Impact of Exchange Rate Volatility on International Trade: A Case Study of a Specific Industry.

2. Analysing the Effectiveness of Capital Controls in Managing Financial Crises: Comparative Study of Different Countries.

3. The Role of International Financial Institutions in Promoting Economic Development in Developing Countries.

4. Evaluating the Implications of Trade Wars on Global Financial Markets.

5. Assessing the Role of Central Banks in Managing Financial Stability in a Globalised Economy.

Dissertation topics in Finance related to Sustainable Finance

1. The Impact of Sustainable Investing on Financial Performance.

2. The Role of Green Bonds in Financing Climate Change Mitigation and Adaptation.

3. The Development of Carbon Markets.

4. The Use of Environmental, Social, and Governance (ESG) Factors in Investment Decision-Making.

5. The Challenges and Opportunities of Sustainable Finance in Emerging Markets.

Dissertation topics in Finance related to Investment Banking

1. The Valuation of Distressed Assets.

2. The Pricing of Derivatives.

3. The Risk Management of Financial Institutions.

4. The Regulation of Investment Banks.

5. The Impact of Technology on the Investment Banking Industry.

Dissertation topics in Finance related to Actuarial Science

1. The Development of New Actuarial Models for Pricing Insurance Products.

2. The Use of Big Data in Actuarial Analysis.

3. The Impact of Climate Change on Insurance Risk.

4. The Design of Pension Plans That Are Sustainable in the Long Term.

5. The Use of Actuarial Science to Manage Risk in Other Industries, Such as Healthcare and Finance.

Dissertation topics in Finance related to Corporate Finance

1. Study the Relations Between Corporate Governance Structures and Financial Performance

2. Testing the Effects of Capital Structure on Firm Performance Across Different Industries

3. Effectiveness of Financial Management Practices in Emerging Markets

4. Integrating Sustainability and CSR Initiatives Impacts a Corporation’s Financial Performance and Enhances its Brand Reputation.

5. A Comparative Study of the Financing Strategies Employed in Mergers and Acquisitions.

Tips To Find Good Finance Dissertation Topics 

Embarking on a dissertation report on finance topics journey requires careful consideration of various factors. Your choice of topic in finance research topics is pivotal, as it sets the stage for the entire research process. We suggest the following tips that can help you pick the perfect dissertation topic:

1. Identify your interests and strengths 

2. Check for current relevance

3. Feedback from your superiors

4. Finalise the research methods

5. Gather the data

6. Work on the outline of your dissertation

7. Make a draft and proofread it

Lastly, we have discussed the importance of finance thesis topics and provided valuable writing tips and tips for finding the right topic. We have also presented a list of thesis topics for finance students within various subfields. With this, we hope you have great ideas for finance dissertations. Good luck with your finance research journey!

Frequently Asked Questions

How do i research for my dissertation project topics in finance, what is the best topic for dissertation topics for mba finance, what is the hardest finance topic, how do i choose the right topic for my dissertation in finance, where can i find a dissertation topic in finance.

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Have you ever found yourself angling for the perfect finance topic, only to be caught in the net of confusion? Well, reel in your worries, because this blog is your golden fish! We've curated 250 distinct finance research topics tailored to any taste. 

Need a nudge in the right direction? Or maybe you're after a whole new financial perspective? Whatever it is, our research paper writing service has got you covered. Dive into this assortment of finance research paper topics and choose an idea that speaks to you.

What Are Finance Research Topics?

finance is all about how money works – how it's made, how it's managed, and how it's spent. It essentially oversees the process of allocating resources and assets over time. This domain is fundamental for the smooth functioning of economies, businesses, and personal lives.

With this in mind, financial research topics are the subjects that explore how finances are managed. These subjects can range from anything from figuring out how Bitcoin affects the stock market to examining how a country's economy recovers after a recession.

Features of Good Finance Research Paper Topics

Now that you understand what a finance domain is all about, let’s discuss what makes finance research paper topics worthwhile. Before you pick any topic, make sure it fills the boxes of these requirements:

• Contemporary relevance Your topic should be connected to current issues or trends in finance.
• Focused scope Your topic should be specific enough to allow a deep analysis. For example, rather than exploring "Global finance," you might examine "The impact of cryptocurrency on global finance."
• Data accessibility Ensure you can find enough information about your topic to base your research on.
• Fresh perspective There are many aspects that have already been covered by other scholars. Make sure your topic offers fresh insights or explores a matter from a new angle.
• Personal engagement If you're excited about your study, that's a good sign you've picked a winning topic.

How to Choose a Finance Research Paper Topic?

Choosing a finance research topic idea is like going on a treasure hunt. But don’t be afraid. Our online essay writer team has  shared guidelines to help you find that 'X' marks the spot!

• Explore possible directions Read articles, watch videos, listen to podcasts. As you search for topics, jot down interesting ideas that capture your attention.
• Prioritize your interests Reflect on what really interests you. You might be fascinated by investment strategies or passionate about sustainable finance.
• Uncover the gaps Look for questions that are yet unanswered or try to recognize unique angles.
• Check for information Now, you need to ensure you have enough equipment and credible sources to work with.
• Take a leap Once you've done all your groundwork, go ahead and pick a theme that resonates with your goals.

Now that you have a clue how to spot decent finance research topic ideas, let’s move one to the actual list of suggestions.

Finance Research Topics List

Get ready to navigate through our collection of finance research paper topic ideas! We've mapped out these suggestions to explore. Each of these topics can be further divided into subtopics for a more in-depth analysis.

• Cryptocurrency's impact on traditional banking.
• Sustainable investment practices and implications.
• Unveiling the role of artificial intelligence in market predictions.
• Microloans and their role in alleviating poverty.
• Behavioral finance: Understanding investor psychology.
• Making a case for teaching money management in schools.
• The rise of fintech startups: Disruption or evolution?
• Entering the era of digital wallets: What's next?
• Exploring the balance between profit and social responsibility in impact investing.
• Success of crowdfunding campaigns.
• Securing our online vaults: The importance of cybersecurity in banking.
• Strategies for recovery after an economic downturn.
• Central banks and their contribution to economic stability.
• Blockchain technology: A new era of transaction processing.
• Robo-advisors in investment management.

Interesting Finance Research Topics

Fasten your seatbelts, scholars! We're about to take off on another round of academic adventure with interesting finance topics. With these ideas at hand, you are sure to find a captivating topic for your financial project.

• How does pandemic affect the global economy?
• Cryptocurrency: A bubble or new standard?
• Influence of artificial intelligence on credit scoring systems.
• Evolution and significance of green bonds.
• Correlation between investor psychology and stock market volatility.
• Impact of educational initiatives on personal money management.
• Fintech startups and traditional banking: Rivals or collaborators?
• Mobile wallets: Balancing convenience and security.
• Is social responsibility becoming a decisive factor in investment choices?
• Success factors in crowdfunding campaigns.
• Prioritizing cybersecurity in the age of digital transactions.
• Strategic approaches to post-recession recovery.
• What role do central banks play in navigating economic turbulence?
• Applications of blockchain beyond cryptocurrency.
• Automated advisors and their impact on investment management.

Easy Finance Research Topics

Finance can be a tough nut to crack. But worry not, we've sifted through the complexities to bring you easy finance research papers topics. They'll help you find the right direction without overwhelming you. Are you ready to take the plunge?

• Understanding credit scores: What makes them rise and fall?
• Basics of personal budgeting.
• An overview of stock market investing.
• The rise and implications of mobile banking.
• Microloans and their impact on small businesses.
• Cryptocurrency: Hype or a game-changer?
• Retirement planning: A critical component of personal finance.
• What are financial regulations?
• A closer look at online payment systems.
• How does crowdfunding work?
• Ethics in finance.
• Emergency fund creation: Its significance in financial planning.
• Tax planning: Exploring strategies and impacts on personal wealth.
• Exploring e-commerce business models.
• Insurance policies and their role in financial risk management.

Great Finance Research Paper Topics

The finance world is a goldmine of great research avenues waiting to be explored. Below we've collected fantastic research topics in finance to inspire your work. Now, all you need to do is take your pick and start investigating.

• Exploring mergers and acquisitions in global corporations.
• Is venture capital a catalyst for startup success?
• Public fiscal policy across nations.
• Insider trading: Unethical advantage or strategic insight?
• Unpacking the intricacies of derivatives and risk management.
• How digital transformation is reshaping banking services.
• Harnessing mathematics for modeling in quantitative disciplines.
• Investigating corporate social responsibility in multinational institutions.
• Unraveling the role of AI in fraud prevention.
• Are microcredit initiatives a key to broader financial inclusion?
• Psychology that drives economic decisions.
• How do credit rating agencies influence market dynamics?
• Ripple effects of inflation on investment portfolios.
• What role does forensic accounting play in unveiling fraud?
• Balance between debt and equity in capital structuring.

Popular Finance Research Topics

Are you wondering what's trending in the world of finance? Consider these popular financial topics to write about and choose one for your project. Don't forget to check if your professor has additional guidelines before you get started! If you have unique requirements and want to obtain a top-quality work tailored to your needs, consider  buying research papers from our experts.

• Machine learning in credit risk modeling: A new frontier?
• Can businesses strike a balance between sustainability and profit?
• Peer-to-peer lending: Revolutionizing or destabilizing finance?
• Microfinancing in developing nations: An analysis of success factors.
• The growth of ESG (Environmental, Social, and Governance) investing.
• Global economic impact of sovereign debt crises.
• How are trends in corporate governance shaping businesses?
• Impact of globalization on investment strategies.
• Examining the rise and implications of neobanks.
• Fiscal policy responses to climate change: A global overview.
• Role of behavioral biases in investment decision-making.
• Economic fallout of pandemics: A case study of COVID-19
• Evaluating the ethics of high-frequency trading.
• Internet of Things (IoT) and its implications for financial services.
• Impact of FinTech innovations on traditional banking.

Current Research Topics in Finance

Keeping pace with the latest trends is crucial in research, and finance is no exception. We've therefore rounded up current finance topics for a research paper, designed to resonate with the here and now.

• Central Bank Digital Currencies (CBDCs): A new era in finance?
• Sustainable finance: Navigating the path to greener economies.
• Tech giants entering financial services: Disruption or evolution?
• Exploring the implications of Brexit on global trade and finance.
• Regulation of fintech in the era of digital currencies.
• Influence of geopolitical conflicts on global fiscal markets.
• Influence of political stability on stock market performance.
• Data privacy in financial market.
• Implications of quantum computing for financial cryptography.
• Ethical implications of AI in finance.
• Effects of trade wars on currency markets.
• COVID-19 and the shift towards a cashless society.
• Evaluating the stability of cryptocurrency markets.
• Impact of remote work trends on global economies.
• Leveraging big data for predictive analysis in finance.

>> Read more: Economics Research Paper Topics

Best Finance Research Topics

When it comes to research, not all topics are created equal. To bring out your best, we've curated a selection of the finest finance research topic list. These topics offer a blend of depth, relevance, and originality.

• Financial implications of demographic shifts in developed economies.
• Challenges of regulating emerging financial technologies.
• Big data and its transformative role in credit risk management.
• Comparative analysis of traditional banks and digital-only banks.
• The rise of ethical investing: Fad or future of finance?
• Financial resilience in the face of global crises.
• Space economy: Financial prospects and challenges.
• Financing strategies for small to medium enterprises in emerging markets.
• Impact of policy changes on financial planning strategies.
• Rise of smart contracts.
• Global economic impacts of aging populations.
• Assessing the financial viability of renewable energy projects.
• Influence of machine learning on investment portfolio management.
• The future of cash: An outdated concept or an enduring necessity?
• Financial implications of autonomous vehicles.

Unique Financial Research Topics

Originality is the currency of academic research, and in finance, it's no different. To help you make your mark, we've compiled a list of truly unique finance paper topics. These topics were selected for their potential to bring fresh perspectives.

• Finance in promoting circular economies.
• Emerging finance strategies for carbon capture and storage.
• Leveraging data analytics to predict market crashes.
• Role of financial regulation in preventing tech monopolies.
• Financing deep-sea exploration: Opportunities and challenges.
• How are finance and agri-tech interrelated?
• The cost of data breaches.
• How do artificial intelligence laws affect fintech?
• Exploring financial strategies for preserving biodiversity.
• Fiscal strategies for promoting urban farming.
• What role does financial policy play in addressing income inequality?
• Financial modeling in the age of quantum computing.
• Use of predictive analytics in insurance underwriting.
• Role of finance in achieving zero-waste economies.
• Financial dynamics of eSports.

Finance Research Topic Ideas for Presentation

Creating a lasting presentation can be a challenge, but it doesn’t have to be. We’ve gathered some of the most interesting financial topics that you can use for your next classroom or workplace presentation.

• Tales of Wall Street: Lessons from the biggest fiscal scandals.
• Economic meltdowns: Causes and lessons learned.
• Gender disparity in wealth accumulation..
• Sustainability and finance: The role of green investments.
• Economic indicators: Predicting financial trends.
• Power of budgeting: Key to financial success.
• Venture capital's role in fostering innovation.
• Microfinance's impact on poverty reduction.
• Forensic accounting.
• Wealth disparity: Analyzing the growing economic divide.
• Inflation and interest: A balancing act.
• Pension crisis in different countries.
• Understanding financial derivatives: Beyond the basics.
• Implications of tax evasion: A global perspective.
• Impact of electronic payment systems on consumer behavior.

Finance Research Paper Topic Ideas for Students

Below we've handpicked a compilation of the best finance research paper topics perfect for budding scholars. To tailor to your academic level, we've meticulously sorted these themes, ensuring they resonate with your knowledge and challenge your critical thinking skills. Whether you're an undergraduate or a postgraduate seeking finance topics to write about, we've got you covered.

Finance Research Topics for College Students

College students majoring in finance should demonstrate solid critical analysis and problem-solving skills. Unlike high school, college provides a platform to delve deeper into complex issues and challenge existing theories. Finance projects for students in college should push beyond surface-level knowledge. With these requirements in mind, we've assembled a set of finance related research paper topics tailored to college students.

• Algorithmic trading: A boon or a bane?
• Financial literacy and student loan debt: Is there a connection?
• Does social media influence stock market trends?
• Effect of corporate scandals on stock prices.
• Role of financial planning in achieving life goals.
• Micro-financing in developing economies.
• Sustainable investing: A look into its effectiveness.
• Role of finance in promoting social entrepreneurship.
• Impact of fiscal policy changes on small businesses.
• Exploring the world of impact investing.
• Personal finance: Comparing self-taught vs. formally educated approaches.
• A comparative study of banking systems across the globe.
• Financial planning: Analysis of gender-specific approaches.
• Impact of economic sanctions on financial markets.
• The dynamics of stock market bubbles.

Finance Research Paper Topics for University Students

University students should exhibit a higher degree of critical thinking and research, as compared to college students. Projects conducted at university level should be ambitious and focused on making an impact in the field of finance. To support your endeavors, we've provided a list of financial research topics for university students.

• Financial management in non-profit organizations.
• How do cultural factors influence investment decisions?
• What significance does microfinancing have in women's empowerment?
• Financial aftermath of natural disasters.
• How do exchange rates affect the tourism industry?
• Financial implications of deepfake technology.
• Challenges and opportunities of financial decentralization.
• Dissecting the financial fallout of global pandemics.
• Strategies in the age of the sharing economy.
• A comparative analysis of Eastern and Western investment philosophies.
• Investigating the correlation between economic freedom and prosperity.
• Role of finance in curbing wildlife trafficking.
• Finance strategies to combat global water scarcity.
• Economic resilience: Lessons from small island nations.
• Analyzing fiscal challenges in the healthcare industry.

Finance Research Paper Topics for MBA

MBA students must showcase a deep understanding of finance principles and an aptitude for critical thinking. To ensure you stay ahead in the game, we've compiled a list of MBA finance topics for a paper to research.

• Role of finance in the transition to clean energy.
• Impact of intergenerational wealth transfer on the global economy.
• Fiscal planning in global corporations.
• Leadership and its impact on financial decision-making.
• Role of finance in driving corporate digital transformation.
• Venture capital investment strategies in emerging markets.
• Implications of corporate restructuring.
• Financial strategies for fostering corporate diversity and inclusion.
• The future of finance in a post-fossil fuel world.
• Risk management strategies in the era of FinTech disruption.
• Developing fiscal strategies for business resilience post-pandemic.
• How does extended reality (XR) impact the business world?
• Effective pension fund management.
• The use of blockchain technology in tracking funds and resources.
• How do venture capitalists assess start-up risks?

Finance Research Topics by Subject

Finance is a vast field with many branches under its umbrella. To make it easier to browse through these research topic ideas for finance, we've organized them according to subject matter. Take a look at the following themes and find a fitting idea!

International Finance Research Paper Topics

The monetary landscape is constantly expanding. To keep up with these changes, many universities are now offering majors in international finance. For those interested in exploring the field on a global scale, here are some relevant international finance research topics.

• Macroeconomic policies across different countries.
• What role does foreign direct investment play in global economies?
• Implications of capital flows on exchange rates.
• Impact of international monetary systems on global financial stability.
• Challenges and opportunities in cross-border investments.
• Trade deficits and their effect on financial markets.
• What is the role of financial institutions in global development?
• Effects of currency manipulation.
• Political and economic risks of investing abroad.
• What impact do tariffs have on international finance?
• Exploring the potential of Islamic Banking as an alternative system.
• International taxation systems.
• Financial education in promoting economic growth in developing countries.
• Trade agreements in facilitating economic integration.
• Foreign exchange risk management strategies.

Public Finance Research Topics

Public finance is a field of study that explores the use and redistribution of resources in the public sector. Below we prepared public finance topics to talk about.

• Public-private partnership in infrastructure development.
• Taxation policies for financial inclusion and reduced income inequality.
• Public debt and economic growth: Evidence from developing countries.
• Successful municipal bond issuance.
• Public finance reforms: Enhancing transparency and accountability.
• Public expenditure efficiency in achieving economic development goals.
• Government spending in education and long-term economic growth.
• Corruption's impact on public finance management.
• Fiscal austerity measures.
• Key factors influencing sovereign credit ratings.
• Budget deficits in developed countries.
• Comparing public and private pension systems.
• Alternative revenue sources for governments.
• The impact of international aid on developing countries.
• Charity tax incentives and their effect on donations.

Corporate Finance Research Topics

Corporate finance is an important area of study that covers a variety of topics related to corporate investments, financial management, and stock market analysis. If you are unsure where to begin, look through these research topics in corporate finance.

• Ethical investing's impact on company monetary strategies.
• Fiscal management in achieving gender pay equality in firms.
• Assessing 'greenwashing' in corporations from a monetary perspective.
• Tax policy implications on business fiscal decisions.
• Optimizing financial resources in multidivisional organizations.
• Investment in cybersecurity: An essential aspect of business economics.
• Transparency in corporate fiscal activities in the digital age.
• Economic recessions and counteracting business monetary strategies.
• How automation and AI transform company monetary management?
• Corporate social responsibility's influence on fiscal strategies.
• Short-term versus long-term financial planning in businesses.
• Effects of international trade agreements on company monetary policies.
• Mitigating supply chain risks through smart fiscal management.
• Implications of corporate rebranding.
• Inflation's effects on budgeting and forecasting in corporations.

Business Finance Research Topics

Business finance is a field of study that examines the allocation of capital within an organization. This subject involves understanding all aspects of financial management, from budgeting to risk assessment. Here are some business finance research topics you can explore.

• Assessing financial health of startups: Metrics that matter.
• Role of venture capitalists in boosting business growth.
• Challenges of monetary management in family-owned businesses.
• How effective is crowdfunding for startup financing?
• Strategies for risk mitigation in retail business.
• Artificial intelligence in business budgeting and forecasting.
• Credit management in small to medium enterprises (SMEs).
• Profitability analysis in manufacturing businesses.
• Fiscal planning in business expansion.
• Effects of global economic changes on business budget management.
• Influence of e-commerce on financial strategies of retail businesses.
• Corporate governance: A comparative analysis.
• Private equity investment strategies.
• Shareholder activism in today’s markets.
• Portfolio management strategies for institutional investors.

>> Read more: Business Research Paper Topics

Personal Finance Research Topics

Personal finance focuses on understanding and managing your money to achieve financial security. Research in this area covers a wide array of aspects, from budgeting and saving to investment decisions and retirement planning. Here are some ideas for personal finance research topics:

• Psychology of spending: Are we programmed to save or spend?
• Retirement planning: How early is too early?
• Impact of financial education on money management habits.
• Role of technology in individual budgeting and saving.
• Is debt an inevitable part of our life?
• Investing for the future: Stocks vs real estate.
• Healthcare costs and their impact on individual economic stability.
• Millennials and money.
• How does inflation impact our day-to-day budgeting?
• Role of emergency funds in financial security.
• Economic recessions on retirement planning.
• Estate planning and wealth transfer.
• Credit scores: Their significance in personal economic health.
• Role of government policies in shaping individual investment strategies.
• Balancing personal spending and saving.

Healthcare Finance Research Paper Topics

Healthcare finance is a specialized field of study that focuses on the financial management of healthcare organizations. It requires an understanding of both financial and healthcare-specific topics, such as reimbursement policies, budgeting and forecasting models, and population health management. Here are some research paper topics related to healthcare finance.

• Budgeting challenges in public health institutions.
• Role of telehealth in reducing healthcare costs.
• Financing strategies for rare diseases treatments.
• Impact of the aging population on healthcare expenditures.
• Population health management and its monetary burdens.
• Impact of COVID-19 on the financial stability of healthcare systems.
• Value-based care: Monetary rewards or challenges?
• Analyzing the costs of electronic health records implementation.
• Cost-effectiveness of preventive care: An exploration.
• Reimbursement policies and their effect on medical practices.
• Influence of pharmaceutical pricing on healthcare costs.
• Out-of-pocket costs: Barrier to healthcare access?
• Role of financial management in healthcare mergers and acquisitions.
• Medical bankruptcy: An unspoken reality?
• Forecasting models for healthcare expenditures: A comparative study.

>> View more: Health Research Topics

Bottom Line on Finance Research Papers Topics

Hopefully, this list of finance research paper topics has given you some great ideas for your next project. Remember, the best way to make sure you write a good finance paper is to start with an interesting and informative topic. If you need any help with the writing process, don’t hesitate to contact our college paper writers .

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• Open access
• Published: 08 June 2020

Deep learning in finance and banking: A literature review and classification

• Jian Huang 1 ,
• Junyi Chai   ORCID: orcid.org/0000-0003-1560-845X 2 &
• Stella Cho 2  

Frontiers of Business Research in China volume  14 , Article number:  13 ( 2020 ) Cite this article

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Deep learning has been widely applied in computer vision, natural language processing, and audio-visual recognition. The overwhelming success of deep learning as a data processing technique has sparked the interest of the research community. Given the proliferation of Fintech in recent years, the use of deep learning in finance and banking services has become prevalent. However, a detailed survey of the applications of deep learning in finance and banking is lacking in the existing literature. This study surveys and analyzes the literature on the application of deep learning models in the key finance and banking domains to provide a systematic evaluation of the model preprocessing, input data, and model evaluation. Finally, we discuss three aspects that could affect the outcomes of financial deep learning models. This study provides academics and practitioners with insight and direction on the state-of-the-art of the application of deep learning models in finance and banking.

Introduction

Deep learning (DL) is an advanced technique of machine learning (ML) based on artificial neural network (NN) algorithms. As a promising branch of artificial intelligence, DL has attracted great attention in recent years. Compared with conventional ML techniques such as support vector machine (SVM) and k-nearest neighbors (kNN), DL possesses advantages of the unsupervised feature learning, a strong capability of generalization, and a robust training power for big data. Currently, DL has been applied comprehensively in classification and prediction tasks, computer visions, image processing, and audio-visual recognition (Chai and Li 2019 ). Although DL was developed in the field of computer science, its applications have penetrated diversified fields such as medicine, neuroscience, physics and astronomy, finance and banking (F&B), and operations management (Chai et al. 2013 ; Chai and Ngai 2020 ). The existing literature lacks a good overview of DL applications in F&B fields. This study attempts to bridge this gap.

While DL is the focus of computer vision (e.g., Elad and Aharon 2006 ; Guo et al. 2016 ) and natural language processing (e.g., Collobert et al. 2011 ) in the mainstream, DL applications in F&B are developing rapidly. Shravan and Vadlamani (2016) investigated the tools of text mining for F&B domains. They examined the representative ML algorithms, including SVM, kNN, genetic algorithm (GA), and AdaBoost. Butaru et al. ( 2016 ) compared performances of DL algorithms, including random forests, decision trees, and regularized logistic regression. They found that random forests gained the highest classification accuracy in the delinquency status.

Cavalcante et al. ( 2016 ) summarized the literature published from 2009 to 2015. They analyzed DL models, including multi-layer perceptron (MLP) (a fast library for approximate nearest neighbors), Chebyshev functional link artificial NN, and adaptive weighting NN. Although the study constructed a prediction framework in financial trading, some notable DL techniques such as long short-term memory (LSTM) and reinforcement learning (RL) models are neglect. Thus, the framework cannot ascertain the optimal model in a specific condition.

The reviews of the existing literature are either incomplete or outdated. However, our study provides a comprehensive and state-of-the-art review that could capture the relationships between typical DL models and various F&B domains. We identified critical conditions to limit our collection of articles. We employed academic databases in Science Direct, Springer-Link Journal, IEEE Xplore, Emerald, JSTOR, ProQuest Database, EBSCOhost Research Databases, Academic Search Premier, World Scientific Net, and Google Scholar to search for articles. We used two groups of keywords for our search. One group is related to the DL, including “deep learning,” “neural network,” “convolutional neural networks” (CNN), “recurrent neural network” (RNN), “LSTM,” and “RL.” The other group is related to finance, including “finance,” “market risk,” “stock risk,” “credit risk,” “stock market,” and “banking.” It is important to conduct cross searches between computer-science-related and finance-related literature. Our survey exclusively focuses on the financial application of DL models rather than other DL models like SVM, kNN, or random forest. The time range of our review was set between 2014 and 2018. In this stage, we collected more than 150 articles after cross-searching. We carefully reviewd each article and considered whether it is worthy of entering our pool of articles for review. We removed the articles if they are not from reputable journals or top professional conferences. Moreover, articles were discarded if the details of financial DL models presented were not clarified. Thus, 40 articles were selected for this review eventually.

This study contributes to the literature in the following ways. First, we systematically review the state-of-the-art applications of DL in F&B fields. Second, we summarize multiple DL models regarding specified F&B domains and identify the optimal DL model of various application scenarios. Our analyses rely on the data processing methods of DL models, including preprocessing, input data, and evaluation rules. Third, our review attempts to bridge the technological and application levels of DL and F&B, respectively. We recognize the features of various DL models and highlight their feasibility toward different F&B domains. The penetration of DL into F&B is an emerging trend. Researchers and financial analysts should know the feasibilities of particular DL models toward a specified financial domain. They usually face difficulties due to the lack of connections between core financial domains and numerous DL models. This study will fill this literature gap and guide financial analysts.

The rest of this paper is organized as follows. Section 2 provides a background of DL techniques. Section 3 introduces our research framework and methodology. Section 4 analyzes the established DL models. Section 5 analyzes key methods of data processing, including data preprocessing and data inputs. Section 6 captures appeared criteria for evaluating the performance of DL models. Section 7 provides a general comparison of DL models against identified F&B domains. Section 8 discusses the influencing factors in the performance of financial DL models. Section 9 concludes and outlines the scope for promising future studies.

Background of deep learning

Regarding DL, the term “deep” presents the multiple layers that exist in the network. The history of DL can be traced back to stochastic gradient descent in 1952, which is employed for an optimization problem. The bottleneck of DL at that time was the limit of computer hardware, as it was very time-consuming for computers to process the data. Today, DL is booming with the developments of graphics processing units (GPUs), dataset storage and processing, distributed systems, and software such as Tensor Flow. This section briefly reviews the basic concept of DL, including NN and deep neural network (DNN). All of these models have greatly contributed to the applications in F&B.

The basic structure of NN can be illustrated as Y  =  F ( X T w  +  c ) regarding the independent (input) variables X , the weight terms w , and the constant terms c . Y is the dependent variable and X is formed as an n  ×  m matrix for the number of training sample n and the number of input variables m . To apply this structure in finance, Y can be considered as the price of next term, the credit risk level of clients, or the return rate of a portfolio. F is an activation function that is unique and different from regression models. F is usually formulated as sigmoid functions and tanh functions. Other functions can also be used, including ReLU functions, identity functions, binary step functions, ArcTan functions, ArcSinh functions, ISRU functions, ISRLU functions, and SQNL functions. If we combine several perceptrons in each layer and add a hidden layer from Z 1 to Z 4 in the middle, we term a single layer as a neural network, where the input layers are the X s , and the output layers are the Y s . In finance, Y can be considered as the stock price. Moreover, multiple Y s are also applicable; for instance, fund managers often care about future prices and fluctuations. Figure  1 illustrates the basic structure.

The structure of NN

Based on the basic structure of NN shown in Fig.  1 , traditional networks include DNN, backpropagation (BP), MLP, and feedforward neural network (FNN). Using these models can ignore the order of data and the significance of time. As shown in Fig.  2 , RNN has a new NN structure that can address the issues of long-term dependence and the order between input variables. As financial data in time series are very common, uncovering hidden correlations is critical in the real world. RNN can be better at solving this problem, as compared to other moving average (MA) methods that have been frequently adopted before. A detailed structure of RNN for a sequence over time is shown in Part B of the Appendix (see Fig. 7 in Appendix ).

The abstract structure of RNN

Although RNN can resolve the issue of time-series order, the issue of long-term dependencies remains. It is difficult to find the optimal weight for long-term data. LSTM, as a type of RNN, added a gated cell to overcome long-term dependencies by combining different activation functions (e.g., sigmoid or tanh). Given that LSTM is frequently used for forecasting in the finance literature, we extract LSTM from RNN models and name other structures of standard RNN as RNN(O).

As we focus on the application rather than theoretical DL aspect, this study will not consider other popular DL algorithms, including CNN and RL, as well as Latent variable models such as variational autoencoders and generative adversarial network. Table 6 in Appendix shows a legend note to explain the abbreviations used in this paper. We summarize the relationship between commonly used DL models in Fig.  3 .

Relationships of reviewed DL models for F&B domains

Research framework and methodology

Our research framework is illustrated in Fig.  4 . We combine qualitative and quantitative analyses of the articles in this study. Based on our review, we recognize and identify seven core F&B domains, as shown in Fig.  5 . To connect the DL side and the F&B side, we present our review on the application of the DL model in seven F&B domains in Section 4. It is crucial to analyze the feasibility of a DL model toward particular domains. To do so, we provide summarizations in three key aspects, including data preprocessing, data inputs, and evaluation rules, according to our collection of articles. Finally, we determine optimal DL models regarding the identified domains. We further discuss two common issues in using DL models for F&B: overfitting and sustainability.

The research framework of this study

The identified domains of F&B for DL applications

Figure  5 shows that the application domains can be divided into two major areas: (1) banking and credit risk and (2) financial market investment. The former contains two domains: credit risk prediction and macroeconomic prediction. The latter contains financial prediction, trading, and portfolio management. Prediction tasks are crucial, as emphasized by Cavalcante et al. ( 2016 ). We study this domain from three aspects of prediction, including exchange rate, stock market, and oil price. We illustrate this structure of application domains in F&B.

Figure  6 shows a statistic in the listed F&B domains. We illustrate the domains of financial applications on the X-axis and count the number of articles on the Y-axis. Note that a reviewed article could cover more than one domain in this figure; thus, the sum of the counts (45) is larger than the size of our review pool (40 articles). As shown in Fig.  6 , stock marketing prediction and trading dominate the listed domains, followed by exchange rate prediction. Moreover, we found two articles on banking credit risk and two articles on portfolio management. Price prediction and macroeconomic prediction are two potential topics that deserve more studies.

A count of articles over seven identified F&B domains

Application of DL models in F&B domains

Based on our review, six types of DL models are reported. They are FNN, CNN, RNN, RL, deep belief networks (DBN), and restricted Boltzmann machine (RBM). Regarding FNN, several papers use the alternative terms of backpropagation artificial neural network (ANN), FNN, MLP, and DNN. They have an identical structure. Regarding RNN, one of its well-known models in the time-series analysis is called LSTM. Nearly half of the reviewed articles apply FNN as the primary DL technique. Nine articles apply LSTM, followed by eight articles for RL, and six articles for RNN. Minor ones that are applied in F&B include CNN, DBM, and RBM. We count the number of articles that use various DL models in seven F&B domains, as shown in Table  1 . FNN is the principal model used in exchange rate, price, and macroeconomic predictions, as well as banking default risk and credit. LSTM and FNN are two kinds of popular models for stock market prediction. Differently, RL and FNN are frequently used regarding stock trading. FNN, RL, and simple RNN can be conducted in portfolio management. FNN is the primary model in macroeconomic and banking risk prediction. CNN, LSTM, and RL are emerging research approaches in banking risk prediction. The detailed statistics that contain specific articles can be found in Table 5 in Appendix .

Exchange rate prediction

Shen et al. ( 2015 ) construct an improved DBN model by including RBM and find that their model outperforms the random walk algorithm, auto-regressive-moving-average (ARMA), and FNN with fewer errors. Zheng et al. ( 2017 ) examine the performance of DBN and find that the DBN model estimates the exchange rate better than FNN model does. They find that a small number of layer nodes engender a more significant effect on DBN.

Several scholars believe that a hybrid model should have better performance. Ravi et al. ( 2017 ) contribute a hybrid model by using MLP (FNN), chaos theory, and multi-objective evolutionary algorithms. Their Chaos+MLP + NSGA-II model Footnote 1 has a mean squared error (MSE) with 2.16E-08 that is very low. Several articles point out that only a complicated neural network like CNN can gain higher accuracy. For example, Galeshchuk and Mukherjee ( 2017 ) conduct experiments and claim that a single hidden layer NN or SVM performs worse than a simple model like moving average (MA). However, they find that CNN could achieve higher classification accuracy in predicting the direction of the change of exchange rate because of successive layers of DNN.

Stock market prediction

In stock market prediction, some studies suggest that market news may influence the stock price and DL model, such as using a magic filter to extract useful information for price prediction. Matsubara et al. ( 2018 ) extract information from the news and propose a deep neural generative model to predict the movement of the stock price. This model combines DNN and a generative model. It suggests that this hybrid approach outperforms SVM and MLP.

Minh et al. ( 2017 ) develop a novel framework with two streams combining the gated recurrent unit network and the Stock2vec. It employs a word embedding and sentiment training system on financial news and the Harvard IV-4 dataset. They use the historical price and news-based signals from the model to predict the S&P500 and VN-index price directions. Their model shows that the two-stream gated recurrent unit is better than the gated recurrent unit or the LSTM. Jiang et al. ( 2018 ) establish a recurrent NN that extracts the interaction between the inner-domain and cross-domain of financial information. They prove that their model outperforms the simple RNN and MLP in the currency and stock market. Krausa and Feuerriegel ( 2017 ) propose that they can transform financial disclosure into a decision through the DL model. After training and testing, they point out that LSTM works better than the RNN and conventional ML methods such as ridge regression, Lasso, elastic net, random forest, SVR, AdaBoost, and gradient boosting. They further pre-train words embeddings with transfer learning (Krausa and Feuerriegel 2017 ). They conclude that better performance comes from LSTM with word embeddings. In the sentiment analysis, Sohangir et al. ( 2018 ) compares LSTM, doc2vec, and CNN to evaluate the stock opinions on the StockTwits. They conclude that CNN is the optimal model to predict the sentiment of authors. This result may be further applied to predict the stock market trend.

Data preprocessing is conducted to input data into the NN. Researchers may apply numeric unsupervised methods of feature extraction, including principal component analysis, autoencoder, RBM, and kNN. These methods can reduce the computational complexity and prevent overfitting. After the input of high-frequency transaction data, Chen et al. ( 2018b ) establish a DL model with an autoencoder and an RBM. They compare their model with backpropagation FNN, extreme learning machine, and radial basis FNN. They claim that their model can better predict the Chinese stock market. Chong et al. ( 2017 ) apply the principal component analysis (PCA) and RBM with high-frequency data of the South Korean market. They find that their model can explain the residual of the autoregressive model. The DL model can thus extract additional information and improve prediction performance. More so, Singh and Srivastava ( 2017 ) describe a model involving 2-directional and 2-dimensional (2D 2 ) PCA and DNN. Their model outperforms 2D 2 with radial basis FNN and RNN.

For time-series data, sometimes it is difficult to judge the weight of long-term and short-term data. The LSTM model is just for resolving this problem in financial prediction. The literature has attempted to prove that LSTM models are applicable and outperform conventional FNN models. Yan and Ouyang ( 2017 ) apply LSTM to challenge the MLP, SVM, and kNN in predicting a static and dynamic trend. After a wavelet decomposition and a reconstruction of the financial time series, their model can be used to predict a long-term dynamic trend. Baek and Kim ( 2018 ) apply LSTM not only in predicting the price of S&P500 and KOSPI200 but also in preventing overfitting. Kim and Won ( 2018 ) apply LSTM in the prediction of stock price volatility. They propose a hybrid model that combines LSTM with three generalized autoregressive conditional heteroscedasticity (GARCH)-type models. Hernandez and Abad ( 2018 ) argue that RBM is inappropriate for dynamic data modeling in the time-series analysis because it cannot retain memory. They apply a modified RBM model called p -RBM that can retain the memory of p past states. This model is used in predicting market directions of the NASDAQ-100 index. Compared with vector autoregression (VAR) and LSTM, notwithstanding, they find that LSTM is better because it can uncover the hidden structure within the non-linear data while VAR and p -RBM cannot capture the non-linearity in data.

CNN was established to predict the price with a complicated structure. Making the best use of historical price, Dingli and Fournier ( 2017 ) develop a new CNN model. This model can predict next month’s price. Their results cannot surpass other comparable models, such as logistic regression (LR) and SVM. Tadaaki ( 2018 ) applies the financial ratio and converts them into a “grayscale image” in the CNN model. The results reveal that CNN is more efficient than decision trees (DT), SVM, linear discriminant analysis, MLP, and AdaBoost. To predict the stock direction, Gunduz et al. ( 2017 ) establish a CNN model with a so-called specially ordered feature set whose classifier outperforms either CNN or LR.

Stock trading

Many studies adopt the conventional FNN model and try to set up a profitable trading system. Sezer et al. ( 2017 ) combine GA with MLP. Chen et al. ( 2017 ) adopt a double-layer NN and discover that its accuracy is better than ARMA-GARCH and single-layer NN. Hsu et al. ( 2018 ) equip the Black-Scholes model and a three-layer fully-connected feedforward network to estimate the bid-ask spread of option price. They argue that this novel model is better than the conventional Black-Scholes model with lower RMSE. Krauss et al. ( 2017 ) apply DNN, gradient-boosted-trees, and random forests in statistical arbitrage. They argue that their returns outperform the market index S&P500.

Several studies report that RNN and its derivate models are potential. Deng et al. ( 2017 ) extend the fuzzy learning into the RNN model. After comparing their model to different DL models like CNN, RNN, and LSTM, they claim that their model is the optimal one. Fischer and Krauss ( 2017 ) and Bao et al. ( 2017 ) argue that LSTM can create an optimal trading system. Fischer and Krauss ( 2017 ) claim that their model has a daily return of 0.46 and a sharp ratio of 5.8 prior to the transaction cost. Given the transaction cost, however, LSTM’s profitability fluctuated around zero after 2010. Bao et al. ( 2017 ) advance Fischer and Krauss’s ( 2017 ) work and propose a novel DL model (i.e., WSAEs-LSTM model). It uses wavelet transforms to eliminate noise, stacked autoencoders (SAEs) to predict stock price, and LSTM to predict the close price. The result shows that their model outperforms other models such as WLSTM, Footnote 2 LSTM, and RNN in predictive accuracy and profitability.

RL is popular recently despite its complexity. We find that five studies apply this model. Chen et al. ( 2018a ) propose an agent-based RL system to mimic 80% professional trading strategies. Feuerriegel and Prendinger ( 2016 ) convert the news sentiment into the signal in the trading system, although their daily returns and abnormal returns are nearly zero. Chakraborty ( 2019 ) cast the general financial market fluctuation into a stochastic control problem and explore the power of two RL models, including Q-learning Footnote 3 and state-action-reward-state-action (SARSA) algorithm. Both models can enhance profitability (e.g., 9.76% for Q-learning and 8.52% for SARSA). They outperform the buy-and-hold strategy. Footnote 4 Zhang and Maringer ( 2015 ) conduct a hybrid model called GA, with recurrent RL. GA is used to select an optimal combination of technical indicators, fundamental indicators, and volatility indicators. The out-of-sample trading performance is improved due to a significantly positive Sharpe ratio. Martinez-Miranda et al. ( 2016 ) create a new topic of trading. It uses a market manipulation scanner model rather than a trading system. They use RL to model spoofing-and-pinging trading. This study reveals that their model just works on the bull market. Jeong and Kim ( 2018 ) propose a model called deep Q-network that is constructed by RL, DNN, and transfer learning. They use transfer learning to solve the overfitting issue incurred as a result of insufficient data. They argue that the profit yields in this system increase by four times the amount in S&P500, five times in KOSPI, six times in EuroStoxx50, and 12 times in HIS.

Banking default risk and credit

Most articles in this domain focus on FNN applications. Rönnqvist and Sarlin ( 2017 ) propose a model for detecting relevant discussions in texting and extracting natural language descriptions of events. They convert the news into a signal of the bank-distress report. In their back-test, their model reflects the distressing financial event of the 2007–2008 period.

Zhu et al. ( 2018 ) propose a hybrid CNN model with a feature selection algorithm. Their model outperforms LR and random forest in consumer credit scoring. Wang et al. ( 2019 ) consider that online operation data can be used to predict consumer credit scores. They thus convert each kind of event into a word and apply the Event2vec model to transform the word into a vector in the LSTM network. The probability of default yields higher accuracy than other models. Jurgovsky et al. ( 2018 ) employs the LSTM to detect credit card fraud and find that LSTM can enhance detection accuracy.

Han et al. ( 2018 ) report a method that adopts RL to assess the credit risk. They claim that high-dimensional partial differential equations (PDEs) can be reformulated by using backward stochastic differential equations. NN approximates the gradient of the unknown solution. This model can be applied to F&B risk evaluation after considering all elements such as participating agents, assets, and resources, simultaneously.

Portfolio management

Song et al. ( 2017 ) establish a model after combining ListNet and RankNet to make a portfolio. They take a long position for the top 25% stocks and hold the short position for the bottom 25% stocks weekly. The ListNetlong-short model is the optimal one, which can achieve a return of 9.56%. Almahdi and Yang ( 2017 ) establish a better portfolio with a combination of RNN and RL. The result shows that the proposed trading system respond to transaction cost effects efficiently and outperform hedge fund benchmarks consistently.

Macroeconomic prediction

Sevim et al. ( 2014 ) develops a model with a back-propagation learning algorithm to predict the financial crises up to a year before it happened. This model contains three-layer perceptrons (i.e., MLP) and can achieve an accuracy rate of approximately 95%, which is superior to DT and LR. Chatzis et al. ( 2018 ) examine multiple models such as classification tree, SVM, random forests, DNN, and extreme gradient boosting to predict the market crisis. The results show that crises encourage persistence. Furthermore, using DNN increases the classification accuracy that makes global warning systems more efficient.

Price prediction

For price prediction, Sehgal and Pandey ( 2015 ) review ANN, SVM, wavelet, GA, and hybrid systems. They separate the time-series models into stochastic models, AI-based models, and regression models to predict oil prices. They reveal that researchers prevalently use MLP for price prediction.

Data preprocessing and data input

Data preprocessing.

Data preprocessing is conducted to denoise before data training of DL. This section summarizes the methods of data preprocessing. Multiple preprocessing techniques discussed in Part 4 include the principal component analysis (Chong et al. 2017 ), SVM (Gunduz et al. 2017 ), autoencoder, and RBM (Chen et al. 2018b ). There are several additional techniques of feature selection as follows.

Relief: The relief algorithm (Zhu et al. 2018 ) is a simple approach to weigh the importance of the feature. Based on NN algorithms, relief repeats the process for n times and divides each final weight vector by n . Thus, the weight vectors are the relevance vectors, and features are selected if their relevance is larger than the threshold τ .

Wavelet transforms: Wavelet transforms are used to fix the noise feature of the financial time series before feeding into a DL network. It is a widely used technique for filtering and mining single-dimensional signals (Bao et al. 2017 ).

Chi-square: Chi-square selection is commonly used in ML to measure the dependence between a feature and a class label. The representative usage is by Gunduz et al. ( 2017 ).

Random forest: Random forest algorithm is a two-stage process that contains random feature selection and bagging. The representative usage is by Fischer and Krauss ( 2017 ).

Data inputs

Data inputs are an important criterion for judging whether a DL model is feasible for particular F&B domains. This section summarizes the method of data inputs that have been adopted in the literature. Based on our review, five types of input data in the F&B domain can be presented. Table  2 provides a detailed summary of the input variable in F&B domains.

History price: The daily exchange rate can be considered as history price. The price can be the high, low, open, and close price of the stock. Related articles include Bao et al. ( 2017 ), Chen et al. ( 2017 ), Singh and Srivastava ( 2017 ), and Yan and Ouyang ( 2017 ).

Technical index: Technical indexes include MA, exponential MA, MA convergence divergence, and relative strength index. Related articles include Bao et al. ( 2017 ), Chen et al. ( 2017 ), Gunduz et al. ( 2017 ), Sezer et al. ( 2017 ), Singh and Srivastava ( 2017 ), and Yan and Ouyang ( 2017 ).

Financial news: Financial news covers financial message, sentiment shock score, and sentiment trend score. Related articles include Feuerriegel and Prendinger ( 2016 ), Krausa and Feuerriegel ( 2017 ), Minh et al. ( 2017 ), and Song et al. ( 2017 ).

Financial report data: Financial report data can account for items in the financial balance sheet or the financial report data (e.g., return on equity, return on assets, price to earnings ratio, and debt to equity ratio). Zhang and Maringer ( 2015 ) is a representative study on the subject.

Macroeconomic data: This kind of data includes macroeconomic variables. It may affect elements of the financial market, such as exchange rate, interest rate, overnight interest rate, and gross foreign exchange reserves of the central bank. Representative articles include Bao et al. ( 2017 ), Kim and Won ( 2018 ), and Sevim et al. ( 2014 ).

Stochastic data: Chakraborty ( 2019 ) provides a representative implementation.

Evaluation rules

It is critical to judge whether an adopted DL model works well in a particular financial domain. We, thus, need to consider evaluation systems of criteria for gauging the performance of a DL model. This section summarizes the evaluation rules of F&B-oriented DL models. Based on our review, three evaluation rules dominate: the error term, the accuracy index, and the financial index. Table  3 provides a detailed summary. The evaluation rules can be boiled down to the following categories.

Error term: Suppose Y t  +  i and F t  +  i are the real data and the prediction data, respectively, where m is the total number. The following is a summary of the functional formula commonly employed for evaluating DL models.

Mean Absolute Error (MAE): \( {\sum}_{i=1}^m\frac{\left|{Y}_{t+i}-{F}_{t+i}\right|}{m} \) ;

Mean Absolute Percent Error (MAPE): \( \frac{100}{m}{\sum}_{i=1}^m\frac{\left|{Y}_{t+i}-{F}_{t+i}\right|}{Y_{t+i}} \) ;

Mean Squared Error (MSE): \( {\sum}_{i=1}^m\frac{{\left({Y}_{t+i}-{F}_{t+i}\right)}^2}{m} \) ;

Root Mean Squared Error (RMSE): \( \sqrt{\sum_{i=1}^m\frac{{\left({Y}_{t+i}-{F}_{t+i}\right)}^2}{m}} \) ;

Normalized Mean Square Error (NMSE): \( \frac{1}{m}\frac{\sum {\left({Y}_{t+i}-{F}_{t+i}\right)}^2}{\mathit{\operatorname{var}}\left({Y}_{t+i}\right)} \) .

Accuracy index: According to Matsubara et al. ( 2018 ), we use TP, TN, FP, and FN to represent the number of true positives, true negatives, false positives, and false negatives, respectively, in a confusion matrix for classification evaluation. Based on our review, we summarize the accuracy indexes as follows.

Directional Predictive Accuracy (DPA): \( \frac{1}{N}{\sum}_{t=1}^N{D}_t \) , if ( Y t  + 1  −  Y t ) × ( F t  + 1  −  Y t ) ≥ 0, D t  = 1, otherwise, D t  = 0;

Actual Correlation Coefficient (ACC): \( \frac{TP+ TN}{TP+ FP+ FN+ TN} \) ;

Matthews Correlation Coefficient (MCC): \( \frac{TP\times TN- FP\times FN}{\sqrt{\left( TP+ FP\right)\left( TP+ FN\right)\left( TN+ FP\right)\left( TN+ FN\right)}} \) .

Financial index: Financial indexes involve total return, Sharp ratio, abnormal return, annualized return, annualized number of transaction, percentage of success, average profit percent per transaction, average transaction length, maximum profit percentage in the transaction, maximum loss percentage in the transaction, maximum capital, and minimum capital.

For the prediction by regressing the numeric dependent variables (e.g., exchange rate prediction or stock market prediction), evaluation rules are mostly error terms. For the prediction by classification in the category data (e.g., direction prediction on oil price), the accuracy indexes are widely conducted. For stock trading and portfolio management, financial indexes are the final evaluation rules.

General comparisons of DL models

This study identifies the most efficient DL model in each identified F&B domain. Table  4 illustrates our comparisons of the error terms in the pool of reviewed articles. Note that “A > B” means that the performance of model A is better than that of model B. “A + B” indicates the hybridization of multiple DL models.

At this point, we have summarized three methods of data processing in DL models against seven specified F&B domains, including data preprocessing, data inputs, and evaluation rules. Apart from the technical level of DL, we find the following:

NN has advantages in handling cross-sectional data;

RNN and LSTM are more feasible in handling time series data;

CNN has advantages in handling the data with multicollinearity.

Apart from application domains, we can induce the following viewpoints. Cross-sectional data usually appear in exchange rate prediction, price prediction, and macroeconomic prediction, for which NN could be the most feasible model. Time series data usually appear in stock market prediction, for which LSTM and RNN are the best options. Regarding stock trading, a feasible DL model requires the capabilities of decision and self-learning, for which RL can be the best. Moreover, CNN is more suitable for the multivariable environment of any F&B domains. As shown in the statistics of the Appendix , the frequency of using corresponding DL models corresponds to our analysis above. Selecting proper DL models according to the particular needs of financial analysis is usually challenging and crucial. This study provides several recommendations.

We summarize emerging DL models in F&B domains. Nevertheless, can these models refuse the efficient market hypothesis (EMH)? Footnote 5 According to the EMH, the financial market has its own discipline. There is no long-term technical tool that could outperform an efficient market. If so, using DL models may not be practical in long-term trading as it requires further experimental tests. However, why do most of the reviewed articles argue that their DL models of trading outperform the market returns? This argument has challenged the EMH. A possible explanation is that many DL algorithms are still challenging to apply in the real-world market. The DL models may raise trading opportunities to gain abnormal returns in the short-term. In the long run, however, many algorithms may lose their superiority, whereas EMH still works as more traders recognize the arbitrage gap offered by these DL models.

This section discusses three aspects that could affect the outcomes of DL models in finance.

Training and validation of data processing

The size of the training set.

The optimal way to improve the performance of models is by enhancing the size of the training data. Bootstrap can be used for data resampling, and generative adversarial network (GAN) can extend the data features. However, both can recognize numerical parts of features. Sometimes, the sample set is not diverse enough; thus, it loses its representativeness. Expanding the data size could make the model more unstable. The current literature reported diversified sizes of training sets. The requirements of data size in the training stage could vary by different F&B tasks.

The number of input factors

Input variables are independent variables. Based on our review, multi-factor models normally perform better than single-factor models in the case that the additional input factors are effective. In the time-series data model, long-term data have less prediction errors than that for a short period. The number of input factors depends on the employment of the DL structure and the specific environment of F&B tasks.

The quality of data

Several methods can be used to improve the data quality, including data cleaning (e.g., dealing with missing data), data normalization (e.g., taking the logarithm, calculating the changes of variables, and calculating the t -value of variables), feature selection (e.g., Chi-square test), and dimensionality reduction (e.g., PCA). Financial DL models require that the input variables should be interpretable in economics. When inputting the data, researchers should clarify the effective variables and noise. Several financial features, such as technical indexes, are likely to be created and added into the model.

Selection on structures of DL models

DL model selection should depend on problem domains and cases in finance. NN is suitable for processing cross-sectional data. LSTM and other RNNs are optimal choices for time-series data in prediction tasks. CNN can settle the multicollinearity issue through data compression. Latent variable models like GAN can be better for dimension reduction and clustering. RL is applicable in the cases with judgments like portfolio management and trading. The return levels and outcomes on RL can be affected significantly by environment (observation) definitions, situation probability transfer matrix, and actions.

The setting of objective functions and the convexity of evaluation rules

Objective function selection affects training processes and expected outcomes. For predictions on stock price, low MAE merely reflects the effectiveness of applied models in training; however, it may fail in predicting future directions. Therefore, it is vital for additional evaluation rules for F&B. Moreover, it can be more convenient to resolve the objective functions if they are convex.

The influence of overfitting (underfitting)

Overfitting (underfitting) commonly happens in using DL models, which is clearly unfavorable. A generated model performs perfectly in one case but usually cannot replicate good performance with the same model and identical coefficients. To solve this problem, we have to trade off the bias against variances. Bias posits that researchers prefer to keep it small to illustrate the superiority of their models. Generally, a deeper (i.e., more layered) NN model or neurons can reduce errors. However, it is more time-consuming and could reduce the feasibility of applied DL models.

One solution is to establish validation sets and testing sets for deciding the numbers of layers and neurons. After setting optimal coefficients in the validation set (Chong et al. 2017 ; Sevim et al. 2014 ), the result in the testing sets reveals the level of errors that could mitigate the effect of overfitting. One can input more samples of financial data to check the stability of the model’s performance. This method is known as the early stopping. It stops training more layers in the network once the testing result has achieved an optimal level.

Moreover, regularization is another approach to conquer the overfitting. Chong et al. ( 2017 ) introduces a constant term for the objective function and eventually reduces the variates of the result. Dropout is also a simple method to address overfitting. It reduces the dimensions and layers of the network (Minh et al. 2017 ; Wang et al. 2019 ). Finally, the data cleaning process (Baek and Kim 2018 ; Bao et al. 2017 ), to an extent, could mitigate the impact of overfitting.

Financial models

The sustainability of the model.

According to our reviews, the literature focus on evaluating the performance of historical data. However, crucial problems remain. Given that prediction is always complicated, the problem of how to justify the robustness of the used DL models in the future remains. More so, whether a DL model could survive in dynamic environments must be considered.

The following solutions could be considered. First, one can divide the data into two groups according to the time range; performance can subsequently be checked (e.g., using the data for the first 3 years to predict the performance of the fourth year). Second, the feature selection can be used in the data preprocessing, which could improve the sustainability of models in the long run. Third, stochastic data can be generated for each input variable by fixing them with a confidence interval, after which a simulation to examine the robustness of all possible future situations is conducted.

The popularity of the model

Whether a DL model is effective for trading is subject to the popularity of the model in the financial market. If traders in the same market conduct an identical model with limited information, they may run identical results and adopt the same trading strategy accordingly. Thus, they may lose money because their strategy could sell at a lower price after buying at a higher.

Conclusion and future works

Concluding remarks.

This paper provides a comprehensive survey of the literature on the application of DL in F&B. We carefully review 40 articles refined from a collection of 150 articles published between 2014 and 2018. The review and refinement are based on a scientific selection of academic databases. This paper first recognizes seven core F&B domains and establish the relationships between the domains and their frequently-used DL models. We review the details of each article under our framework. Importantly, we analyze the optimal models toward particular domains and make recommendations according to the feasibility of various DL models. Thus, we summarize three important aspects, including data preprocessing, data inputs, and evaluation rules. We further analyze the unfavorable impacts of overfitting and sustainability when applying DL models and provide several possible solutions. This study contributes to the literature by presenting a valuable accumulation of knowledge on related studies and providing useful recommendations for financial analysts and researchers.

Future works

Future studies can be conducted from the DL technical and F&B application perspectives. Regarding the perspective of DL techniques, training DL model for F&B is usually time-consuming. However, effective training could greatly enhance accuracy by reducing errors. Most of the functions can be simulated with considerable weights in complicated networks. First, one of the future works should focus on data preprocessing, such as data cleaning, to reduce the negative effect of data noise in the subsequent stage of data training. Second, further studies on how to construct layers of networks in the DL model are required, particularly when considering a reduction of the unfavorable effects of overfitting and underfitting. According to our review, the comparisons between the discussed DL models do not hinge on an identical source of input data, which renders these comparisons useless. Third, more testing regarding F&B-oriented DL models would be beneficial.

In addition to the penetration of DL techniques in F&B fields, more structures of DL models should be explored. From the perspective of F&B applications, the following problems need further research to investigate desirable solutions. In the case of financial planning, can a DL algorithm transfer asset recommendations to clients according to risk preferences? In the case of corporate finance, how can a DL algorithm benefit capital structure management and, thus, maximize the values of corporations? How can managers utilize DL technical tools to gauge the investment environment and financial data? How can they use such tools to optimize cash balances and cash inflow and outflow? Until recently, DL models like RL and generative adversarial networks are rarely used. More investigations on constructing DL structures for F&B regarding preferences would be beneficial. Finally, the developments of professional F&B software and system platforms that implement DL techniques are highly desirable.

Availability of data and materials

Not applicable.

In the model, NSGA stands for non-dominated sorting genetic algorithm.

A combination of Wavelet transforms (WT) and long-short term memory (LSTM) is called WLSTM in Bao et al. ( 2017 ).

Q-learning is a model-free reinforcement learning algorithm.

Buy-and-hold is a passive investment strategy in which an investor buys stocks (or ETFs) and holds them for a long period regardless of fluctuations in the market.

EMH was developed from a Ph.D. dissertation by economist Eugene Fama in the 1960s. It says that at any given time, stock prices reflect all available information and trade at exactly their fair value at all times. It is impossible to consistently choose stocks that will beat the returns of the overall stock market. Therefore, this hypothesis implies that the pursuit of market-beating performance is more about chance than it is about researching and selecting the right stocks.

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Acknowledgments

The constructive comments of the editor and three anonymous reviewers on an earlier version of this paper are greatly appreciated. The authors are indebted to seminar participants at 2019 China Accounting and Financial Innovation Form at Zhuhai for insightful discussions. The corresponding author thanks the financial supports from BNU-HKBU United International College Research Grant under Grant R202026.

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JH carried out the collections and analyses of the literature, participated in the design of this study and preliminarily drafted the manuscript. JC initiated the idea and research project, identified the research gap and motivations, carried out the collections and analyses of the literature, participated in the design of this study, helped to draft the manuscript and proofread the manuscript. SC participated in the design of the study and the analysis of the literature, helped to draft the manuscript and proofread the manuscript. The authors read and approved the final manuscript.

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Part A. Summary of publications in DL and F&B domains

Part b. detailed structure of standard rnn.

The abstract structure of RNN for a sequence cross over time can be extended, as shown in Fig. 7 in Appendix , which presents the inputs as X , the outputs as Y , the weights as w , and the Tanh functions.

The detailed structure of RNN

Part C. List of abbreviations

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Huang, J., Chai, J. & Cho, S. Deep learning in finance and banking: A literature review and classification. Front. Bus. Res. China 14 , 13 (2020). https://doi.org/10.1186/s11782-020-00082-6

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• The impact of blockchain on global payment systems.
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• The impact of currency fluctuations on international trade.
• Strategies for managing foreign exchange risk in multinational corporations.
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• Cross-border mergers and acquisitions: challenges and opportunities.
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• The future of international financial regulation in a post-Brexit Europe.
• The impact of emerging markets on global finance.
• Foreign direct investment trends and their economic impacts.
• The role of sovereign wealth funds in international finance.
• The challenges of implementing international accounting standards.
• The impact of international remittances on developing economies.
• The role of digital currencies in reshaping international finance.
• The effects of protectionist trade policies on global finance.
• International financial market trends and their implications for investors.
• The role of expatriate remittances in national economic stability.
• The impact of international trade agreements on financial services.
• Global risk management strategies in the finance sector.
• The role of green finance in promoting sustainable development.
• The impact of international environmental policies on financial strategies.
• The future of global banking in the context of rising nationalism.
• The role of international finance in disaster recovery and resilience.
• The influence of international finance on poverty reduction strategies.
• Strategies for financing international healthcare initiatives.
• The evolving role of Islamic finance in the global market.
• The impact of fintech on international banking and finance.
• Challenges in financing international infrastructure projects.
• The role of international finance in climate change mitigation.
• Ethical considerations in international finance: fostering global financial integrity.

14. Investment Thesis Topics

• The role of ESG criteria in investment decision-making.
• The impact of technological innovation on investment strategies.
• Market reaction to unexpected global events and its effect on investment portfolios.
• Behavioral biases in investment: a study of market anomalies.
• The future of real estate investment in a fluctuating economic landscape.
• The role of quantitative analysis in portfolio management.
• The impact of demographic changes on investment trends.
• Strategies for sustainable and responsible investing.
• The influence of regulatory changes on investment strategies.
• The role of artificial intelligence in enhancing investment decisions.
• Cryptocurrency investment: risks and opportunities.
• The impact of global trade tensions on investment strategies.
• Investment strategies for low interest rate environments.
• The role of crowdfunding in the investment landscape.
• The impact of social media on investor sentiment and stock prices.
• The effectiveness of passive versus active investment strategies.
• The role of venture capital in driving technological innovation.
• The future of bond markets in a changing economic context.
• The role of international investments in diversifying portfolios.
• Impact of inflation expectations on investment decisions.
• The evolving landscape of commodity investments.
• Investment opportunities in emerging markets.
• The impact of fiscal policy changes on investment strategies.
• The role of hedge funds in the current financial market.
• The influence of central bank policies on investment strategies.
• The role of pension funds in the global investment market.
• Ethical investing: balancing profit and principles.
• The future of investments in renewable energy.
• The impact of political stability on foreign investments.
• The role of technology in asset management and valuation.

15. Management Accounting Thesis Topics

• The role of management accounting in strategic decision-making.
• Cost management strategies in the era of global supply chain disruptions.
• The impact of digital transformation on management accounting practices.
• The role of management accounting in environmental sustainability.
• Performance measurement and management in diverse organizational settings.
• Risk management strategies in management accounting.
• The evolving role of management accountants in corporate governance.
• The impact of regulatory changes on management accounting.
• The role of management accounting in healthcare cost containment.
• The influence of management accounting on operational efficiency.
• Management accounting practices in nonprofit organizations.
• The role of cost analysis in pricing strategies.
• The impact of technological advancements on budgeting and forecasting.
• The effectiveness of management accounting tools in project management.
• The role of management accounting in mergers and acquisitions.
• The impact of cultural differences on management accounting systems.
• The role of management accounting in enhancing business resilience.
• The influence of management accounting on business model innovation.
• Management accounting in the digital economy: challenges and opportunities.
• Strategic cost management for competitive advantage.
• The role of management accounting in supply chain optimization.
• The future of management accounting in the context of AI and automation.
• The impact of financial technology on management accounting.
• The role of management accounting in crisis management and recovery.
• Performance metrics and their impact on organizational success.
• The role of management accounting in supporting sustainable practices.
• The impact of global economic conditions on management accounting.
• The role of predictive analytics in management accounting.
• The effectiveness of internal controls in management accounting.
• The role of management accounting in international business expansion.

16. Personal Finance Thesis Topics

• The impact of financial technology on personal savings strategies.
• Behavioral insights into personal debt management.
• The role of personal finance education in shaping financial literacy.
• The influence of economic downturns on personal investment choices.
• Retirement planning: trends and strategies in the current economic climate.
• The effectiveness of digital tools in personal budgeting and financial planning.
• Analyzing the gender gap in personal finance management.
• The impact of cultural factors on personal saving and spending habits.
• Personal finance challenges for the gig economy workers.
• The role of personal finance in achieving long-term financial security.
• Cryptocurrency as a personal investment: risks and rewards.
• The impact of peer-to-peer lending platforms on personal finance.
• The influence of social media on personal financial decisions.
• Ethical considerations in personal financial advice.
• The evolution of consumer credit markets and its impact on personal finance.
• Strategies for managing personal financial risk.
• The role of emergency funds in personal financial planning.
• The impact of student loans on financial planning for millennials.
• Personal finance strategies for different life stages.
• The effect of inflation on personal savings and investment strategies.
• The future of personal finance in the age of AI and automation.
• The role of insurance in personal financial planning.
• The impact of tax laws changes on personal finance strategies.
• The psychology of spending: understanding consumer behavior.
• Personal financial planning for expatriates: strategies and challenges.
• The role of estate planning in personal finance.
• Impact of healthcare costs on personal financial stability.
• The role of financial advisors in the era of self-directed financial planning.
• Financial planning for sustainable living: integrating environmental considerations.
• The challenges and opportunities in personal wealth building.

17. Public Finance Thesis Topics

• The role of public finance in addressing income inequality.
• Fiscal policies for sustainable economic growth.
• The impact of taxation on small businesses.
• Public finance management in times of economic crisis.
• The role of government spending in stimulating economic development.
• Strategies for managing national debt.
• The effectiveness of public welfare programs.
• The challenges of healthcare financing in public sectors.
• The impact of international aid on public finance.
• Public finance strategies for environmental conservation.
• The role of public finance in urban development.
• Tax evasion and its implications for public finance.
• The impact of public finance on education quality and access.
• Financing public infrastructure: challenges and solutions.
• The role of public finance in disaster management.
• The effectiveness of fiscal decentralization.
• Public finance reforms and their impact on service delivery.
• The challenges of pension financing in the public sector.
• The impact of political stability on public financial management.
• Public-private partnerships: financial implications and models.
• The role of transparency in public finance.
• The impact of corruption on public financial management.
• Financing renewable energy projects through public funds.
• The role of public finance in health care reform.
• The effectiveness of government subsidies in promoting economic sectors.
• The challenges of financing sustainable transportation systems.
• The impact of demographic changes on public finance.
• The role of digital technologies in improving public finance management.
• The global trends in public finance and their implications for domestic policy.
• The impact of climate change on public financial strategies.

18. Quantitative Finance Thesis Topics

• The application of machine learning algorithms in predicting stock market trends.
• The role of quantitative methods in risk management.
• Developing advanced models for credit risk assessment.
• The impact of high-frequency trading on market stability.
• The use of big data analytics in portfolio management.
• Quantitative approaches to asset pricing in volatile markets.
• The effectiveness of quantitative strategies in hedge funds.
• The role of algorithmic trading in enhancing market efficiency.
• Quantitative models for predicting bond market movements.
• The impact of quantitative finance on regulatory compliance.
• The application of blockchain technology in quantitative finance.
• The challenges of quantitative finance in cryptocurrency markets.
• The integration of environmental, social, and governance (ESG) factors in quantitative analysis.
• The role of quantitative finance in private equity valuations.
• Developing quantitative approaches for derivatives pricing.
• The impact of quantitative finance techniques on financial advising.
• Quantitative methods for assessing market liquidity.
• The role of sentiment analysis in quantitative finance.
• Quantitative trading strategies for commodities markets.
• The application of game theory in financial strategy.
• Quantitative finance and its role in insurance underwriting.
• The impact of geopolitical events on quantitative financial models.
• The use of quantitative finance in forecasting economic downturns.
• Machine learning models for real estate investment analysis.
• Quantitative finance techniques in sports betting markets.
• The impact of artificial intelligence on financial market predictions.
• Quantitative methods for managing currency exchange risks.
• The role of quantitative finance in managing pension fund assets.
• The effectiveness of quantitative models in emerging financial markets.
• The future of quantitative finance in a globally interconnected economy.

19. Risk Management Thesis Topics

• The role of risk management in enhancing corporate resilience.
• Cybersecurity risks in financial institutions: management strategies.
• The impact of climate change on risk management in insurance.
• Risk management techniques in the fintech sector.
• The effectiveness of enterprise risk management (ERM) frameworks.
• Risk management in global supply chains.
• The role of risk management in sustainable business practices.
• Financial risks associated with political instability.
• The challenges of operational risk management in complex organizations.
• Risk management strategies for digital transformation projects.
• The impact of regulatory changes on risk management practices.
• Risk assessment techniques for investment in volatile markets.
• The role of data analytics in risk identification and mitigation.
• Risk management considerations in mergers and acquisitions.
• The impact of reputation risk on corporate strategy.
• Risk management in the healthcare industry.
• The challenges of risk management in the energy sector.
• The role of risk management in nonprofit organizations.
• Implementing risk management in public sector entities.
• The future of risk management in the context of AI advancements.
• Credit risk management in banking post-global financial crisis.
• Risk management strategies for emerging technologies.
• The role of psychological factors in risk management decision-making.
• Legal risks in international business operations.
• The impact of cultural differences on risk management strategies.
• Environmental risk management and corporate responsibility.
• Risk management techniques for protecting intellectual property.
• The role of insurance in comprehensive risk management.
• The challenges of liquidity risk management in financial markets.
• The future of risk management education and training.

20. Taxation Thesis Topics

• The impact of digital economy on global taxation frameworks.
• Tax policy as a tool for economic recovery post-pandemic.
• The effectiveness of tax incentives in promoting renewable energy investments.
• The role of taxation in addressing wealth inequality.
• International tax competition and its implications for global economic stability.
• The challenges of implementing value-added tax (VAT) in developing countries.
• Tax evasion and its impact on national economies.
• The role of tax policy in encouraging corporate social responsibility.
• The impact of tax reforms on small and medium-sized enterprises.
• Comparative analysis of progressive versus flat tax systems.
• The effectiveness of digital services taxes in the global economy.
• The role of taxation in sustainable development goals.
• Taxation strategies for digital currencies and blockchain transactions.
• The impact of tax policies on consumer behavior.
• The role of taxation in healthcare financing.
• Tax compliance challenges in the gig economy.
• The implications of tax havens on international relations.
• The role of automated systems in improving tax collection efficiency.
• Taxation and its impact on foreign direct investment flows.
• The future of estate taxes and their role in wealth distribution.
• Taxation of e-commerce transactions.
• The impact of international tax treaties on cross-border investments.
• The role of taxation in the informal economy.
• The challenges of carbon taxes in combating climate change.
• The role of tax audits in enhancing fiscal transparency.
• The impact of tax policies on retirement planning.
• Taxation challenges in the pharmaceutical industry.
• The role of taxation in funding public education.
• The impact of taxation on agricultural development.
• The future of consumption taxes in an increasingly digital world.

This comprehensive list of accounting and finance thesis topics has been curated to reflect the latest challenges and emerging trends within the field. Whether you are exploring traditional areas like taxation and corporate finance or delving into the evolving realms of fintech and international finance, these topics are designed to provide a robust foundation for your thesis research. Each category is intended to spark innovative thinking and encourage a deep exploration of issues that are pivotal to the current and future landscape of accounting and finance. By selecting a topic from this extensive collection, students can ensure their research is relevant, timely, and contributes meaningfully to their academic and professional growth in the field of accounting and finance.

The Range of Accounting and Finance Thesis Topics

Accounting and finance stand as critical pillars in the modern economic and corporate world, guiding everything from daily business operations to global financial strategies. The study of these disciplines is not just about learning to balance books or manage corporate assets; it’s about understanding the forces that drive economic activities and shape financial landscapes. Research in accounting and finance is paramount as it provides the empirical evidence needed to develop robust financial models, innovative management practices, and effective regulatory policies. The relevance of accounting and finance thesis topics is thus foundational to nurturing informed, adept professionals capable of navigating the complexities of financial markets and addressing the challenges of economic flux.

Current Issues in Accounting and Finance

• Globalization and Regulatory Complexity : As businesses operate across borders, the complexity of regulatory compliance increases. Researchers are tasked with exploring the implications of global regulatory frameworks and their synchronization, or lack thereof, which affects multinational corporations and global financial stability.
• Technological Disruptions : The rapid integration of technologies such as blockchain, AI, and machine learning in financial operations presents both opportunities and challenges. Studies focus on their impacts on financial privacy, security, and new types of financial crime, as well as their potential to improve efficiency and transparency.
• Ethical and Sustainability Challenges : With rising concerns over corporate responsibility and sustainable development, research is increasingly focusing on how financial practices can be aligned with ethical standards and sustainability goals. This includes studies on green financing, ethical investing, and the financial implications of corporate sustainability initiatives.

Recent Trends in Accounting and Finance

• Automation and Data Analytics : The adoption of advanced data analytics and automation tools is transforming traditional accounting tasks. Research topics explore the impact of these technologies on workflow efficiencies, data accuracy, and strategic decision-making within financial departments.
• Sustainable Finance : As the demand for environmentally and socially responsible investment options grows, there is an increasing focus on how financial markets can support ESG principles. Researchers examine the integration of sustainability into financial analysis and decision-making processes.
• Fintech Innovations : The emergence of fintech and its components like mobile payments, peer-to-peer lending, and cryptocurrencies are reshaping the financial services industry. Theses may focus on the regulatory challenges, market dynamics, and consumer behavior influenced by these innovations.

Future Directions in Accounting and Finance

• Digital Transformation : Future research will likely delve deeper into the consequences and potentials of continued digital transformation in finance, such as the widespread adoption of internet of things (IoT) technologies and further advancements in AI for automated trading and personal finance management.
• Predictive Finance and AI : With AI’s increasing capability to predict financial outcomes, future topics could include the development of AI-driven models for credit scoring, risk management, and investment strategies, emphasizing their accuracy, ethical considerations, and regulatory needs.
• Sustainability and Finance : An emerging research frontier is the intersection of finance with global sustainability challenges. Potential topics include the financing of climate change initiatives, the role of financial institutions in promoting sustainable practices, and the creation of innovative financial products that support sustainable economic growth.

The breadth of accounting and finance thesis topics is indicative of the field’s extensive scope and its significant impact on societal and economic frameworks. Continued research is essential for advancing theoretical foundations and developing practical applications that address both current challenges and future opportunities. This ongoing academic inquiry is crucial for fostering a financial landscape that is not only robust and dynamic but also ethical and sustainable, ensuring that the field of accounting and finance remains at the forefront of economic innovation and societal advancement.

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Top 140 Finance Research Paper Topics

Why finance topics? The search for interesting finance topics is a constant one. Of course, this is understandable because knowledge of hot topics in finance puts you ahead of the game. Students or researchers who major in business will, at one point or the other in their careers, make presentations, and submit research papers, essays,or help with dissertation or projects. With the headache of writing these papers aside, the challenge of picking finance topics always looms around. We have, therefore, carried out extensive research to present you with these 30 finance topics that will make your paper worth reading! When in doubt, this list of finance topics will surely come in handy to shed some light on that darkness!

Finding Excellent Topics in Finance

We offer you 30 researchable topics in finance. But why should we only catch fish for you if we can teach you how to fish too? The need to find unique topics in finance is on the increase. Here are some excellent tips that will help you choose appropriate finance topics:

• Find out unanswered questions from previous research works or develop on areas that require additional study.
• Read several theses to form ideas.
• Check economics topics . They may be more general but you can narrow down some of them.
• Search online for related topics that are unique, or make them unique to suit your purpose.
• Discuss your chosen topic with other students or people who have experience writing dissertations asking for their input.

Research Topics In Finance

In financial research, unique topics are pivotal to the overall success of the study. The reason for this is simple. Now put yourself in the shoes of professors who have read hundreds of theses and essays. They already know common topics that students like to write or work on. A hot research topic in finance will surely catch the attention of your professor and will likely earn you better grades. Writing finance research papers becomes much easier when you have superb finance research topics.

Here is a finance research topics list that will spark people’s interest in your research work and make your finance research paper worth reading! Ready for these research topics in finance? Read on!

• Merger and Acquisition: An Analytical Study of the Benefits and Set-backs.
• Capital Asset Pricing Model: Possible Solutions to its Inadequacies.
• Global Financial Crisis: A Critical Study of the Role of Auditors and Stakeholders.
• The Impact of Manipulating the Commodity Market on Future Commerce.
• Continuous-time Models: An exhaustive Comparative Analysis of its Application in Divers financial Environments.
• How Speculations Undermine the Stability of Banking in Asian Markets.
• Branding: Its Effect on Consumer Behavior.
• An effective strategy for managing inventory and controlling your budget.
• An analytical report on the various investments in tax-saving products.
• Using a systematic investment strategy to build stability for retail investments.
• How income tax is planned and implemented in India’s economy.
• A detailed analysis of how the Indian banking system operates.
• How does multi-level marketing work in different economies around the world?
• A detailed report on electronic payment and how it can be improved.
• A case study regarding senior citizen investment portfolios.
• Are there potential risks and rewards when comparing savings to investments?
• Is ratio analysis an effective component of financial statement analysis?
• How the Indian economy functions with its current banking operations.

Finance Research Topics For MBA

Here are some great finance research topics you can use toward your MBA. It’s sure to intrigue your professor and get you to look at finance from a different perspective.

• Investment analysis of a company of your choice.
• A detailed report on working capital management.
• Financial plans and considerations for saving taxes and salaried employees.
• A detailed analysis of the cost and costing models of the company of your choice.
• The awareness of investments in financial assets and equity trading preference with financial intermediaries.
• The perspective of investors and their involvement with life insurance investments.
• A detailed analysis of the perception of mutual fund investors.
• The comparative study between UIL and the traditional products.
• A detailed report on how the ABC company manages cash.

Corporate Risk Management Topics

These are some key topics you can use relating to corporate risk management.

• A detailed report on the fundamentals of corporate risk management.
• The analytical concepts relating to effective corporate and financial management within a company.
• How does corporate risk management affect the financial market and its products?
• What are risk models and how are they evaluated?
• How is market risk effectively measured and managed in today’s economy?
• How can a company be vigilant of potential credit risks they can face?
• What are the differences between operational and integrated risks in the corporate world?
• Is liquidity an effective strategy to lower financial risk to a company?
• How risk management can connect with and benefit investment management.
• The current issues that are affecting the modern marketplace and the financial risks they bring.

Healthcare Finance Research Topics

These are some key topics you can use relating to healthcare finance research.

• Is it better for the government to pay for an individual’s healthcare?
• The origins of healthcare finance.
• An analysis of Canada and their healthcare finance system.
• Is healthcare financing a right or a privilege?
• The changing policies of healthcare in the U.S.
• Can healthcare be improved in first-world countries?
• Can the healthcare system be improved or remade?
• How much influence does the government have on healthcare in a country?
• The impact of growing global health spending.
• Is free healthcare achievable worldwide?

Corporate Finance Topics

Corporate finance deals with processes such as financing, structuring of capital, and making investment decisions. It seeks to maximize shareholder value by implementing diverse strategies in long and short-term financial planning.

Corporate finance research topics broadly cover areas like tools for risk management, trend research in advanced finance, physical and electronic techniques in securities markets, research trends in advance finance, investment analysis, and management of government debt. The following corporate finance topics will surely minimize any risk of mistakes!

• Using the Bootstrapped Interest Rates to Price Corporate Debt Capital Market Instruments.
• Corporate Organizations: The Impact of Audit Independence on Accountability and Transparency.
• Buybacks: A Critical Analysis of how Firms can Buy Back at Optimal Prices.
• Merge and Acquisitions: Reasons why Firms still Overpay for bad Acquisitions.
• Corporate Finance: Ethical Concerns and Possible Solutions.
• Understanding the investment patterns relative to smaller and medium-capitalization businesses.
• A detailed analysis of the different streams of investment relating to mutual funds.
• Equity investors and how they manage their portfolios and perception of potential risks.
• How does investor preference operate in the commodity market in Karvy Stock Broking Limited?
• An analysis of the performance of mutual funds in the public and private sectors.
• Understanding how Videcon manages its working capital.
• The Visa Port trust and how it conducts ratio analysis.
• How the gold monetization scheme has affected the Indian economy and banking operations.
• How does SWIFT work and what are the potential risks and rewards?
• A detailed analysis of the FMC and SEBI merger.

Business Finance Topics

Every decision made in a business has financial implications. It is, therefore, essential that business people have a fundamental understanding of finance. To show your knowledge, you must be able to write articles on finance topics in areas such as financial analysis, valuation, management, etc. Here are some juicy business finance topics!

• Application of Business Finance: Its importance to the Business Sector.
• The Importance of Business Finance in the Establishment of Business Enterprises.
• Modernization of Business: Roles of Business Finance in Business Modernization.
• A detailed study on providing financial aid to self-help groups and projects.
• Is tax an effective incentive for selling life insurance to the public?
• Understanding how the performance of mutual funds can change within the private and public sectors.
• Is there a preference for different investment options from financial classes?
• A detailed analysis of retail investors and their preferences and choices.
• A study on investors and their perspective on investing in private insurance companies.
• How analyzing financial statements can assess a business’s performance.
• Increasing the accountability of corporate entities.
• Ethical concerns connected to business finance and how they can be managed.
• The level of tax paid by small to medium businesses.

International Finance Topics

As the world is now a global village, business transactions occur all around the world. No more are we limited to local trade, and this is why the study of international is essential and relevant. Here are some international finance topics that will suit your research purpose!

• Stock Exchange: How Important are the Functions of a Bank Office?
• Global Economic Crises: Possible Precautions to prevent Global Financial crisis.
• Bond Rating: the Effect of Changes on the Price of Stocks.
• How the Banking Industry can Decrease the Impact of Financial Crisis.
• Is it possible for a country to budget funds for healthcare for the homeless?
• The negative impact of private healthcare payments on impoverished communities.
• What sectors in healthcare require more funding at the moment?
• The dilemma of unequal access to adequate healthcare in third world countries.
• Can cancer treatment be more inexpensive to the public?
• The problem with the high pricing of medication in the U.S.
• Is there a better way to establish healthcare financing in the U.S?
• What are the benefits of healthcare finance systems in Canada and the UK?
• How can third-world countries improve their healthcare systems without hurting their economy?
• Is financing research a priority in healthcare and medicine?
• Does free healthcare hurt the tax system of a country?
• Why is free and privatized healthcare present in different economies?
• How does government funding affect healthcare finance systems?
• How do patient management systems work?
• Where does affordable healthcare financing fit in growing economies?
• The economic impact of COVID-19 in various countries.
• The healthcare policies of the Serbian government.

Finance Research Paper Ideas

Writing a research paper requires an independent investigation of a chosen subject and the analysis of the remarkable outcomes of that research. A finance researcher will, therefore, need to have enough finance research paper topics from which to choose at his fingertip. Carefully selecting a finance thesis topic out of the many finance research papers topics will require some skill. Here are some exciting finance paper topics!

• Behavioral Finance versus Traditional Finance: Differences and Similarities.
• Budgetary Controls: The Impact of this Control on Organizational performance.
• Electronic Banking: The Effect of e-Banking on Consumer Satisfaction.
• Credit and Bad Debts: Novel Techniques of management in commercial Banks.
• Loan Default: A Critical Assessment of the Impact of Loan Defaults on the Profitability of Banks.
• A detailed analysis of the best risk management methods used in the manufacturing industry.
• Identifying and measuring financial risks in a derivative marketplace.
• Exploring the potential risks that can occur in the banking sector and how they can be avoided.
• The risks that online transactions bring.
• What are the methods used to ensure quantitive risk management is achieved?
• A better understanding of policy evaluation and asset management.
• What makes traditional finance so different from behavioral?
• The significance of budgetary control in a corporate organization.
• How do loans benefit the profitability of banks?
• How do commercial banks assist their clients that are in bad debt?
• The various considerations we need to be aware of before making investment decisions.

Personal Finance Topics

Personal finance covers the aspects of managing your money, including saving and investing. It comprises aspects such as investments, retirement planning, budgeting, estate planning, mortgages, banking, tax, and insurance. Researching in this area will surely be of direct impact on the quality of living. Here are some great personal finance topics that are eager to have you work on them!

• Evaluation of Possible Methods of Saving while on a Budget.
• The Effect of Increase in Interest Rate and Inflation on Personal Finance.
• Benefits of Working from Home to both Employers and Employees.
• Will dental services be considered an essential medical service soon?
• Is affordable or free healthcare a right that everyone should be entitled to?
• The best ways to save money while on a tight budget.
• What happens to personal finance when inflation and interest rates rise?
• The financial benefits of working from home.
• Does innovations in personal finance act as an incentive for households to take risks?
• A detailed analysis of credit scores.
• The importance of credit and vehicle loans.
• A detailed analysis of employee benefits and what should be considered.
• The effect of tax on making certain financial decisions.
• The best ways to manage your credit.
• The difficulties that come with mobile banking.

Finance Topics For Presentation

Sometimes, you may need to present a topic in a seminar. The idea is that you can whet the appetite of your audience with the highlights of your subject matter. Choosing these finance seminar topics requires a slightly different approach in that you must be thoroughly familiar with that topic before giving the presentation. Interesting and easy-to-grasp finance topics are, therefore, necessary for presentations. Here are some topic examples that fit perfectly into this category.

• Analysis of the Year-over-Year Trend.
• Maximizing Pension Using Life Insurance.
• The Architecture of the Global Financial System.
• Non-communicable diseases and the burden they have on economies.
• Is there a connection between a country’s population and its healthcare budget?
• The spending capability of medical innovations in a third-world economy.
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Emerging Trends in Banking and Finance

3rd International Conference on Banking and Finance Perspectives

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• Nesrin Ozatac 0 ,
• Korhan K. Gökmenoglu 1

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Department of Banking and Finance, Eastern Mediterranean University, Famagusta, Cyprus

• Presents current developments in the fields of banking and finance from an international perspective
• Provides various perspectives on the global financial challenges and strategies for mitigating impacts on the banking system and financial markets
• Discusses the relationship between stock market development, banking sector development, foreign direct investment, economic freedom, and economic growth in diverse economies
• Dissects the complexities of financial institutions in Islamic countries

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This volume presents current developments in the fields of banking and finance from an international perspective. Featuring contributions from the 3rd International Conference on Banking and Finance Perspectives (ICBFP), this volume serves as a valuable forum for discussing current issues and trends in the banking and financial sectors, especially  in light of the global economic challenges triggered by financial institutions. Using the latest theoretical models, new perspectives are brought to topics such as e-finance and e-banking, Islamic banking, capital flight, bank efficiency, risk assessment, bankruptcy, investment diversification, and insider trading. Offering an opportunity to explore the challenges of a rapidly changing industry, this volume will be of interest to academics, policy makers, and scholars in the fields of banking, insurance, and finance.

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Introduction

• adaptability
• stock market
• Islamic banking
• islamic finance

Table of contents (17 papers)

Front matter, the determinants of nonperforming loans: the case of turkey.

• Korhan K. Gökmenoğlu, Emmanuela G. Kenfack, Barış Memduh Eren

Determinants of External Debt: The Case of Malaysia

• Korhan Gokmenoglu, Rabiatul Adawiyah Mohamed Rafik

Asset Allocation, Capital Structure, Theory of the Firm and Banking Performance: A Panel Analysis

• Nader Alber

Does Research and Development Expenditure Impact High-Technology Export in Turkey: Evidence from ARDL Model

• Elma Satrovic, Adnan Muslija

The Cyclicality of Allowance for Impairment Losses in Indonesia

• Ndari Surjaningsih, Januar Hafidz, Justina Adamanti, Maulana Harris Muhajir, Dhian Pradhita Sari

Evalution of FDI in CE, SEE and Kosovo in Relation to Growth Rates and Other Indicators

• Nakije Kida

Forecasting Economic Activity of East Asia Through the Yield Curve (Predicting East Asia’s Economic Growth and Recession)

• Osman Altay, Kelvin Onyibor

Risk Information of Stock Market Using Quantum Potential Constraints

• Sina Nasiri, Eralp Bektas, Gholamreza Jafari

Migration Influence on Human Capital Under Globalization

• Olga Lashkareva, Sofya Abetova, Gulnar Kozhahmetova

Destination Marketing and Tourism Entrepreneurship in Ghana

• Selira Kotoua, Mustafa Ilkan, Maryam Abdullahi

Assessing the Factors Militating Against Microfinance in Alleviating Chronic Poverty and Food Insecurity in Rural Northern Ghana

• Bibiana Koglinuu Batinge, Hatice Jenkins

Improving the Mobile Payment Experience and Removing the Barriers

• Ersin Unsal

Financial Sector-Based Analysis of the G20 Economies Using the Integrated Decision-Making Approach with DEMATEL and TOPSIS

• Hasan Dinçer, Serhat Yüksel

Due Diligence for Bank M&A’s: Case from Turkey

• Veclal Gündüz

International Insurance Industry and Systemic Risk

• Necla Tunay, K. Batu Tunay, Nesrin Özataç

Bounds of Macrofinance and the Quality of Credit Portfolio in Emerging Economies

• K. Batu Tunay, Necla Tunay, Nesrin Özataç

Profitability Determinants of Islamic and Conventional Banks During the Global Financial Crises: The Case of Emerging Markets

• Alimshan Faizulayev, Eralp Bektas

Editors and Affiliations

Nesrin Ozatac

Korhan K. Gökmenoglu

Bibliographic Information

Book Title : Emerging Trends in Banking and Finance

Book Subtitle : 3rd International Conference on Banking and Finance Perspectives

Editors : Nesrin Ozatac, Korhan K. Gökmenoglu

Series Title : Springer Proceedings in Business and Economics

DOI : https://doi.org/10.1007/978-3-030-01784-2

Publisher : Springer Cham

eBook Packages : Economics and Finance , Economics and Finance (R0)

Copyright Information : Springer Nature Switzerland AG 2018

Hardcover ISBN : 978-3-030-01783-5 Published: 27 October 2018

Softcover ISBN : 978-3-030-13194-4 Published: 10 December 2019

eBook ISBN : 978-3-030-01784-2 Published: 26 October 2018

Series ISSN : 2198-7246

Series E-ISSN : 2198-7254

Edition Number : 1

Number of Pages : VI, 274

Number of Illustrations : 17 b/w illustrations, 22 illustrations in colour

Topics : Banking , International Finance , Islamic Finance , Economic Growth , Development Economics

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The state of AI in early 2024: Gen AI adoption spikes and starts to generate value

If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey  on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.

About the authors

This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.

Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.

AI adoption surges

Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.

Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).

Gen AI adoption is most common in the functions where it can create the most value

Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research  determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.

Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.

Investments in gen AI and analytical AI are beginning to create value

The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.

Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.

Inaccuracy: The most recognized and experienced risk of gen AI use

As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.

Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).

Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.

In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.

Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.

Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.

Bringing gen AI capabilities to bear

The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.

Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.

Gen AI high performers are excelling despite facing challenges

Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.

To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.

What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.

Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.

In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.

About the research

The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

Alex Singla and Alexander Sukharevsky  are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee  is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall  is an associate partner in the Washington, DC, office.

They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.

This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.

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