case study of banks

TOP 30 Case Studies of Customer Experience in Banking and Fintech Design

UXDA | Financial UX Design

This collection of the best UX case studies on creating and researching the customer experience in digital banking and Fintech apps. These articles are collected from Medium and arranged based on the amount of applause in 2023. This banking apps collection offers an excellent opportunity to get instant inspiration from 30 insightful UX case studies demonstrating how to design modern digital financial solutions.

Post by Alex Kreger, financial UX Strategist/Founder of UX Design Agency

1. Islamic Bank in Qatar: Creating Omni-Channel Experience

👏🏻 3.7k applause.

Case study by Atishay Goyal

A team of 8 developers, 1 Business Analyst, and 1 UX designer was setup onsite in Qatar to execute the revamp of CB’s digital presence and an Omni-Channel experience to its customers. This included retail, personal and corporate banking over web, mobile, ATM, call centers, and marketing channels.

We were not too focused on the competitor analysis as CB was one of the best apps in Qatar / Islamic Banking. Our major aim was to innovate and build something which ensures CB maintains the legacy.

In close collaboration with the marketing team, the design team came up with 7 factors (short-term and long-term) that would define the success of the new experience which CB offered. Initially was considered the HEART framework from Google and then cut short the parameters so that they can be measurable for an MVP.

UX Case Study: Omni-Channel Banking for the largest Islamic Bank in Qatar

An attempt to refine the banking experience of islamic banking with gesture and smart feature based workflow..

uxdesign.cc

2. Light Bank App: 10 Steps to Luxury Mobile Banking

👏🏻 3.3k applause.

Case study by UXDA | Financial UX design architects

UXDA team spent several months designing Light Bank. The end result is based on experience gained by solving financial design challenges daily and driven by their passion to disrupt the financial world in order to make it all about the users and their needs.

We dared to create the simplest, most beautiful and delightful banking UI experience in the world, while maintaining the full-scale digital banking functionality.

The story about Light Bank has become one of the most popular banking customer experience case studies ever made, reaching more than 50 000 views on Medium alone. It has been awarded by globally famous international design awards such as International Design Awards (IDA), London Design Awards, A’Design Award, DNA Paris Design Award, and also nominated for one of the world’s biggest and most prestigious design awards─the iF Design Award 2019 and the Red Dot Award 2019.

UX Guide: How to Create Simplest and Most Beautiful Banking Design in The World

What if there was a banking solution designed by apple would all the other digital financial services be forced out of….

uxplanet.org

3. ITTI Digital Back-Office: Complete Digital Transformation

👏🏻 1.2k applause.

The main challenge of ITTI Digital when they started working with UXDA was to disrupt the banking industry by creating a never-before-seen core-banking solution 100% focused on the employees: a solution that would take into account all bank employees’ pain points, needs and daily tasks, thus making their job easier, enjoyable and more meaningful from a banking end-customer perspective.

Most banks reveal they are very afraid of making such a huge and revolutionary shift, changing a structure that has been working for decades, but ITTI Digital is living proof that it’s 100% worth it.

During the project, ITTI Digital learned to make users their main priority, and their perception changed dramatically. It felt like they had finally opened their eyes to what has always been right in front of them─their users.

UX Design Case Study: Complete Banking Back-Office Transformation

We are sharing an exclusive step-by-step case study that clearly demonstrates the huge power of ux design transforming…, 4. cashmetrics: finance management service, 👏🏻 880 applause.

Case study by Tubik Studio

CashMetrics provides support for retailers, mainly in the fashion segment: it helps to organize their operations and monitor cash flows. It is aimed at simplifying operational processes, with a key focus on tracking profits, shipping costs, and fees.

Mobile and web applications help to effectively manage financial and communication issues and this way design directly supports the business.

When it comes to financing, the first idea that immediately comes to mind is boring tables and complex calculations. So, the primary goal was to step away from dull, unclear, and overwhelming data presentation and make the service eye-pleasing and easy to use.

Case Study: CashMetrics. User Experience Design for Finance Management Service

Ux design process for cashmetrics, the service helping retailers manage cash and operate effectively: dashboard and…, 5. banking as a game saving children’s financial future, 👏🏻 825 applause.

Adults struggle with finances but want their children to be successful people with good financial habits. Almost 60% of all working-age Americans have no retirement savings. At the same time, 49% of parents don’t know how to discuss finances with kids in an understandable and enjoyable way. Four in ten adults couldn’t cover a $400 emergency expense in the U.S.

We have to expand the horizon of banking apps by focusing not only on standard banking features and functionality but also on connecting finances with relationships between family members.

How could parents ensure their kids’ financial future? UXDA explored this challenge with a research-based kids’ banking app concept. It provides insights on how an app could impact family relationships, create a good foundation for their kids’ financial future and ensure a successful inclusion of children in the modern digital economy.

UI/UX Case Study: Banking as a Game Saving Children’s Financial Future

Adults struggle with finances but want children to be successful people with good financial habits. what if a banking…, 6. how we designed ai-powered spatial banking for apple vision pro, 👏🏻 813 applause.

With the launch of Vision Pro, Apple intends to shift the digital world from mobile computing, in which they have become trendsetters, to revolutionary spatial computing. Wizards from Cupertino offer a mind-blowing spatial experience on the visionOS platform. But how could spatial banking look and feel?

It looks like Apple is once again poised to take humanity to a new experiential frontier ahead of the next digital revolution. Vision Pro is just the first step toward mixed reality, testing technology and user experience. How should the banking industry prepare for this, and what should we consider?

Imagine a digital bank of the future. Unlike the traditional one, this future bank will be in constant dialogue with the user and take maximum care of the user’s financial life. At its heart will be an AI-powered advisor that constantly analyzes a multitude of data to find the best solutions for the user.

How We Designed AI-Powered Spatial Banking for Apple Vision Pro

Will the launch of apple vision pro start the next digital revolution wizards from cupertino offer a mind-blowing….

medium.muz.li

7. Banking Super App: Hundreds Of Features in One Mobile Solution

👏🏻 791 applause.

The presence of a large number of products and the flow of big data passing through the bank can become a promising basis for building a highly personalized banking ecosystem around a specific user. And, today, there are more and more advanced technological solutions of data processing and personalization through AI in banking accessible on the market.

However, despite the technological opportunities, the key challenge is delivering the super app experience in a clear and user-friendly way.

When considering how to create a banking super app, there’s an important question to answer: how will it work technically? As this kind of financial digital product is complex, a banking super app could rely on 10 digital banking trends.

UX Case Study: How to Create a Super App

The future of banking — the ultimate banking super app ux/ui design concept case study., 8. fintarget trading: how to attract newbies, 👏🏻 690 applause.

Case study by Purrweb

The BCS team suggested the hypothesis: the company will attract more customers if it proves that investing is easy — even for those who don’t have any experience. To quickly test the hypothesis, the BCS team decided to get started with an MVP.

BCS had a perfect tool for newbies — auto-following. The service was popular among current BCS clients but no one from the outside world knew about it.

The team found a perfect balance between deadlines, complicated topics, and agreements — this was the block-by-block UI/UX design for MVP. As a result, Fintarget released an investment platform within 1.5 months instead of the predicted three.

How to Attract Newbies to The Sophisticated Investment World. A UI/UX Case Study

Using a real-project example, the purrweb team explains how ui/ux design can simplify investments for newbies..

productcoalition.com

9. Metaverse Banking: First VR / AR Design Concept

👏🏻 687 applause.

True mixed reality potential lies far beyond games; it’s a new digital platform that provides a revolutionary user experience. VR and AR technologies will merge digital content with the real world to create one constant reality.

We could say that the modern digital world as we see it today is only the basic preparation stage for the immersive mixed reality that we will fully experience in a few decades.

The only way to be ready for the revolutionary switch to VR/AR digital reality is to start generating ideas and concepts today. With this case study, UXDA team encourages the industry to view financial products through the lens of the future. As technological advancements are rapidly developing and bringing more and more benefits for the customers, this is a must for any company that strives to become more successful, not only today but also in the future.

Case Study: World’s First Mixed Reality VR / AR Banking UX/UI Design Concept

Virtual reality (vr) and augmented reality (ar) are nothing new for passionate gamers, but, in other industries…, 10. next-gen hedge fund ui for nba players, 👏🏻 635 applause.

Magma Capital Funds, a Chicago-based private hedge fund investment manager hired UXDA to design a next-gen investment platform, Magma, for high-net-worth individuals. The aim was to create a quantitative hedge fund product to simplify and enhance the investment experience with cutting-edge technologies. The team has incorporated machine learning and artificial intelligence technology into the core of its back-end, and they needed an equally advanced client dashboard.

Magma Capital Fund’s challenge was to invent a digitally-driven customer-focused solution that makes the abstract concept of investment real and clear to digitally-native users. They needed a Magma product to wow the new generation of HNWI investors with luxury aesthetics and a simple and fully digitized onboarding experience while empowering them with a sense of control, sophistication and affiliation.

The traditional world of hedge fund investing is known for its high risks and requires an analytical approach with careful decision-making, which is what makes it so conservative. To create an innovative product in the conservative hedge fund industry, you need courage and a progressive mindset.

UX Case Study: Designing Next-Gen Hedge Fund UI for NBA Players

Magma capital funds, a chicago-based private hedge fund investment manager hired uxda to design a next-gen investment…, 11. applying chatgpt experience to conversational banking, 👏🏻 622 applause.

AI-powered solutions such as ChatGPT could fuel new types of products in banking, more personalized and more connected to users, but integrating innovative technologies often causes complications in terms of user experience. And here, the methods of financial UX design are very effective, as they help to imagine the unimaginable and create a digital solution, focusing on the needs of users. Let’s explore how it works with designing ChatGPT-like conversational banking products.

BELLA’s founders aimed to design their app as the heart and soul of their brand, to fulfill their mission and bring a breath of fresh air to the industry. They set out to bridge the emotional gap and break down the barriers of traditional user experience models by treating users like family members.

This case study illustrates an example of how disruptive AI technologies, such as ChatGPT, could be integrated into the banking experience with the help of customer-centered UX design.

UX Case Study: Applying ChatGPT Experience to Conversational Banking

Ai-powered solutions such as chatgpt could fuel new types of products in banking, more personalized and more connected…, 12. banking super app design to modernize mauritius, 👏🏻 607 applause.

Mauritius Telecom (MT), the largest telecommunications company in Mauritius, had the vision to ensure a powerful digital future for Mauritians. To achieve this, the MT team needed to expand their digital ecosystem by reinventing the existing my.t money app and transforming it into a digital bank. To open up new digital opportunities for the people of Mauritius, the MT team has moved beyond a regular service app and introduced the first financial Super App in Mauritian history.

Privacy is a crucial concern for users, and we addressed that in the design approach. The aim was to ensure a safe and secure experience for users, providing the option to hide sensitive information.

With a customer-centered mission, MT aims to modernize the islands of Mauritius Republic and make the lives of its 1.3 million inhabitants much more convenient and advanced through innovation and technology. As telecom leader MT uses its power to bridge a financial and payments technology gap.

Rich but well-balanced information architecture and a frictionless user journey were crucial to ensure easy access to essential everyday tasks while exploring multiple opportunities of the my.t money Super App. That required careful planning and continuous user testing and improvements throughout the user experience design process.

UI/UX Case Study: Banking Super App Design to Modernize Mauritius

Mauritius telecom (mt), the largest telecommunications company in mauritius, had the vision to ensure a powerful…, 13. purpose-driven financial investment platform design, 👏🏻 604 applause.

The Runrate app challenge was to seamlessly combine planning, budgeting and investing with a philosophy of manifesting the future. And, to create such a never-before-seen product, the project team needed the courage to change their mindset and enter the unknown.

Developing a new type of product always runs the risk of being unclear to users. As we implement non-standard features, user interaction may get complicated because of the need to learn and adapt to an unfamiliar mental model. To seamlessly integrate that unique model, we focused on researching user scenarios, prioritizing features in line with the business purpose and developing intuitive user flows.

We all get inspired by many different things ─ art, music, philosophy, movies, etc., and we don’t even think about how these things can sync with the financial industry. But, all of this relates to the human experience, to our emotions and the meaning of life. And we need the courage to shift our mindset and bring all of that into the financial world to put real meaning behind the money!

UX/UI Case Study: Purpose-Driven Financial Investment Platform Design

The runrate app challenge was to seamlessly combine planning, budgeting and investing with a philosophy of manifesting….

theuxda.medium.com

14. MasterCard: Pre-Paid Card Management

👏🏻 600 applause.

Case study by Davide Tremolada

MasterCard wants to allow users to manage their cards and solve problems in-app. To do that, they requested a redesign of the card management section, which would provide a clear and simple flow, where users feel secured.

We were able to work closely as a team, prioritizing a supportive approach to ideas and sketching instead of endless debating.

Using research and testing were fundamental steps into understanding the real needs and pain points of the users. More importantly, they gave us the knowledge to solve each step in an effective and smooth way.

MasterCard — Pre-Paid Card Management. A UX Case Study.

How to solve problems in-app, following users’ goals.

blog.prototypr.io

15. Akuna: Personal Budgeting Reimagined

👏🏻 586 applause.

Case study by Denislav Jeliazkov

On average, millennials who carry debt report owing to a total of $27,900 (excluding mortgages), slightly less than baby boomers and about $8,000 less than the average amount Gen Xers owe. 34% of Americans don’t know how much of their monthly income goes toward paying down their personal debt.

I led efforts to evolve the service and address customer pain‐points related to the spending and planning experiences.

The team was focused on features related to solving some of the major desired outcomes found in the initial research. The app should be simple and easy to understand. Also, encouraging them to build financial habits.

Personal Budgeting Reimagined — A UI/UX Case Study

Contactless cards and online shopping sites make payment a breeze., 16. bank of jordan: from 2,8 to 4,7 app rate in six months, 👏🏻 563 applause.

It took six months for the Bank of Jordan’s mobile banking app to go from a 2.8 to 4.7 star rating on Google Play. How? By teaming up with UXDA to create a comprehensive UX transformation. This changed the app dramatically, making banking more accessible and convenient than ever before for millions of customers in the Middle East.

In a step-by-step UX case study, we guide you through this unique digital transformation that completely changed the way so many people view their banking.

In the last couple of years, there’s been a “spring” of rapid banking digitalization in the Middle Eastern regions. Many huge and influential market players have launched new mobile banking apps for their retail customers. One such success story comes from a well-known and respected Middle Eastern bank─the Bank of Jordan.

UX Case Study: Use Empathy to Rise the App Rating from 2,8 to 4,7 in Six Months

It took six months for the bank of jordan’s mobile banking app to go from a 2.8 to 4.7 star rating on google play. how…, 17. bitex: chinese stock analysis app, 👏🏻 520 applause.

The task was set to make a complete redesign of a live application Bitex oriented initially to the Chinese market. The application is an aggregator that collects data from world exchanges, processes it, builds the necessary charts and diagrams and displays this information in a user-friendly and digestible way. The main goal of Bitex is to help the trader decide on investments.

The fact is that many users from Asian countries believe that the more information is shown on the page, the better.

An interesting feature was discovered in the subconscious perception of color-coding. Here’s the simplest example: in Western resources about finance, the rise in the share price is indicated in green and the drop in red. Meanwhile, in Asian countries, everything is the opposite. Since investors planned to enter the Western market, we were forced to add a switch “color growth price” to solve this problem.

Case Study: Bitex. UX Design Challenge for a Stock Analysis App

When you start working on a new application as a ux designer, you never know beforehand what knowledge you might need…, 18. igtb: corporate banking with 5,000+ screens, 👏🏻 512 applause.

In the world of banking, there are a lot of complex digital solutions. Among them are some truly exceptional ones, such as Contextual Banking Experience (CBX), a corporate banking product by iGTB (Intellect Global Transaction Banking), part of Intellect Design Arena. It consists of 5,000+ screens and is continuously growing. An outstanding effort was needed to integrate Design Thinking principles and UX design approach into such a huge banking solution development.

Working with Intellect Design Arena has been a remarkable experience for UXDA in many ways. It’s a unique opportunity to get a chance to work closely together with one of the world’s leading banking software vendors.

If we use the analogy with spaceships, mankind dreams about spaceships that are easily able to travel billions of miles through our solar system. Unfortunately, existing spaceships weren’t constructed for multiple takeovers and landings on other planets until the “SpaceX” program appeared, with an ambitious mission to land on Mars. iGTB has a similar mission in corporate banking to ensure huge, corporate, multi-account budget management with ease. Hence, the innovative iGTB solution was built.

UX Case Study: Building a Banking SpaceUX Shuttle

In the world of banking, there are a lot of complex digital solutions. among them are some truly exceptional ones, such…, 19. monese: smart transactions and spending breakdowns, 👏🏻 493 applause.

Case study by Monese

For the spending management features to be understandable, Monese needed to get the clearest data out of the raw, original transaction data. Using location databases and online resources, Monese was able to match the name of the merchant, the address and a lot of other details. In addition, Monese built a merchant service in-house, to show the right logo for most merchants and treat chains and stand-alone businesses differently. The result was a drastically improved look and feel, with richer, smarter insights.

Since we are not the first company to offer spending features, we didn’t want to just do what others are doing. We wanted to create the best solution, one that will fix the pain points of other services.

Monese took a lot of inspiration from music charts. Not just because “top lists” are amazing but also because they are built to keep you engaged and excited. A music chart, just like the Monese merchant chart, is always updating and can point out changes and trends.

UX case study: Smart transactions and spending breakdowns using location data

At monese, we want people to bank freely wherever they are without all of the annoying, sometimes impossible…, 20. bkt: turning a 100-year-old bank into a digital innovator, 👏🏻 491 applause.

Banka Kombëtare Tregtare (BKT), the largest and oldest operating commercial bank in Albania, recognized that their digital offerings have lagged behind and needed a revamp to adapt to present-day service requirements. They challenged UXDA to update the bank’s legacy operations by designing an app that would enhance customer service and empower clients to make smarter financial decisions. The goal was to change the perception of the brand from complex and formal to friendly and pleasant.

The existing app had functionality problems, with users finding it difficult to perform certain tasks, such as easily accessing transactions, resulting in confusion about their financial situation. The outdated design also strengthened their perception of BKT as an old-fashioned and complex bank, which was not reflective of the bank’s brand identity and value proposition.

Embracing change in a large financial corporation can be a daunting task, but BKT demonstrated that even big, established 100-year-old banks can successfully update their legacy to keep up with the digital age. They recognized that the banking app was more than just a digital channel — it was a product in its own right. Therefore, they directed increased attention and funding to turn it into a key asset to execute the brand’s mission.

UX Case Study: Turning a 100-Year-Old Bank into a Digital Innovator

Banka kombëtare tregtare (bkt), the largest and oldest operating commercial bank in albania, recognized that their…, 21. united arab bank: transition to next-gen experience, 👏🏻 458 applause.

United Arab Bank (UAB) is an established, leading financial solutions provider in the United Arab Emirates (UAE), offering its clients tailor-made financial services in corporate and retail banking in the UAE. However, they felt that UAB’s mobile banking does not entirely reflect their brand’s values and mission of providing an excellent customer experience for digital banking in the UAE. UAB wanted to serve customers with a full range of digital offerings, but, for some tasks, people were still visiting the physical branch.

The UAB team faced a challenge in enhancing the mobile banking experience for its users. They viewed the existing app as having a complex, confusing flow and an outdated design, which made it difficult for users to perform tasks and left a negative perception of its functionality and even the brand. Users were presented with a lot of information and description of details before performing the action. As a result, many users preferred to visit a physical branch rather than use the app.

UAB team achieved a customer-centered design for the most important user flows in the app and make improvements in line with the established bank brand and digital strategy. These adjustments helped the bank enhance the customer experience and digitize financial behavior patterns. With the help of UX, the UAB team was able to reinvent traditional bank services and bring a breath of fresh air into its digital ecosystem.

UX Case Study: Arab Bank’s Transition to Next-Gen Experience

United arab bank (uab) is an established, leading financial solutions provider in the united arab emirates (uae)…, 22. budgit: made with vulnerable customers in mind, 👏🏻 438 applause.

Case study by Jaymie Gill

budgit is a money management app that empowers and encourages vulnerable customers to control, maintain, and track their spending. Customers can get control of their money with a budget, gain awareness of their spending habits with insights, and access tools that assist them with financial management.

This project was created as my submission for D&AD’s New Blood Awards 2020 ‘Barclays UI/UX/IxD Digital Service Design’ brief.

Offering adaptive & accommodating digital banking tools and services, budgit supports vulnerable customers with everyday banking, helping them manage their money better, and in doing so, improve their mental & financial health.

‘budgit by Barclays’, a UX Case Study

Making money management work for mental health, 23. instadapp: blockchain decentralized application redesign, 👏🏻 425 applause.

Case study by ULTIM STUDIO

InstaDApp is a decentralized application that allows individuals to track their distributed blockchain assets over a range of products and move them around based on real-time market data.

InstaDApp is a fairly new product and even though the overall product brings value to users, we‘ve spotted some UX issues that are undermining the user experience for power users.

If you are not familiar with what decentralized applications (dApps) are, dApps exist and run on a blockchain network in a public, open-source, decentralized environment and are free from control and interference from any single authority, unlike standard apps such as Airbnb or Uber that run on a system which is owned and operated by an organization giving it full authority over the app and its database.

Redesigning the InstaDApp Dashboard — A UX Case Study

Redefining the future of decentralized banking., 24. bkash: redesign of the first mobile banking in bangladesh, 👏🏻 409 applause.

Case study by pixorus studio

bKash is the very first mobile banking service provider in Bangladesh. People who have a mobile and a bKash account can utilize all of the facilities provided by bKash. This app is a simple, easy-to-use and highly secure mobile money app for sending cash quickly to people, recharging mobile balance, paying at your favorite stores and shops, making utility and other bill payments from home, and so much more.

Understanding the audience is the main thing to know, that is needed to develop something that works for users.

The challenge was to find out the proper number of active app user, interviewing them to find out more problems, make a solution, exploring new ideas/features and finally design something that really solves user’s problems.

UIUX case study: Mobile banking app “bKash” redesign concept. (Step by step process)

A conversation between the three team members of pixorus that drove them to redesign a new concept for a pioneer of…, 25. accountable: a better solution to pfm, 👏🏻 368 applause.

Case study by Timothy Ogundipe

Accountable was designed to solve the problems around tracking of finances, access to financial records, spending analysis, budgeting and financial education.

Millennials want services that are immediate, reliable and offer a wide range of convenience.

We make use of money in our day to day activities. We spend money on what we want and what we need. Subscriptions and recurring expenses are ever-increasing due to our wants and needs. They begin to accumulate and get difficult to manage.

UI/UX case study: Providing a better solution to personal finance management.

Introduction, 26. monzo: designing a better borrowing experience, 👏🏻 344 applause.

Case study by Juliana Martinhago

There are cemented mental models around credit. Especially when it comes to loans. Some people are averse to the idea completely. This is often because of previous bad experiences of their own, or relatives. It doesn’t matter how well you build your product, some people just don’t want to use it.

We want to ensure that if anything goes wrong, we’ve got our customer’s backs. Human customer service is one of Monzo’s key attributes and it’s not different with Lending.

Designers will always strive for straight-forwardness, speed and simplicity. However, in Lending, you might want to reconsider. Monzo’s loans flow has always been incredibly smooth, but during some of user testing sessions, many people felt it might be “too easy”, and that can be scary. You might not want to offer people a loan in a couple of taps, but you still can keep it simple, of course.

Designing a better borrowing experience

What we’ve been learning while trying to solve a challenging problem and make borrowing work for everyone, 27. instadapp: blockchain decentralized application redesign, 28. loan management system redesign, 👏🏻 180 applause.

Case study by Julia Bondarenko

The old Loan system had an outdated UI and couldn’t support new business flows, which have developed over years in the industry. It was a stand-alone system, not included into the whole core banking system, which led to inconsistencies in the UI and data architecture.

The UI design was made to be easy for an eye, because users would typically work with the Core Banking system for majority of their work day.

The team updated the UI according to the new standards, also enhancing UX by intuitive flows and appropriate system feedback. New business flows were added to the system. The loan system was integrated into the complex architecture of the whole core banking system, accessible from multiple browsers.

Core banking system — a UX case study

This ux case study provides a detailed description of how i designed core banking systems as a senior ux…, 29. jenius redesign: solution toward a cashless society, 👏🏻 65 applause.

Case study by Floater

With Jenius, you’ll have full control of how you want to transact and manage your finance with your smartphone in a safe, easy, and smart way, just from the tip of your finger toe.

According to The Jakarta Post, the top five e-wallets based on monthly active users is GoPay, OVO, Dana, LinkAja, and Jenius. With the demographic users ranging from 20 to 35 years old.

Sometimes, with the redesign, there are remained new problems that may occur according to users. An in-depth interview may gather insights provided by users so that we can make our design better.

Jenius - All in one M-Banking Experience — UI/UX Redesign Case Study

In this our first case study, we want to share the process and decision made of how we redesign the jenius app., 30. erste bank: simplify banking for 300k seniors, 👏🏻 43 applause.

Case study by Madesense Digital

Erste Bank wanted to offer to people in their senior years smartphones equipped with our senior launcher and the new simplified banking app which is the subject of this case study.

Our screen design changed several times during testing, quite radically. The size of the font needed to almost double, seniors were not able to see the bottom navigation panel and they did not understand the chart on the main screen.

It works something like a visit to the doctor. We asked a lot of questions to better understand the needs of the business and its users. We dig deep for the reasons Erste Bank would want to create an application in the first place and how its functionalities should look to provide real value for older people.

How we helped Erste Bank to simplify mobile banking for almost 300k seniors | Case Study

It’s a holiday today and i finally found some time to look back a bit at how we created a mobile app for erste bank….

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Written by UXDA | Financial UX Design

UX experts who focus exclusively on next-gen financial services and infuse soul into 150+ banking and Fintech products across 37 countries | theuxda.com

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TymeBank Case Study: The Customer Impact of Inclusive Digital Banking

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This publication is also available in French  and Spanish .

Executive Summary

This case study presents insights from customer research with TymeBank clients that bolsters CGAP’s hypotheses around how digital banks can support the mission of financial inclusion. As a fully digital South African bank that disproportionately serves low-income rural customers, TymeBank has created a suite of basic products that cater to the essential financial needs of those customers, namely a low-cost transactional account and a high-yield savings account. Judging from product uptake and client testimonials, these products add to a compelling value proposition that not only resonates with customers but improves their lives.

TymeBank’s distribution network, which is based on its partnerships with the nationwide Boxer and Pick n Pay (PnP) grocery store chains, helps to keep operational costs low and passes cost savings onto customers in the form of more affordable services. A clear majority of the bank’s customers cite affordability as a key source of value and the reason they opened a TymeBank account. The distribution network also extends the bank’s reach to areas that are underserved by traditional players. The affordability and accessibility likely explain why underserved segments, such as low-income women and rural customers, are over-represented in TymeBank’s (active) customer base as compared to the overall banked population in South Africa.

Despite having access to other banking options, TymeBank customers overwhelmingly see no compelling alternatives in the market. Crucially, the value customers see in the bank appears to be inversely related to income, with poorer customers reporting higher levels of satisfaction.   

In today’s high-tech financial services landscape, which is often dominated by headlines about fintech startups and tech giants, it is easy to overlook the role banks can play in advancing financial inclusion. The high cost of running brick-and-mortar branch networks has traditionally inhibited banks from serving less profitable client segments, including the low-income groups that are the focus of financial inclusion. Banks have also been slow to adapt the digital innovations that have helped some newcomers reach these segments at lower cost. It is no surprise that some observers have questioned whether banks are even relevant to financial inclusion.

However, there are reasons to believe that banks can play an important role in financial inclusion if they overcome the challenges of their legacy systems and processes and digitize operations. In fact, banks have advantages over other types of financial services providers (FSPs) that may allow them to have an outsized impact on financial inclusion – if they are willing to expand down- market. Most importantly, banks do not face the same regulatory constraints as other providers. Whereas mobile money providers and fintechs generally cannot provide a wide array of financial products (ranging from savings to credit), banks can. License to intermediate retail deposits further plays to a bank’s advantage in the arena of digital credit. Banks can fund their lending portfolios with retail deposits that are typically cheaper than the other funding sources pure lenders use, which further reduces the cost of reaching low-income customers with credit.

CGAP previously presented three emerging business models in banking that we consider to be particularly promising for financial inclusion (Jeník and Zetterli 2020). These models are fully digital retail banks, marketplace banks, and Banking-as-a-Service (BaaS) (see Box 1). We conclude that they have the potential to deepen financial inclusion by:

• Lowering the cost of financial services; 
• Improving access to a greater variety of services;
• Creating services that better meet the needs of various customer segments; and 
• Improving the customer experience. 1

We analyzed several fully digital retail banks in a series of detailed case studies (Jeník, Flaming, and Salman 2020). One of these cases focused on TymeBank in South Africa. TymeBank is a fully digital retail bank founded with financial inclusion as a core business objective. Since its 2018 launch, the bank has onboarded over 4 million customers.

TymeBank offers low-income customers simple products at low prices, such as checking accounts, savings accounts, and debit cards – all through a distribution network that combines online and offline customer interaction based on partnerships with grocery store chains Boxer and PnP. In the area of credit products, TymeBank only offers a “buy now, pay later” option called MoreTyme. This case study provides a compelling example of how challenger banks can leverage digital technology to reach excluded customer segments with more affordable and useful products.

This paper builds on the TymeBank case study by examining the impact the bank’s services have had on low-income customers. By combining a quantitative analysis of TymeBank customer data with a phone-based survey of a randomly selected sample of low-income customers, the paper addresses the following questions:

• Does TymeBank serve low-income customers?
• Are its products relevant to low-income customers?
• What impact do the bank’s products have on low-income customers’ lives, in their own words?

The aim of this research is to shed light on the potential of digital banks to deepen financial inclusion in a way that improves the lives of low-income customers. CGAP is conducting additional research with other providers to better understand the impact of new financial services business models on customers. 2

TymeBank’s main value proposition consists of (i) simple, affordable, and accessible products; (ii) fast and automated onboarding; and (iii) incentive programs that appeal to target segments (e.g., the SmartShopper loyalty program). These are the qualities we would expect customers to point out when talking about the benefits of using TymeBank.

They are also important features that respond to three frequently cited barriers to financial inclusion: (i) expensive services, (ii) limited access points, and (iii) prohibitive know-your-customer (KYC) requirements. 3

Product affordability relies on TymeBank’s ability to maintain low operational costs and proportionally reduce them further as the bank grows. Current cost efficiency is due to the bank’s technology and microservice architecture (Flaming and Jeník 2020), its branchless model, and digitally facilitated onboarding. TymeBank onboards approximately 110,000 customers per month: about 93,500 through kiosks at an estimated cost of US$3 per customer, and about 16,500 via web at approximately US$0.60 per customer. 4

SOUTH AFRICA 5

South Africa enjoys relatively high levels of financial inclusion, including a banked adult population of approximately 85 percent in a market dominated by the country’s well-established commercial banks. However, many customers only use their bank account to receive government benefits; other use cases lag. There is little to no use of non-bank mobile money wallets.

Across demographic, socioeconomic, and geographic factors, financial inclusion levels positively corelate with higher age (people aged 18–29 are among those least included), urban areas, income level and regularity. Only 38 percent of individuals who reported having no income are banked, while 31 percent are entirely excluded.

METHODOLOGY

For the qualitative analysis based on customer interviews, 1,162 customers were screened from an overall sample of 10,000. The aim was to reach those TymeBank customers living in poverty (i.e., 70 percent or more likely to be living on less than US$5.50). Ultimately, 278 customers were identified for in-depth interviews. The screener surveys were conducted partly through interactive voice response (IVR) surveys and partly through live phone calls.

The quantitative analysis used customer data from TymeBank to assess the potential impact of the bank’s offering on its customer base, particularly individuals from groups that generally exhibit lower levels of financial inclusion. The data examined spanned a nine-month period from July 2020 to March 2021. The analyzed data correlated to active EveryDay account customers, defined as those who had performed a transaction within the past 30 days. Various sets of proxies were applied to estimate income level (e.g., onboarding location, outstanding balance, frequency of transactions, average size of transactions).

The analysis considered several important caveats:

a) We recognize that TymeBank is not representative of all fully digital retail banks in South Africa or elsewhere. The findings presented in this paper should not be interpreted as automatically applicable to other digital banks without careful consideration.

b) The research was conducted during the COVID-19 pandemic; some findings were or could be affected (e.g., as customer behavior changes in response to the pandemic).

c) Despite our best efforts to exclusively focus the analysis on low-income segments, we were unable to identify customers based on their stated income levels since TymeBank does not collect that information. Customer segmentation was performed through the previously mentioned set of proxies for the customer data analysis and through the screening questionnaire for the customer interviews. 6

d) The quantitative analysis focused on active customers with at least one transaction performed over the past 30 days, unless otherwise noted.

e) Where customers stated they had been financially excluded before opening a TymeBank account, we did not identify the underlying cause(s) of financial exclusion.

Key Findings

Does tymebank serve low-income customers.

Our research showed that TymeBank serves a higher proportion of low-income customers than the typical bank in South Africa, and a significantly higher portion of the most financially excluded segment.

Low-income customers in South Africa are relatively highly banked, although they are under-represented. South Africans earning US$200 per month or less constitute 47 percent of the population but only 41 percent of the banked population. 7 However, we estimate that this segment represents 48 percent of TymeBank’s active user base. 8

Among the three-quarters of TymeBank customers for whom data are available, 58 percent live in metropolitan areas and 42 percent in rural areas. This compares to South Africa’s rural population of 35 percent (as of 2016); we estimated this share to be even lower in 2021 (approximately 30 percent). 9 Hence, rural customers appeared to be noticeably overrepresented in the TymeBank user base.

Young, rural, low-income women comprise the most financially excluded and underserved segment in South Africa. This group forms 2.3 percent of South Africa’s banked population but 7 percent of TymeBank’s active base – nearly three times as much. 10 Finally, 13 percent of TymeBank’s active customers are first-time bank customers. 11

From a more general perspective, women in the low-income segment represent a higher-than- average share of the bank’s overall customer base sample (65 percent versus 57 percent),12 which suggests that low-income women particularly benefit from TymeBank’s services.

These findings lead us to conclude that TymeBank customers disproportionately seem to come from traditionally unbanked and underserved segments. In fact, the evidence suggests that the bank’s customer base may particularly skew toward the most underserved segments.

DOES TYMEBANK OFFER PRODUCTS THAT ARE RELEVANT TO LOW-INCOME CUSTOMERS?

Customers find TymeBank’s products useful and act upon features designed to promote certain behaviors.

The bank’s customers particularly value the low cost of its services and the convenience of access and usage. The lower their income, the more value customers seem to derive from its services. While the vast majority of TymeBank customers have previously held bank accounts, 67 percent say they see no good alternative to TymeBank (Figure 4). This response is despite the fact that, as of the time the research was conducted, the bank still only had a relatively modest payments and savings offering and had yet to launch credit products. (TymeBank has since launched MoreTyme, a “buy now, pay later” consumer credit product.) Customer endorsement seems driven by the strength of the bank’s value proposition and the low cost of its services. When asked, customers specifically appreciate the low fees (48 percent) and the high-yield savings account (38 percent).

Importantly, women make up a larger share of the total number of GoalSave (savings account) users compared to their representation in the overall customer base (3 percentage points higher). This finding suggests that female customers find value in the product, although they had slightly lower savings per user than men (US$58 versus US$59). The number of their deposits exceeds the number of withdrawals.

We did not find any significant differences in usage and product lifecycle patterns across income groups (aside from the frequency and size of transactions that correlate with income level), which suggests that TymeBank covers its customers’ essential needs across segments. The similarities in lifecycle (behavior patterns across products, such as most frequently performed type of transaction and their change over time) indicate that customers across income levels increase their engagement as they grow confident with the products.

However, important nuances do exist. For instance, the most excluded segment uses till machines for cash-in and cash-out transactions that are free-of-charge (and perhaps more accessible in certain areas), compared to the ATMs other segments prefer. This may be explained by price sensitivity that drives the preference for free till point withdrawals compared to ATM withdrawals, which are charged at US$0.61 per part of US$70.

The value generated for low(er) income customers will hopefully further expand as TymeBank expands its product offering (e.g., insurance and diverse credit products).

WHAT IMPACT DOES TYMEBANK HAVE ON CUSTOMERS’ LIVES?

Most customers report positive life changes due to their use of TymeBank. Importantly, levels of customer satisfaction increase as customer income decreases. This suggests that the TymeBank value proposition tailored to lower-income customers resonates well.

We relied on the actual voices of customers from the demand survey to gauge the impact the TymeBank offering had on its users. When asked, 73 percent of customers reported a positive change in quality of life attributable to TymeBank. The change could be associated with multiple factors. For instance, 80 percent of interviewed customers reported a decrease in the amount spent on bank fees, which is crucial for low-income segments that have historically experienced cost as one of the biggest barriers to financial inclusion. Nearly a third (31 percent) of customers who reported life improvement said that their access to financial services had expanded thanks to TymeBank. Customers also reported an improved ability to digitally transact and receive money (51 percent and 55 percent of all interviewees, respectively).

One of the most important findings concerned the ability to save. Seventy-three percent of interviewed customers reported an increase in their savings balance due to TymeBank. Savings likely drove customers’ ability to achieve their financial goals (68 percent) and improve financial resilience (32 percent).

These findings support our overall hypothesis that digital banks are well placed to deepen financial inclusion with cheaper, better products that reach beyond payments and are relevant to improving the lives of low-income customers.

It is critical to note that the high-interest yield on the GoalSave savings account was among the reasons most prominently cited by customers as driving them toward TymeBank. Our finding that female and young TymeBank customers were more likely to save using the bank service compared to what nationwide averages suggest was also important. While the national numbers show a 9 percentage point gap in formal savings between men and women (35 percent versus 26 percent), the gap among TymeBank customers favored women by 10 percentage points (45 percent versus 55 percent).

Our findings also revealed areas for improvement. Perhaps not surprisingly, TymeBank customers have not been spared the surge of fraud in South Africa. Ten percent of customers reported challenges concerning security and protection of funds. Six percent of respondents mentioned delays in service delivery and nearly the same share complained of issues related to digital access. Complaints were related to system downtime, clearing time (TymeBank is planning to offer real-time clearing), and the general concerns first-time users may have about their funds.

When asked about potential improvements, the presence of physical branches scored the highest (11 percent), followed by improved security (9 percent) related to the challenges mentioned in the previous paragraph and improved digital services (5 percent).

While these findings are encouraging, more research is needed before conclusive statements can be made about the broader role of digital banks in advancing financial inclusion. We encourage other experts to undertake similar research and add to the emerging evidence on the impact of digital banks on financial inclusion.

Acknowledgments

This case study features insights from research commissioned by CGAP and conducted by 60 Decibels and Genesis Analytics under the leadership of Ivo Jeník.

The author thanks CGAP colleagues Gayatri Vikram Murthy and Mehmet Kerse for reviewing this paper, and Gcinisizwe Andrew Mdluli for contributions and insights. Peter Zetterli and Xavier Faz oversaw the effort. Andrew Johnson led the editorial work.

This paper would not have been possible without the time and dedication of the team from TymeBank and TymeGlobal.

Flaming, Mark, and Ivo Jeník. 2020. “ How Does Tech Make a Difference in Digital Banking ?” CGAP blog post, 11 November.

Jeník, Ivo, Mark Flaming, and Arisha Salman. 2020. “ Inclusive Digital Banking: Emerging Markets Case Studies .” Working Paper. Washington, D.C.: CGAP.

Jeník, Ivo, and Peter Zetterli. 2020. “ Digital Banks: How Can They Deepen Financial Inclusion? ” Slide deck. Washington, D.C.: CGAP.

Download a PDF of this Case Study >>

1 To assess bank inclusivity, we developed and implemented a four-dimensional framework focused on cost, access, fit, and experience (CAFE). See Jeník and Zetterli (2020), page 42. In a business-to business (B2B) model, BaaS providers have other FSPs as their customers. Thus, their impact on end users is indirect.

2 see collection of cgap research on fintech and new financial services business models: www.cgap.org/fintech, 3 world bank global findex database (2017)., 4 atm-like machines placed in partner grocery stores – mainly pnp and boxer – allow for automated customer onboarding in less than five minutes., 5 this section is based on data from the finmark trust finscope (south africa) 2018 database., 6 the quantitative analysis used the average monthly inflows of customers originated at pnp value stores (us$271) and boxer stores (us$224) to estimate income level. the qualitative analysis estimated that 35 percent of tymebank’s customers live on less than us$5.50 per day, based on the screener survey findings., 7 the finmark trust finscope (south africa) 2018 database., 8 using place of origination (pnp value and boxer stores) as a proxy for low income., 9 south africa gateway .  , 10 the finmark trust finscope (south africa) 2018 database., 11 n = 1,162., 12 comparing screened customers (n = 1,162) and interviewed customers (n = 278)., related resources, inclusive digital banking: emerging markets case studies, digital banks: how can they deepen financial inclusion, related research, 8 billion reasons: inclusive finance as a catalyst for climate action, open finance self-assessment tool and development roadmap, global landscape: data trails of digitally included poor (dip) people.

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Case Study: How Aggressively Should a Bank Pursue AI?

• Thomas H. Davenport
• George Westerman

A Malaysia-based CEO weighs the risks and potential benefits of turning a traditional bank into an AI-first institution.

Siti Rahman, the CEO of Malaysia-based NVF Bank, faces a pivotal decision. Her head of AI innovation, a recent recruit from Google, has a bold plan. It requires a substantial investment but aims to transform the traditional bank into an AI-first institution, substantially reducing head count and the number of branches. The bank’s CFO worries they are chasing the next hype cycle and cautions against valuing efficiency above all else. Siti must weigh the bank’s mixed history with AI, the resistance to losing the human touch in banking services, and the risks of falling behind in technology against the need for a prudent, incremental approach to innovation.

Two experts offer advice: Noemie Ellezam-Danielo, the chief digital and AI strategy at Société Générale, and Sastry Durvasula, the chief information and client services officer at TIAA.

Siti Rahman, the CEO of Malaysia-headquartered NVF Bank, hurried through the corridors of the university’s computer engineering department. She had directed her driver to the wrong building—thinking of her usual talent-recruitment appearances in the finance department—and now she was running late. As she approached the room, she could hear her head of AI innovation, Michael Lim, who had joined NVF from Google 18 months earlier, breaking the ice with the students. “You know, NVF used to stand for Never Very Fast,” he said to a few giggles. “But the bank is crawling into the 21st century.”

• Thomas H. Davenport is the President’s Distinguished Professor of Information Technology and Management at Babson College, a visiting scholar at the MIT Initiative on the Digital Economy, and a senior adviser to Deloitte’s AI practice. He is a coauthor of All-in on AI: How Smart Companies Win Big with Artificial Intelligence (Harvard Business Review Press, 2023).
• George Westerman is a senior lecturer at MIT Sloan School of Management and a coauthor of Leading Digital (HBR Press, 2014).

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Digital transformation in banking: A complete guide

August 02, 2023

Digital transformation is a challenge for the banking industry, but it is necessary to adapt to the modern world where customers expect fast, efficient, and convenient services. Traditional approaches no longer meet the needs of the modern consumer. So, banks that want to remain competitive must abandon conservative methods and fully immerse themselves in the process of digital transformation.

This article discusses what is digital transformation in banking, key factors driving digital transformation , and successful examples of digital transformation in the banking industry.

What is digital transformation in banking?

5 key factors driving digital transformation in banking, technologies that drive digital transformation in banking, successful examples of digital transformation in the banking industry, how can soloway tech help you digitally transform your business.

Digital transformation in banking refers to applying new digital technologies and strategies to change and improve banking operations. This includes various changes to increase efficiency, meet customer needs, improve operational effectiveness, and develop new digital products and services.

Mobile applications and personal cabinets on the website are vivid examples of banks’ digital transformation. It is enough to press the buttons on a smartphone or computer to open an account, take out a loan, or order a new plastic card. The services are available not only to individuals but also to legal entities. The accounting departments use client-bank programs to transfer salaries to employees, pay taxes, and receive money from customers.

When you call the hotline of your financial organization, you are answered not by a specialist but by a robot. Virtual assistants have replaced some employees. Moreover, some US banks operate without branches at all. There are employees only in the head office, and customer transactions are conducted exclusively through the Internet.

It is more convenient for people to work with banks remotely, so credit institutions invest a lot of money in digital transformation. It is important that the interface of applications is user-friendly and understandable and transactions are fast. This will attract more customers and, accordingly, increase the profits of the financial company.

Pros and cons of digital transformation in banking

Digital transformation in banking is developing at a rapid pace. It has objective advantages: 

• Services of financial organizations are available from anywhere in the world
• The cost of remote operations is cheaper
• There are no queues
• Improved customer service
• Improved operational efficiency
• Big Data and analytics
• Innovation and new opportunities

But there are disadvantages too: 

• Dependence on the Internet
• Vulnerability of security systems and regular hacker attacks
• Inaccessibility for some customers
• Threat of job losses
• Dependence on technology

Technology should become a tool that will give banks more flexibility in decision-making and reduce risks.

Fintech companies, which have recently created large-scale services with significantly more interaction points with the client than the classic banking business, are taking the lead. Given that over the last 10 years, the banking industry has experienced a serious tightening of regulatory requirements, fintech is becoming a severe competitor for banks. The solution that banks have found is to change their business model with a focus on digitalization, create their own ecosystems, and develop non-financial services.

Ecosystems are a new global standard for business development and a major stage in the development of the economy. They aggregate data on producers and consumers and help optimize the resources of both. There is no turning back. Creating ecosystems seems to be a common vertical integration strategy for banks when related businesses are pulled up to the core business.

We highlight 5 key factors driving digital transformation in banking:

• Customer experience. Providing convenience and personalization for customers is a crucial factor in digital transformation. Banks should develop and implement innovative digital channels, such as mobile apps, online banking, chatbots, and others, to facilitate access to financial services and improve customer satisfaction.
• Automation and process optimization. The use of automation technologies, such as robo-advisors, machine learning, and artificial intelligence, helps reduce routine operations, lower costs, and improve efficiency. This can include automating lending, foreign exchange, internal audit, and more.
• Evolving regulatory landscape. Regulatory changes and initiatives have pushed banks to adopt digital transformation. Open banking regulations, data protection regulations (such as GDPR), and initiatives promoting competition and innovation have compelled banks to invest in technology to comply with regulations, foster innovation, and enhance transparency.
• Competitive pressure. Fintech startups and tech giants have disrupted the traditional banking landscape. These non-traditional players offer innovative and agile financial services, posing a competitive threat to traditional banks. To remain competitive, banks invest in digital technologies to improve their offerings, provide unique value propositions, and stay ahead of the competition.
• Enhanced customer insights. Digital transformation enables banks to gain deeper insights into customer behavior, preferences, and needs. By analyzing customer data, banks can offer personalized services, targeted marketing campaigns, and customized product recommendations, leading to higher customer satisfaction and loyalty.

These factors interact with each other and require a comprehensive approach for successful digital transformation in the banking industry.

An important point is cybercrime. The emergence of new technologies has left hackers with many loopholes for hacking into networks and devices. At the current growth rate, cyberattack damage will amount to about $10.5 trillion annually by 2025 —a 300% increase from 2015.

However, cyber threats are not slowing down digital transformation. On the contrary, they drive it (this applies to banks and other organizations). The search for vulnerabilities is a never-ending process that contributes to developing security systems. 

The main principle of the fight against cybercrime in many banks is that the fight should be at all levels. It means from the protection of external perimeters to specific systems at specific addresses and ports. This includes protection against DoS attacks, firewalls, full control of the bank’s systems, control of viruses to avoid data leakage, etc.

Technologies are evolving at an incredible pace. Artificial Intelligence (AI), Big Data, Blockchain, and other innovations transform how we live, work, and do business.

For example, artificial intelligence allows banks to automate processes and make customer interaction more personalized and efficient. Machine learning can analyze large amounts of data, identify patterns and trends that help make better decisions and predict risks. Machine learning and neural networks also greatly help in document recognition and remote customer verification. 

Big Data analysis is becoming a valuable tool in the banking sector, allowing banks to identify patterns, trends, and useful insights hidden in huge amounts of data. It can be used to develop personalized products and services, improve decision-making, detect fraud , and understand and predict customer behavior.

Blockchain is another innovative technology that can tremendously change the banking industry. Most of the current problems in the banking sector are related to the human factor. In particular, they include high commission costs and time spent on money transfers and transactions, internal and external fraud, human error, leakage of personal data, and much more. There are several main areas where blockchain technology can be used in the banking industry:

• Smart contracts
• International payments, settlements for foreign trade transactions, and internal payments
• Transactions with securities
• National digital currency

Other technologies that drive digital transformation in banking include Cloud Computing, Internet of Things (IoT), Robotic Process Automation (RPA), Biometrics, and Open Banking APIs.

Many success stories of digital transformation in banking demonstrate how digitalization improves customer banking experience and operational efficiency. For example:

• DBS Bank (Singapore). DBS Bank is considered one of the leaders in digital transformation. They have developed a digital platform, DBS Digibank, which provides customers with a wide range of banking services through mobile apps and online banking. They actively use artificial intelligence and analytics to provide personalized recommendations and improve customer experience.
• JPMorgan Chase (USA). JPMorgan Chase has embraced digital transformation to improve operational efficiency and customer service. They have developed their proprietary digital platform, Chase Mobile Banking, which allows customers to perform various banking transactions through mobile devices. They also actively apply machine learning and analytics to better analyze data and deliver services.
• ING Bank (Netherlands). ING Bank has moved from a traditional bank to a digital organization. They provide customers convenient online services and mobile apps and actively use data analytics to provide personalized offers and improve customer experience. They have also implemented digital tools within the bank to streamline processes and improve efficiency.
• BBVA (Spain). BBVA focused on digital transformation and innovation to improve customer experience and banking processes. They developed the BBVA Digital Banking platform, which provides customers with a wide range of services through mobile apps and online banking. They have also implemented blockchain technology to improve the security and efficiency of financial transactions.
• Ally Bank (USA). Ally Bank is an example of a successful digital transformation. They provide a full range of banking services through an online platform, including account opening, lending, investments, and mortgages. Ally Bank actively utilizes digital channels and tools to provide convenience and accessibility to customers.

These examples demonstrate how banks use digital technologies to increase the availability of services, improve customer experience, and optimize their operations.

At SoloWay Tech, we specialize in providing comprehensive digital transformation and consulting services to help businesses thrive in the digital age. With our expertise and industry knowledge, we can guide your organization through the complex digital transformation process, enabling you to unlock new opportunities and achieve sustainable growth. We can:

• Consult regarding the digital transformation of your business
• Develop a digital transformation strategy
• Design digital customer experience
• Optimize business processes
• Automate business processes
• Re-engineer legacy apps
• Develop innovative products and services
• Implement end-to-end ML and AI engines
• Engineer IoT
• Build Big Data infrastructure
• Consult regarding the best implementation of IT infrastructure in your business.

At SoloWay Tech, we understand that each business has unique challenges and requirements. Our collaborative approach, deep industry expertise, and proven methodologies empower us to tailor our services to your specific needs, enabling you to achieve sustainable growth and competitive advantage through digital transformation.

Embark on your digital transformation journey with SoloWay Tech and unlock the full potential of your business in the digital era. Contact us today to learn more about our services and how we can help you drive innovation, efficiency, and success.

Digital transformation has become imperative for the banking industry to adapt to the evolving needs and expectations of customers in the modern world. The shift towards digitalization offers numerous advantages, such as enhanced customer experiences, improved operational efficiency, access to Big Data analytics, and new opportunities for innovation. However, there are also challenges to consider, including cybersecurity risks, potential job losses, and dependence on technology.

To embark on a successful digital transformation journey, businesses may seek the expertise of companies like SoloWay Tech that specialize in assisting organizations in their digitalization efforts. With the right guidance and implementation strategies, banks can harness the power of digital technologies to stay competitive, meet customer expectations, and drive innovation in the ever-evolving banking landscape.

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Please note you do not have access to teaching notes, impact of big data analytics on banking: a case study.

Journal of Enterprise Information Management

ISSN : 1741-0398

Article publication date: 23 November 2022

Issue publication date: 7 March 2023

The paper aims to help enterprises gain valuable knowledge about big data implementation in practice and improve their information management ability, as they accumulate experience, to reuse or adapt the proposed method to achieve a sustainable competitive advantage.

Design/methodology/approach

Guided by the theory of technological frames of reference (TFR) and transaction cost theory (TCT), this paper describes a real-world case study in the banking industry to explain how to help enterprises leverage big data analytics for changes. Through close integration with bank's daily operations and strategic planning, the case study shows how the analytics team frame the challenge and analyze the data with two analytic models – customer segmentation (unsupervised) and product affinity prediction (supervised), to initiate the adoption of big data analytics in precise marketing.

The study reported relevant findings from a longitudinal data analysis and identified some key success factors. First, non-technical factors, for example intuitive analytics results, appropriate evaluation baseline, multiple-wave implementation and selection of marketing channels critically influence big data implementation progress in organizations. Second, a successful campaign also relies on technical factors. For example, the clustering analytics could promote customers' response rates, and the product affinity prediction model could boost efficient transaction and lower time costs.

Originality/value

For theoretical contribution, this paper verified that the outstanding characteristics of online mutual fund platforms brought up by Nagle, Seamans and Tadelis (2010) could not guarantee organizations' competitive advantages from the aspect of TCT.

• Transaction cost theory
• Big data analytics
• Enterprise information management
• Banking industry
• Precise marketing

He, W. , Hung, J.-L. and Liu, L. (2023), "Impact of big data analytics on banking: a case study", Journal of Enterprise Information Management , Vol. 36 No. 2, pp. 459-479. https://doi.org/10.1108/JEIM-05-2020-0176

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Copyright © 2022, Emerald Publishing Limited

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Top 40 Most Popular Case Studies of 2021

Two cases about Hertz claimed top spots in 2021's Top 40 Most Popular Case Studies

Two cases on the uses of debt and equity at Hertz claimed top spots in the CRDT’s (Case Research and Development Team) 2021 top 40 review of cases.

Hertz (A) took the top spot. The case details the financial structure of the rental car company through the end of 2019. Hertz (B), which ranked third in CRDT’s list, describes the company’s struggles during the early part of the COVID pandemic and its eventual need to enter Chapter 11 bankruptcy. 

The success of the Hertz cases was unprecedented for the top 40 list. Usually, cases take a number of years to gain popularity, but the Hertz cases claimed top spots in their first year of release. Hertz (A) also became the first ‘cooked’ case to top the annual review, as all of the other winners had been web-based ‘raw’ cases.

Besides introducing students to the complicated financing required to maintain an enormous fleet of cars, the Hertz cases also expanded the diversity of case protagonists. Kathyrn Marinello was the CEO of Hertz during this period and the CFO, Jamere Jackson is black.

Sandwiched between the two Hertz cases, Coffee 2016, a perennial best seller, finished second. “Glory, Glory, Man United!” a case about an English football team’s IPO made a surprise move to number four.  Cases on search fund boards, the future of malls,  Norway’s Sovereign Wealth fund, Prodigy Finance, the Mayo Clinic, and Cadbury rounded out the top ten.

Other year-end data for 2021 showed:

• Online “raw” case usage remained steady as compared to 2020 with over 35K users from 170 countries and all 50 U.S. states interacting with 196 cases.
• Fifty four percent of raw case users came from outside the U.S..
• The Yale School of Management (SOM) case study directory pages received over 160K page views from 177 countries with approximately a third originating in India followed by the U.S. and the Philippines.
• Twenty-six of the cases in the list are raw cases.
• A third of the cases feature a woman protagonist.
• Orders for Yale SOM case studies increased by almost 50% compared to 2020.
• The top 40 cases were supervised by 19 different Yale SOM faculty members, several supervising multiple cases.

CRDT compiled the Top 40 list by combining data from its case store, Google Analytics, and other measures of interest and adoption.

All of this year’s Top 40 cases are available for purchase from the Yale Management Media store .

And the Top 40 cases studies of 2021 are:

1.   Hertz Global Holdings (A): Uses of Debt and Equity

2.   Coffee 2016

3.   Hertz Global Holdings (B): Uses of Debt and Equity 2020

4.   Glory, Glory Man United!

5.   Search Fund Company Boards: How CEOs Can Build Boards to Help Them Thrive

6.   The Future of Malls: Was Decline Inevitable?

7.   Strategy for Norway's Pension Fund Global

8.   Prodigy Finance

9.   Design at Mayo

10. Cadbury

11. City Hospital Emergency Room

13. Volkswagen

14. Marina Bay Sands

15. Shake Shack IPO

16. Mastercard

17. Netflix

18. Ant Financial

19. AXA: Creating the New CR Metrics

20. IBM Corporate Service Corps

21. Business Leadership in South Africa's 1994 Reforms

22. Alternative Meat Industry

23. Children's Premier

24. Khalil Tawil and Umi (A)

25. Palm Oil 2016

26. Teach For All: Designing a Global Network

27. What's Next? Search Fund Entrepreneurs Reflect on Life After Exit

28. Searching for a Search Fund Structure: A Student Takes a Tour of Various Options

30. Project Sammaan

31. Commonfund ESG

32. Polaroid

33. Connecticut Green Bank 2018: After the Raid

34. FieldFresh Foods

35. The Alibaba Group

36. 360 State Street: Real Options

37. Herman Miller

38. AgBiome

39. Nathan Cummings Foundation

40. Toyota 2010

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By Peter Ramsey

Key takeaways

A race to open my account, what the challenger banks have done differently, 1. opening an account via the app, 2. instant access to apple pay, 3. digital id verification, 4. asking for a limited address history, 5. the welcome letter, opening 12 bank accounts.

On average, people stay with their bank for 17 years—which implies that it’s either too much effort to switch, or that they have brand loyalty beyond belief. People are more likely to stay with their bank than they are to stay with their partner.

A study from 2020 estimated that within 5 years 44% of brits will have a digital-only bank. But most of you reading this will already have an account with at least one of the challenger banks, like Monzo , Revolut or Starling . You probably also remember the experience being far better than when you set up your traditional bank account more than a decade ago.

But was the application process actually any better, or are you just wrongly assuming that your traditional bank hasn’t improved since 1995?

This chapter sets out to answer the question: is it actually any easier to open an account with one of the challenger banks, or it is all a brilliantly orchestrated marketing spin.

Summary : The challenger banks were significantly quicker (in days) and required less effort (in clicks) to get an active account.

Allowed you to open an account directly through the app.

Changed the perspective of when your account is ‘open’.

Remove the worst part of the traditional experience.

Minimised data collection.

Created a memorable experience when your card arrives.

The industry can be confusing. Promises of “opening an account in minutes” are enticing, and might technically be true, but don’t reflect what we really experience.

In the real world, you’d probably only say your account was active when you could actually use your card. So I set the following parameters:

1. I can use my card in a shop . This includes receiving my PIN.

2. I have access to online banking on my mobile . This includes receiving my card reader (if needed).

And remember, none of the banks knew they were in this race.

Number of working days to have an active account

Number of working days

As you’d expect, the challenger banks were considerably faster than the more traditional banks. And remember; these are working days, not calendar days.

But when we talk about how easy something is, there’s another metric to consider: effort required. Typically, products feel more intuitive—and easier—the less input they need. So I also logged every time I had to do something .

Number of clicks to create an account

Number of clicks

As you can see, the challenger banks not only required the fewest clicks, but needed significantly fewer than even the best scoring traditional bank.

It took 5x as many clicks to open an account with First Direct, than it did with Revolut.

So the answer is yes, it actually is easier to open an account with the challenger banks . Maybe not significantly easier than every traditional bank—Barclays and Lloyds were also very good—but they were certainly easier than the average traditional bank.

But how did they achieve this? The second part of this chapter explores that question.

How did the challenger banks require significantly less input? And what lessons can we learn to help us build better products in the future?

It shouldn’t come as a surprise to you that all the banks have IOS apps. But what may surprise you is that not all of the banks let you actually open an account through their apps.

For this, if I was required to phone somebody, or visit their website in a browser, I counted it as a fail. I wanted to open the account using their app, and just their app.

So which banks allow you to open an account via the app?

First Direct

Two thirds of the banks forced me to go to their website at least once to get an active account. Some of them let you apply for an account through their app, but then you couldn’t create your online banking credentials unless you log in through their website.

And it’s worth noting that parts of Nationwide , Metro , Santander and Natwest  were not even fully responsive on mobile.

What the challenger banks—and to their credit, Barclays—have done is consolidate all of their processes . Sure, this is considerably easier when starting with a blank canvas, as these new banks were, but nonetheless, it makes a significant improvement to the experience.

Whilst the challenger banks are also tethered by the speed of the postal service, they do have a trick up their sleeve. They allow you to add your card to Apple Pay as soon as your account has been approved. None of the traditional banks let you do this.

Sceptics may argue that this feature is a gimmick, because you can’t use your card in all shops, or to get cash out, but they’re missing the point. They’d be mistakenly assuming that the value of this feature was in its utility, which it isn’t.

Instead, the value of getting some instant access is in the feeling of progress, achievement and ownership. By giving some access to the account, and proving that it is open, it subconsciously changes the way you think.

Traditionally, you’d not say that you had an open bank account until you’d actually received your card.

But the challenger banks have rephrased this wait as being “ your account is open, see, you can use your card it right now , we just need to post it to you “.

This is immensely subtle and clever. That simple change of perspective makes opening an account with Monzo, Revolut and Starling feel instant, even if it really does take a few days to get full access to your account. You get an immediate feeling of ownership and objective success.

The key here is to understand that most of the time we cannot change external factors. The challenger banks couldn’t make the post arrive faster. But when building an experience you can change the users perception of that wait. There’s another analogy in the footnotes if you’d like more on this point.

This is immensely subtle and clever. That simple change of perspective makes opening an account with Monzo, Revolut or Starling feel instant.

Understandably, you need to prove your identity when opening an account. Traditionally, this would mean walking into a branch with your passport, or sending scanned copies of your ID in the post.

But what was a laborious exercise—and might’ve taken a whole afternoon—can now be done in seconds, from the comfort of your own home.

The user scans a suitable ID document, and then takes a photo or video selfie, that’s it. No trip to the bank required. So who actually utilises digital identity verification services?

To clarify, there are some circumstances where banks will probably always have to complete at least some of the checks manually. But for your average consumer, like me, there are very few excuses to not use one.

Cost certainly isn’t the issue. Third parties like Onfido (Revolut) , Jumio (Monzo) , and HooYu (Natwest) provide this service for—in my experience, and estimating a bank’s volume—less than 50p a check. Possibly a lot less.

It’s obvious that this is such a major improvement on the user experience. It’s possible that proving your identity in the traditional way, would have been the worst part of the entire process. And because of a psychological heuristic known as the 🏔 Peak-end Rule , it would have been one of the most rememberable.

The challenger banks also asked fewer questions . Let’s focus on one area: address history.

Banks all ask for your address history so they can run a Credit Search. But there’s some discrepancy in how much address history the banks ask for:

Only asks for current address

Asked for previous addresses (more than 1 at least):

First-direct

Monzo , Revolut and Starling only asked for my current address. Whereas every other bank required at least one previous address—normally asking for 3 years of address history.

In short, the more address history you provide, theoretically, the higher the chance that the bank will accurately find your credit report.

Which highlights an important trade-off :

1. Ask the user to provide previous addresses — Increased input required, but a greater chance of finding their credit report.

2. Remove the previous address fields — A better user experience, but a slightly increased risk of not finding their credit report.

This decision is not quite as simple as that and i’ve discussed the broader impact of this in the footnotes.

But, it’s clear that the challenger banks have made a conscious decision to prioritise the experience . This self-restraint is even more impressive when you consider that for many years companies have been trying to harvest as much data as possible—even if they never use it.

It’s clear that the challenger banks have made a conscious decision to prioritise the experience.

Since publishing this chapter, Monzo has been in touch with me to talk through a third option —which is immensely clever. They will ask for one address, run a search, and if they cannot find you, they’ll then ask for more.

I’m unsure at this point if the other challenger banks have done the same—but it’s entirely possible. Mitigating risk in this two-step process adds technical challenge, but reduces the amount of input required from the user in many cases.

Receiving your card in the post is an experience—or rather, it’s an opportunity for the bank to create an experience that you remember positively.

It’d be foolish for a bank to downplay the impact that this letter has. It’s not just a piece of paper, it’s the moment your digital actions become physical . Let’s focus on just one element that they all have in common: the envelope.

If I asked you to imagine the most boring envelope you could, it’d probably be a plain white rectangle with ‘Private and confidential’ written on it.

And which of our banks used this exact envelope?

Arrived in that boring envelope:

Did not arrive in a boring white envelope:

I’m not sure there could be a more literal example of these banks trying to look different . They’re not just sending their cards out in less boring envelopes, but in envelopes you probably haven’t seen very often.

All 3 of the challenger banks have sought to create an experience that feels different. Something that—before you even open it—signals “we are not your typical bank”.

Personally, my favourite is Monzo’s, because it’s brilliance is in the subconscious comparison you’ll make when picking it up. When was the last time you received a letter in a blue envelope? Possibly never. If you did it would have been probably a birthday card from a friend .

Building memorable experiences don’t need to be expensive. The distinction is in the detail. In this case, it was as simple as using a coloured envelope.

And if you’re in any doubt over the opportunity missed here, try searching Twitter for “ Starling card arrived” , then “ HSBC card arrived” .

It’s not even close: opening an account with any of the challenger banks, is considerably better than with any traditional bank.

In this chapter I’ve highlighted 5 things they’ve done differently, but there are a lot more. Other than Barclays —who I was really impressed by—the traditional banks have a lot of catching up to do.

I know you’ll want me to pick a winner between Monzo , Revolut and Starling —as I will in later chapters—but there was very little between them here. Whilst Monzo took nearly twice as many clicks to open an account than Revolut, Monzo actually opened my account faster . Meanwhile, Starling was consistently good throughout all the tests.

I’d probably give all 3 of them a very similar score.

That was an easy way to consume 50 hours of UX research, right?

What will you dive into next?

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Hiding complexity to create effortless onboarding

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The secrets of a $7.08 welcome bonus

The product psychology behind why Robinhood offer new users $7.08 of free stock, and not $10.

All of the UX analysis on Built for Mars is original, and was researched and written by me, Peter Ramsey.

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Deposit Insurance and Bank Liquidity Creation: Evidence from a Natural Experiment in China*

• Open access
• Published: 02 July 2024

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• Xiangyi Zhou 1 ,
• Xinyue Li 2 ,
• Yifan Zhou 3 &
• Alper Kara 4  

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In this paper, we examine how the implementation of deposit insurance influences the impact of bank capital, excess lending, banking competition and monetary policy on liquidity creation of banks. Our examination uses China’s introduction of deposit insurance in 2015 as a natural experiment. We find that deposit insurance positively reinforces the effect of capital but weakens that of monetary policy on liquidity creation. We do not find that deposit insurance has a significant influence on the effects of excess lending and competition on the liquidity creation of banks. We also show that the implementation of deposit insurance has heterogenous effects on the liquidity creation of large and small banks.

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1 Introduction

The theory of financial intermediation postulates that liquidity creation is one of the major reasons why banks exist and is a fundamental service they provide to the economy (Diamond and Dybvig 1983 ; Bhattacharya and Thakor 1993 ). Banks create liquidity by borrowing short-term, often liquid, liabilities and then lending to projects that are often long term and illiquid. By transforming illiquid assets into liquid liabilities (i.e., liquidity transformation), banks improve the allocation of capital and accelerate economic growth (Bencivenga and Smith 1991 ). Among all the services that banks provide, liquidity creation has the most significant effect on economic growth (Berger and Sedunov 2017 ).

At the same time, that theory posits that banks are, by nature, opaque institutions with complex and risky business activities, especially related to lending. This opacity results in information asymmetries for depositors that dissuade them from depositing their savings with banks. Hence, in order to attract savers and increase public confidence in banks, as well as the financial system, many countries heavily regulate the banking sector and utilize a set of safety nets. One of the most widely implemented financial safety net is deposit insurance. This net has become common in the last 50 years with the number of countries with deposit-insured banking systems increasing from 12 in 1974 to 147 in 2023 (International Association of Deposit Insurers 2023 ) Deposit insurance has the aim of protecting depositors and providing stability to the financial system as it reduces the possibility of bank runs and contagion between banks (Diamond and Dybvig 1983 ). Footnote 1

Therefore, the overarching objective of this paper is to explore the possible link between deposit insurance and liquidity creation. To do so, we combine the two strands of the literature on the channels for liquidity creation by banks and the impact of deposit insurance. In particular, we aim to examine whether and how the presence of deposit insurance influences the channels through which banks create liquidity.

Studies have proposed a number of channels that that lead to the liquidity creation of banks such as their capital levels (Diamond and Rajan 2000 , 2001 ), excess lending (Chen et al. 2015 ), and competition (Boyd and De Nicolo 2005 ) as well as monetary policy (Berger and Bouwman 2017 ). Recently, more empirical studies have tested these channels, especially after the seminal paper by Berger and Bouwman ( 2009 ), which develops various robust indicators to measure liquidity creation. On the link between bank capital and liquidity creation, Lei and Song ( 2013 ), Distinguin et al. ( 2013 ), Horvath et al. ( 2014 ), Fu et al. ( 2016 ) and Casu et al. ( 2019 ) report a negative relationship for US, Czech, Chinese and European banks, respectively. Footnote 2 However, some studies have found that the relationship between nonperforming loans, an indicator of excess lending, and liquidity creation is either non-existent (Umar and Sun 2016 ) or negative (Chen et al. 2015 ) in China. Horvath et al. ( 2016 ) and Jiang et al. ( 2019 ) show that competition reduces liquidity creation. While Berger and Bouwman ( 2017 ), Rauch et al. ( 2011 ), and Dang ( 2022 ) find that monetary policy has a positive impact on liquidity creation for US, German, and Vietnamese banks, respectively.

Two theoretical approaches are often used to explain the creation and expansion of deposit insurance: an economic approach grounded in potential efficiency gains (i.e., public-interest motivation) and political approach grounded in the rising power of special interest groups that favored deposit insurance (i.e., private-interest motivation) (Laeven 2004 ; Calomiris and Jaremski 2016 ). The empirical research on deposit insurance presents investigations into the economic drivers of the adoption of deposit insurance as well as its effects on the performance of banks and the risk and stability of the financial system (Kane 1987 ; Barth 1989 ; Demirguc-Kunt et al. 2008a , b ; Calomiris and Chen 2016 ). This research commonly makes the argument that deposit insurance may have negative effects on the stability of the financial system if it is not supported by the regulation and supervision of banks, as this insurance could lead to excessive bank risk-taking due to moral hazard (Demirgüç-Kunt and Detragiache 2002 ). Literature has also provided overwhelming supporting evidence that, all things being equal, deposit insurance increases the risk-taking of banks, reduces the market discipline of insured depositors and benefit larger institutions more (see, e.g., Ely and Weaver 1991 ; Beltratti and Stulz 2012 ; Calomiris and Jaremski 2019 ; Dewenter et al. 2018 ; Hovakimian and Kane 2000 ; Ioannidou and Penas 2010 ; Nier and Baumann 2006 ; Wagster 2007 ). Footnote 3

The objective of our paper poses an important question as the implementation of deposit insurance could potentially modify these channels for liquidity creation. By the same token, the outcome expectations of policy interventions to increase liquidity creation in the financial system via the four identified channels may be dependent on the existence of deposit insurance in a country. Our paper is also relevant to countries that have already implemented deposit insurance. For example, albeit being a less likely scenario, governments’ consideration of removing deposit insurance may have indirect consequences on liquidity creation. Similarly, increasing or decreasing the coverage of deposit insurance may have indirect consequences on liquidity creation. For instance, after the Global Financial Crisis the European Union required its members to increase their protection of deposits to a minimum of €50,000 in 2009 and then to €100,000 in 2010 (European Commission 2014 ). Such policy changes to strengthen financial stability may have unanticipated positive or negative consequences on the liquidity created in the financial system. Hence, our paper has the potential to inform these decisions.

In particular, in this paper we use a natural experiment in China in which the government promulgated the regulations and set up the deposit insurance system in 2015. Footnote 4 We use a panel data set of 126 Chinese commercial banks covering a period from 2011 to 2017.

We adopt two identification strategies. First, we use the external shock provided by the introduction of deposit insurance to directly estimate the policy impact on the liquidity creation of banks from the modification of the channels of capital, excess lending, competition, and monetary policy. Second, we use a difference-in-differences (DID) approach that not only identifies the difference in liquidity before and after the policy implementation for each bank but also the heterogeneous effects of the policy on different types of banks. In this set-up, we assign the largest five banks (hereafter “big 5”), which are also state-controlled Footnote 5 , as the control group. Our reasoning is that the deposits of these banks were already protected implicitly before the introduction of the system in 2015. Given that the big 5 had a market share of more than 50% of total assets, the public’s common belief was that they were too big to fail and that the government would bail them out in case of a crisis or a bank run. The treatment group is other banks. We postulate that the interaction of explicit deposit insurance in 2015 with the four major channels through which banks create liquidity may have different effects on the big 5 than other banks.

We find that the deposit insurance positively reinforces the effect of bank capital but weakens that of monetary policy on liquidity creation. We do not find a significant influence of deposit insurance on the effects of excess lending and competition on the liquidity creation of banks. We also show that implementation of deposit insurance has heterogenous effects on the liquidity creation of large and small banks. Our results are robust to alternative definitions of liquidity creation and when we separate the on- and off-balance sheet effects.

Our contribution to the literature is threefold. First, we extend the wide-ranging literature on deposit insurance and provide empirical evidence in relation to the possible effect of deposit insurance on liquidity creation. As mentioned above, the literature often examines the effects of deposit insurance on the lending of banks and the financial stability of the system, and it has shown that deposit insurance increases the insolvency risk of banks by encouraging reckless behavior that reduces the liquidity risk (or increases liquidity creation) of banks (Calomiris and Jaremski 2019 ; Demirgüç-Kunt and Detragiache 2002 ). In particular, we contribute to this literature by examining the interaction of deposit insurance with the main channels through which banks create liquidity.

Second, our paper contributes to the strand of the literature that focuses on the factors affecting liquidity creation (Berger and Bouwman 2017 ; Boyd and De Nicolo 2005 ; Chen et al. 2015 ; Diamond and Rajan 2000 , 2001 ; Fungáčová et al. 2017 ). Closest study to our paper is the work by Fungáčová et al. ( 2017 ) in which they examine the effect of deposit insurance on the relationship between capital and liquidity creation in Russian banks. They find that the introduction of deposit insurance in Russia had different effects on the relationship between banks’ capital and liquidity creation across different types of banks. However, we extensively expand their work by adding excess lending, competition, and monetary policy to bank capital to examine the impact of deposit insurance on liquidity creation.

In addition, the exploration of China’s experience in implementing deposit insurance is important as its institutional setting differs from Russia. First, prior to the implementation of the deposit insurance in Russia, the government explicitly stated that it would only protect the deposits in state-owned banks (Chernykh and Cole 2011 ). In contrast, prior to the implementation of deposit insurance in China, the government has never publicly announced that it would only protect the deposits in state-owned banks. However, society expected that the government would rescue these banks first. Second, unlike Russia, China did not experience a large-scale banking crisis before the implementation of explicit deposit insurance. The incentive for Chinese financial authorities to introduce explicit deposit insurance was to improve the functioning of the banking sector to enhance financial system stability rather than in order to deal with a banking crisis, such as in the case of Russia. These nuances between the two countries highlight the relevance and importance of exploring the Chinese experience in the implementation of deposit insurance.

The liquidity creation in the Chinese banking system has received substantial attention recently due to the growth of the Chinese economy and its increasing importance on the world stage (Zhang et al. 2021 ). Hence, we also contribute to the limited literature that examines the liquidity creation of banks in China. For example, empirical studies find that banks’ diversification, and increased capital have the potential to reduce their liquidity creation (Hou et al. 2018 ; Lei and Song 2013 ), while excess lending does not influence liquidity creation (Chen et al. 2015 ). Zhang et al. ( 2021 ) show that excessive liquidity creation increases systemic risk. Therefore, as far as we are aware, our paper is the first to provide a comprehensive examination on the effect of the introduction of deposit insurance on liquidity creation in the banking sector of China. It is also important to understand the effects of safety nets, such as deposit insurance, regarding the opaque and large-scale interconnections of banks with other financial intermediaries (particularly the shadow banking sector) being a threat to the financial stability of China (Ehlers et al. 2018 ; IMF 2018 ; Nivorozhkin and Chondrogiannis 2022 ).

The paper is organized as follows. In the next section, we provide a brief review of the developments in the banking sector of China and the implementation of deposit insurance. Section  3 provides a review of the literature on the effect of deposit insurance on liquidity creation. In Sect.  4 , we detail the research design and empirical models. Section  5 presents the data sources, definitions of the variables, and descriptive statistics. We discuss the empirical results in Sect.  6 , followed by robustness tests in Sect.  7 . Section  8 concludes.

2 Institutional Background

Prior to the series of reforms in the banking industry in China in 1978, the People’s Bank of China (PBOC) also operated as a commercial bank (Lin and Zhang 2009 ). The objective of the reforms was to transform the banking sector from being solely state-owned, monopolistic, and policy-driven to a multi-ownership, competitive, and profit-oriented system (Liang et al. 2013 ). At that time, China’s financial industry was immature; there was a very limited number of banks that provided simple services, were often government backed, and lacked the ability to operate independently (Zhou 2016 ). Therefore, China needed a banking system that could support its ambitious goals for economic development. As part of the reforms in 1978, the government created four state-owned commercial banks (Bank of China, the China Construction Bank, the Agricultural Bank of China, and the Industrial and Commercial Bank of China), and the PBOC passed on commercial banking duties to these banks, acting as a supervisor to the financial system. In addition, the government introduced a number of banks jointly owned by it and the public (joint-stock banks) in the mid-1980s (Liang et al. 2013 ). Due to these changes, the banking system became increasingly complicated, and risks were building up on banks’ balance sheets. At the same time, the government did not develop the capabilities of its regulators. Hence, due to losses and failures, it had to undertake large-scale bailouts in the late-1980s, which contributed to the increasing lack of market discipline (Zhou 2016 ).

Between 1993 and 2017, China’s banking sector had undergone a notable transformation (Williams 2018 ). In the early 1990s, the central government gave local governments the authority to establish regional banks, known as city commercial banks, by consolidating local rural and urban cooperatives. Today there are more than 450 city and rural commercial banks operating across the country. These banks have played an important role in China’s regional economic development (Zhang et al. 2016 ). External observers felt that China’s banking sector was technically insolvent as the newly established banks struggled to cope with the fast pace of economic growth (Liu 2009 ). At the end of the 1990s, it became apparent that the four big-state-owned banks, which each supported a particular sector in the economy, built up significant amounts of nonperforming loans, creating a serious threat to China’s financial market and the economy (Li and Zeng 2007 ). As a result, the government injected RMB 270 billion into the big four banks by issuing special Treasury bonds (Okazaki 2007 ). Footnote 6 Similarly, the nonperforming loans of city and rural commercial banks were estimated to be 50% of their total loans during the same period. The government eventually required them to merge with healthier financial institutions. Hence, Wu ( 2012 ) estimates that China spent RMB 5 trillion on financial bailouts during this time, which was around one-third of China’s GDP at the time. These reforms continued up to 2007 when China gave licenses to foreign banks to accept Chinese citizens’ deposits in local currency. Some studies have argued that such a rapid expansion of the banking sector required a more robust regulatory system and triggered the establishment of the China Banking Regulatory Commission (Zhang et al. 2016 ). Footnote 7

However, even though there were wide-ranging bailouts and mergers in the banking sector in China, there were significant differences in terms of the risks taken by the depositors of non-state-owned banks. For example, some Chinese bank failures showed that small-bank depositors faced a much higher risk of losing their deposits. Furthermore, the process for depositors to claim losses or receive compensation was costly as they had to wait for a significant number of years as the process for bankruptcies or mergers was very long. Given the inflation and opportunity costs, the loss for depositors investing in small banks was large. For instance, in 2001, the Shantou Commercial Bank became insolvent and was taken over by the authorities. The risk disposal process lasted 10 years until being completed in 2011. Footnote 8 Another example is related to the failure of Hainan Development Bank, which went bankrupt in 1998, that caused significant losses for depositors. Today, the liquidation process of Hainan Development Bank has taken more than 20 years and is still not fully completed; this process is reducing the compensation due to inflation.

In 2014, the PBOC issued the first draft bill as a sign of adoption of explicit deposit insurance. Zhou ( 2016 ) argues that the plan for the adoption of deposit insurance was part of a set of ongoing policies to modernize China’s financial market through reform and to better discipline China’s banking sector and enhance financial stability. Some example of these policies was the liberalization of interest rates and the designing of exit mechanisms for banks. The reform also coincided with a greater risk of banks encountering a liquidity crisis due to the slowing speed of economic development at the time. The growth of China’s shadow banking sector and the expansion of private lending also made adopting deposit insurance as a safety net as a priority to tackle the systemic risks building up in the financial sector. Before the introduction of explicit deposit insurance, China implemented a highly predictable implicit government guarantee against bank failures that left the Chinese people believing that they were highly protected (Yamori and Sun 2019 ). Zhou ( 2016 ) supports these arguments stating that China had been using implicit deposit insurance as the main mechanism to deal with failed banks. Accordingly, in the case of a bank run the PBOC intervened and implemented its “lender of last resort” mandate to pay for the deposits and debts of distressed banks. The introduction of deposit insurance made it clear that the government had abandoned the implicit protection of all deposits and started partial protections (Yamori and Sun 2019 ).

China’s current deposit insurance is under the control of the PBOC. It was set up as a management agency with access to RMB 10 billion (around USD 1.4 billion at the time). Membership by deposit institutions was mandatory, including the state-controlled banks, joint-stock banks, and foreign banks. The agency does not protect the deposits of foreign banks and foreign branches of domestic banks. Coverage is a maximum of 500,000 RMB (around USD 80,000). The current system uses a flat-rate premium; however, China envisages that a combination of a benchmark premium rate and a risk-based one will be used in the near future. Footnote 9 The resources of the insurance funds of the agency mainly come from the premiums paid by insured institutions (set at between 0.01 and 0.02%), properties received under liquidation of insured institutions, and income from the management of the fund. Footnote 10

3 Review of the Literature on the Effect of Deposit Insurance on Liquidity Creation

In this section we explain the theoretical underpinnings and the empirical evidence regarding the four channels (i.e. bank capital, excessive lending behavior, banking market competition intensity and monetary policy) identified by the previous studies. For each of these channels’ literature provides competing theories and, sometimes, conflicting evidence. Our main objective is to examine how the introduction of deposit insurance may influence and moderate the relationship between the channels and liquidity creation and to provide evidence to support the competing theories.

3.1 Through the Channel of Bank Capital

Studies have proposed two competing hypotheses, risk absorption and financial fragility , to explain the nexus between bank capital and liquidity creation. The risk absorption hypothesis posits a positive relationship (Bhattacharya and Thakor 1993 ), while the financial fragility hypothesis proposes a negative one (Diamond and Rajan 2000 , 2001 ). The Risk absorption hypothesis postulates that banks are more likely to create liquidity when they have higher levels of capital, as this position helps them to absorb more risk. In other words, all things being equal, higher capital leads to greater liquidity creation as the bank carries lower risk at a particular moment. Deposit insurance increases the confidence of depositors and other participants in the financial system in general but in particular in banks that reduces the likelihood of bank runs. Therefore, after the introduction of explicit deposit insurance, banking risks should decrease at the same rate as increased bank capital absorbs more risks. Banks, recognizing their less risky position due to deposit insurance and feeling safer, would choose to increase their liquidity creation. In other words, for a given level of bank capital, the introduction of deposit insurance would lead to more bank lending as it decreases the risk of deposit withdrawals. Footnote 11 In sum, the deposit insurance would positively reinforce the positive effect of bank capital on liquidity creation.

The financial fragility hypothesis postulates that liquidity creation decreases when banks have higher levels of capital. The reasoning is the bank’s ability to accumulate private information from depositors and borrowers over time. Depositors, concerned about possible abuse of their private information, become more vigilant about their deposits in the bank. As a consequence, banks may adopt a fragile financial structure in which they have a larger amount of liquid deposits to fulfill their commitment to depositors while buffering borrowers from depositors’ liquidity needs. In such circumstances, a higher level of capital increases the bargaining power of the bank and improves the credibility of its commitments. Having more capital, the bank does not need to maintain a fragile structure, or liquidity, in order to gain the trust of depositors. In other words, an increase in bank capital reduces financial fragility and, therefore, enhances liquidity creation. In this set-up, the availability of deposit insurance should reduce a bank’s need to maintain a fragile structure as depositors feel safer. Hence, the existence of explicit deposit insurance would mitigate the negative effect of capital on liquidity creation.

3.2 Through the Channel of Excess Lending

The lending business is the major income source for Chinese banks. Loan growth increases revenue as well as asset liquidity. However, greater lending (so-called “excess lending”) that exceeds the average lending of the bank’s peers could increase the level of credit risk and the amount of nonperforming loans over time. The liquidity spiral hypothesis, developed by Chen et al. ( 2015 ), posits that excess lending positively influences liquidity creation. It argues that excess lending is a consequence of underestimating the risks of projects that the bank is lending to and leads to loosening of lending standards. A bank with a large number of bad loans due to excess lending would decrease its cash inflow that would then aggravate the mismatch in liquidity maturities and then would increase liquidity creation.

Complementing this theory, we argue that in an environment where no deposit insurance exists the excess lending by a particular bank may lead to the withdrawal of deposits as some depositors may lose confidence in the bank. This withdrawal results in less liquidity creation as it alleviates the maturity mismatch and counterbalances the liquidity created by excess lending. The introduction of deposit insurance may have two moderating effects on the positive relationship between excess lending and liquidity creation. First, studies show that deposit insurance incentivizes banks to engage in the reckless behavior of more risky lending (Calomiris and Jaremski 2019 ; Demirgüç-Kunt and Detragiache 2002 ). Hence, the introduction of deposit insurance would stimulate excess lending and amplify its effect on liquidity creation. Second, having their deposits insured, depositors would be less likely to withdraw their funds even if they suspect risky behavior from banks.

Alternatively, the liquidity trade-off theory hypothesizes that the relationship between excess lending and the liquidity creation by banks is negative (Chen et al. 2015 ). It argues that the increased credit risk from excess lending pushes banks to reduce their liquidity creation in order to mitigate potential shocks. In such circumstances, deposit insurance could boost a bank’s confidence as the probability of runs is lower. Hence, ceteris paribus, having the protection of deposit insurance, a bank could increase its overall risk levels and could reduce its incentive to lower liquidity risk. In other words, the deposit insurance would mitigate the negative effect of excess lending on liquidity creation as posited by the liquidity trade-off theory.

3.3 Through the Channel of Bank Competition

The literature proposes opposing ideas regarding the relationship between bank competition and liquidity creation. The competition-stability argument hypothesizes that intensive bank competition increases the liquidity of banks (Boyd and De Nicolo 2005 ). Accordingly, in a competitive banking environment individual banks are less likely to have the monopolistic power to influence market rates. Hence, greater competition leads to lower lending rates and higher deposit rates, and such rates would boost the demand for deposits and loans in the economy, contributing to greater liquidity creation of banks. Introducing deposit insurance to a competitive banking market may favor larger banks in their possibly monopolistic positions. As in the case of China, implicit (i.e. too big to fail) and explicit deposit insurance may provide double protection for larger banks that cements their positions as dominant market players. It would be challenging for smaller banks to compete with larger banks given their wider branch networks. Overall, the presence of deposit insurance may give more advantage to larger banks, which then can control the lending and deposit rates that may reduce the positive effect of competition on liquidity creation.

In contrast, the competition-fragility hypothesis argues that bank competition reduces liquidity creation (Horvath et al. 2016 ) as intense competition could have a negative impact on the profitability of banks. Higher profits allow banks to absorb more risk. Hence, less profit would reduce banks’ capacity to absorb risk that in turn, would push them to reduce liquidity creation. The presence of deposit insurance in a banking system would also increase the chances of newcomers to the system as depositors would have more confidence in such banks. In a competitive market, smaller and newer banks would be more likely to have pricing power and the ability to take on risk, driving down the overall profitability of the banking market. As a result, less profitable banks would have a reduced capacity to create liquidity.

3.4 Through the Channel of Monetary Policy

Berger and Bouwman ( 2017 ) hypothesize that monetary policy has an influence on the liquidity creation by banks. They explain that loose monetary policy may increase deposits that expand the reserves of banks. An increase in bank deposits would then lead to an increase in loanable funds or decrease the cost of funds. Footnote 12 As a result, banks may respond by lending more that would lead to more liquidity creation. Assuming symmetry, the opposite effects can be predicted for tight monetary policy.

The literature has also hypothesized a link between deposit insurance and the effectiveness of monetary policy (Andries and Billon 2010 ; Cecchetti and Krause, 2005 ). Cecchetti and Krause ( 2005 ) develop an equilibrium model and predict that countries with explicit deposit insurance and state-owned banks with a high degree of assets have less credit in the private sector. They argue that the bank loans extended to the private sector decrease in the presence deposit insurance as a consequence of less efficient financial intermediation that has higher costs. Andries and Billon ( 2010 ) also theoretically show that having national deposit insurance leads to a heterogeneous change in the monetary policy. Their arguments are based on the lending channel of monetary policy (Kashyap and Stein 1993 ) where a monetary contraction entails a decline in banks’ reserves that leads them to scale down their lending. However, in the presence of deposit insurance banks benefit from a more stable deposit base that enables them to insulate their loan portfolios against a restrictive monetary policy. Cecchetti and Krause ( 2005 ) and Opiela ( 2008 ) provide supporting evidence for the arguments that deposit guarantees weakened the effect of monetary policy interventions for Poland and a multinational sample of 49 countries, respectively.

3.5 The Heterogeneous Effect of Deposit Insurance on Liquidity Creation of Big 5 State-Owned Banks and Others

State-owned banks, i.e. big 5, constitute more than 36% of the assets in the banking system in China (Amstad et al. 2020 ). Moreover, big 5 are crucial in implementing government’s economic policy. Hence, we argue that in China big state-owned banks have always benefited from an implicit deposit insurance. This is similar to the case of Russia, where Fungáčová et al. ( 2017 ) argue that state-owned banks, given their vital role in the economy, were always protected through an implicit government guarantee. In China, deposit holders and all other market participants deem the big 5 state-owned banks are too big to fail. Their expectation is for Chinese government to step in to bailout big 5 banks in case of a bank run or a default. Hence, we postulate that depositors and market participants expectations of the state-owned big 5 state-owned banks in terms of their riskiness might have been less affected by the implementation of an explicit deposit insurance in comparison to other banks. In contrast, other banks’ business would be more impacted by the introduction of the implicit deposit insurance scheme. To this extent, they are more likely to engage in liquidity creation after the introduction of the scheme. Hence, we expect that the direction and intensity of the effect of deposit insurance on liquidity creation to differ between state-owned big 5 banks and other banks and look for evidence in our empirical model.

4 Empirical Models

We examine the impact of the implementation of deposit insurance in China on liquidity creation ( LC ) for bank i in year t as follows:

where \(post2015\) captures the policy effect and equals one for the years after 2015 and zero otherwise. \({X}_{it}\) represents the measures for bank capital, excess lending, bank competition, and monetary policy. The \({Z}_{it}^{K}\) is a vector of control variables for bank characteristics and macroeconomic variables. K is the number of the control variables. All variables are explained in the following section. \(\beta\) and \(\lambda\) are the estimated coefficients for the main and control variables, respectively.

Considering that implicit deposit insurance for the big 5 state-owned banks, we set the group of big 5 state-owned banks as the control group (treat = 0), and other banks as the treatment group (treat = 1). The following DID regression compares the different effects of the two groups before and after the implementation of explicit deposit insurance on liquidity creation ( LC ) for bank i in year t as follows:

where \(treat\) is the dummy variable for the treatment group. The main explanatory variables are denoted by C, N, COMP , and MP and represent bank capital, excess lending, bank competition, and monetary policy, respectively. The \({\gamma }_{4}\) , \({ \delta }_{4}\) , \({\theta }_{4}\) and \({\sigma }_{4}\) indicate how the implementation of deposit insurance heterogeneously affects the influence of the channels on the liquidity creation of state-owned big 5 and others. Focusing on the significance and directional signs of these four coefficients, we test the causal effect of deposit insurance on liquidity creation. K is the number of control variables. All variables are explained in the following section. The \(\lambda\) is the estimated coefficients for the control variables.

5 Data, Variables, and Descriptive Statistics

5.1 sample and data sources.

Our dataset consists of 126 Chinese commercial banks and covers the period from 2011 to 2017. We exclude the local branches of foreign banks as these are not covered by the insurance. Footnote 13 We also exclude three policy banks Footnote 14 that do not have a commercial motive and the Postal Savings Bank of China which was classified as “other financial institutions” by the regulatory authority during our sample period. The final sample accounts for more than 80% of the total assets of the Chinese banking industry. The sample comprises five large state-owned Banks (i.e., the Bank of China, China Construction Bank, Agricultural Bank of China, Industrial and Commercial Bank of China and the Bank of Communication), 12 joint-stock banks, 77 city commercial banks, and 32 rural commercial banks. We collect the data mainly from the BankFocus database that we complement with the CSMAR and Wind databases as well as banks’ annual reports. The macroeconomic variables are calculated based on the data released by the National Bureau of Statistics of China.

5.2 Definitions of Variables

5.2.1 dependent variables.

We broadly follow Berger and Bouwman ( 2009 ) to construct the measures of liquidity creation. However, we argue that the classification of the accounting items capturing liquidity for US banks for on- and off-balance sheets is not fully replicable for Chinese banks. This is because the type of business in the Chinese banks and the consumption model of Chinese citizens are different from those in the US. Further, Berger and Bouwman ( 2009 ) use US bank call reports whose accounting items are different from the BankFocus database we use. There is no one-to-one match between the two databases. Therefore, we construct the measures of liquidity creation more in line with the characteristics of Chinese banks by following the three steps described in Sun et al. ( 2014 ).

In step 1, we classify the account items on- and off-balance sheet as liquid , semi-liquid , or illiquid based on the ease, cost, and time necessary for banks to turn their obligations into liquid funds and the ease, cost, and time customers need to withdraw liquid funds from banks. On the assets side, we classify the cash and transactive securities as liquid assets. We categorize residential real estate loans as illiquid assets in contrast to Berger and Bouwman ( 2009 ) who classify them as semi-illiquid. This is because mortgage-backed securitization is not widespread in China, and banks rarely securitized and sell mortgages for liquidity. In the same vein, we classify the items with high liquidation costs such as industrial and commercial loans , real estate investments , fixed assets , and intangible assets as illiquid assets. We classify consumer loans as semi-liquid assets due to their flexible structure and short maturity. We classify interbank loans as semi-liquid as they are large in size and have greater transparency, which makes converting them to cash easier than industrial and commercial loans.

On the liability side, we classify demand deposits and transactive liabilities as liquid. Time deposits are defined as semi-liquid liabilities due to the interest cost of withdrawing in advance. Similar to interbank loans, the interbank liabilities are classified as semi-liquid. Illiquid liabilities consist of the liabilities that are hard to withdraw and that are more expensive to cash out as well as subordinated debt, bank reserves, and other liabilities. Differing from Berger and Bouwman ( 2009 ), we classify savings deposits as semi-liquid liabilities instead of as liquid liabilities. This is because unlike US households, who have a weak attitude towards household saving and have a greater propensity to use income for current consumption, Chinese households are more inclined to save a larger proportion of their income to meet the future needs for big expenditures such as housing and education. This behavior reduces the liquidity of savings deposits in China. From the off-balance sheet items, we classify contingent liabilities (such as loan commitment and letters of credit) as illiquid as they are similar to industrial and commercial loans in terms of convertibility to cash. We categorize securitized assets and collaterals as semi-liquid as they are easily converted into cash despite the cost of liquidation. In general, our classifications of off-balance sheet items are consistent with those in Berger and Bouwman ( 2009 ); Table  1 displays them.

In step 2, we assign weights to all the categorized items. Illiquid assets, liquid liabilities, and illiquid off-balance items are assigned a weight of 0.5. The semi-liquid assets, semi-liquid liabilities, and semi-liquid off-balance sheet items are assigned a weight of 0. We assign the weight of -0.5 to liquid assets, illiquid liabilities, and liquid off-balance sheet items.

In the last step, we calculate the measures of liquidity creation based on the following Eq. ( 3 ). Following Berger and Bouwman ( 2009 ), we regard the liquidity creation per unit of assets as the main dependent variables. The main measures are total liquidity creation ( lc_ta ), liquidity creation on balance sheet ( lc1_ta ), and liquidity creation off-balance sheet ( lc2_ta ). Footnote 15

5.2.2 Explanatory Variables

To measure the capital levels of banks, we use capital adequacy ratio (car) , Tier 1 capital adequacy ratio (ccar) , and equity ratio (ea) as variables (Lei and Song 2013 ). The capital adequacy ratio is the sum of Tier 1 and 2 capital divided by the total risk weighted asset. The Tier 1 capital adequacy ratio is the Tier 1 capital divided by the total risk weighted assets. The equity ratio equals the total equity divided by total assets.

We use excess loan growth rate (elgr1 and elgr2) and relative loan (relative_loan) (Chen et al. 2015 ). The first measure for excess loan growth, elgr1 , is the difference between the growth rate of a bank’s loans in a given year and in the last year. The second measure, elgr2 , is the difference between the growth rate of a bank’s loans in a given year and the mean growth rate in the last two years. Relative loan is defined as the loans to total assets ratio of each bank minus the average loans to total assets ratio of all banks.

To measure bank competition, we use 5-bank concentration ratio ( cr5 ), Herfindahl-Hirschman Index ( HHI ), and size ratio . Measure cr5 is the size proportion of the big 5 in the full sample, which illustrates the degree of monopoly and market competitiveness. We calculate this variable for assets ( cr5_assets ), deposits ( cr5_deposits ), and loans ( cr5_loans ). HHI is the sum of the squared market share of each bank. We calculate the HHI based on assets ( hhi_assets ), deposits ( hhi_deposits ), and loans ( hhi_loans ). Size ratio is the market share of each bank divided by the size of the total market. We use total assets ( sl_assets ), total deposits ( sl_deposits ), and total loans ( sl_loans ) to calculate the market share.

As an indicator of monetary policy, we use deposit rate ( depositr ) that is the 1-year benchmark deposit rate, loan rate ( lendr) that is the 1-year benchmark loan rate, and deposit reserve ratio ( rmbdrr ). Definitions of all explanatory variables are presented in Table  2 .

5.2.3 Control Variables

Control variables are mainly composed of bank characteristics and macroeconomic indicators. Bank size ( size ) is measured by the natural logarithm of the bank’s total assets. Following Fungáčová et al. ( 2017 ), we use risk asset ratio , volatility , and ZSCORE as proxies for bank risk. The risk asset ratio is the risk weighted assets divided by total assets. Volatility is the standard deviation of ROAs for each bank for the whole sample period (Laeven and Levine 2009 ). ZSCORE is the sum of ROA and equity to total assets ratio divided by the standard deviation of the annual return on assets. We use the natural logarithm of the ZSCORE value. Bank profitability is represented by ROA ( roaa ) and ROE ( roae ). Bank governance variables consist of the ratio of deposits to asset ( bankgovernance1 ), net loans to total assets ( bankgovernance2 ), and growth rate of gross loans ( bankgovernance3 ) (Lei and Song 2013 ). We also use the dummy variable IPO ( ipo ) to capture whether a bank is listed on the stock market. The bank type ( banktype ) variable is a categorial variable that equals one for the big 5, two for joint-stock banks, three for city commercial banks, and four for rural commercial banks. The macroeconomic variables are annual GDP ( gdp ) and CPI ( cpi ) growth. All definitions are presented in Table  2 .

5.3 Descriptive Statistics

We present the descriptive statistics for all variables in Table  3 . We observe that Chinese banks’ average liquidity creation per dollar of assets is 0.160 USD of which 0.095 and 0.065 came from on- and off-balance sheet items, respectively. The big 5 are the major contributors to the liquidity creation in the Chinese banking industry with an average of 0.313 USD per dollar of assets. For the overall sample, the average capital adequacy ratio is 12.87% with the average Tier 1 capital ratio being 10.75%. The average excess loan growth is -1.10% ( elgr1 ) for one year and − 1.60% ( elgr2 ) for two years.

All of the bank concentration ratios, whether measured by assets, deposits, or loans, show that the monopolistic power of the big 5 weakened from 2011 to 2017. The big 5 concentration ratios are the highest around the year of 2011 (about 75.1%), and the lowest in 2017 (about 55.0%). Similar trends occur when the concentration is measured with HHI. The distribution of size ratio indicates that big and small banks have significantly large differences in their relative market shares. The average profitability of Chinese banks is 14.40% ( roae ) and 1.00% ( roaa ). In terms of risk measures, the average risk weighted asset and ZSCORE are 65.02% and 3.90, respectively. On the liabilities side, the average deposit ratio of Chinese banks is 68.82%. Finally, we observe that 35.82% of the banks are listed on the stock market.

6.1 Main Results

We present the results for the impact of deposit insurance through the channel of bank capital in Table  4 . We find that the coefficients for the interaction terms car×post2015 (interaction of deposit insurance period with capital adequacy ratio) and ccar×post2015 (interaction of deposit insurance period with Tier 1 capital adequacy ratio) are positive and statistically significant. These results show that the presence of deposit insurance positively reinforces the effect of bank capital on liquidity creation. This finding is plausible as bank risks decrease after the introduction of deposit insurance as the same level of bank capital absorbs more risks. Furthermore, the deposit insurance increases depositors’ confidence that reduces the banks’ need to have a highly liquid financial structure in order to maintain that confidence and establish a reputation. We do not find a significant coefficient for ea×post2015 . This finding may not be surprising as ea is not a ratio that could capture the true capital levels of a bank as capital adequacy is directly related to the risk that the bank is taking. In other words, the effect of deposit insurance on the relationship between capital and liquidity creation can only be observed through variables that are risk adjusted, such as car and ccar . In sum, our evidence shows that the introduction of deposit insurance can reinforce the positive effect of bank capital on liquidity creation and mitigate the negative effect of capital on liquidity creation. Moreover, we find the coefficients for car , ccar , and ea are negative (but not statistically significant) that is in line with Lei and Song ( 2013 ) and the predictions of the financial fragility hypothesis.

We present the results for the impact of deposit insurance through the channel of excess lending in Table  5 . We do not find significant coefficients for any of the interaction terms of elgr1×post2015 , elgr2×post2015 , or relative_loan×post2015 . Hence, we do not find any evidence on the effect of deposit insurance on the influence of excess lending on the liquidity creation by banks. We find positive coefficients for relative loan , elgr1 , and elgr2 ; however, only the first one is statistically significant that partly supports Chen et al. ( 2015 ) who finds that excess lending positively influences liquidity creation.

We present the results for the impact of deposit insurance through the channel of bank competition in Table  6 . We find that none of the interaction terms (cr5_assets×post2015, hhi_assets×post2015 , and sl_assets×post2015) are significant. Therefore, deposit insurance does not influence the liquidity creation through the channel of bank competition. Footnote 16 At the same time we find that the coefficients for cr5_assets and hhi_assets are positive and statistically significant. These findings are in line with the competition-stability argument of Boyd and De Nicolo ( 2005 ) that intense bank competition increases liquidity creation. On the other hand, sl_assets is negative and significant that supports the competition-fragility hypothesis of Horvath et al. ( 2016 ) that bank competition reduces the liquidity creation by banks. Footnote 17

We present the results for the impact of deposit insurance on liquidity creation through the monetary policy channel in Table  7 . We find that the coefficients for two ( depositr×post2015 and lendr×post2015 ) out of the three interaction terms are negative and statistically significant. The results show that deposit insurance reduces the positive impact of monetary policy on liquidity creation. These findings are in line with the literature arguing that deposit insurance reduces the effectiveness of monetary policy (Cecchetti and Krause 2005 ; Lin 2015; Opiela 2008 ). We also find that the coefficients for depositr , lendr , and rmbdrr are all positive and in some cases (Column 1 and 3) are statistically significant. These results confirm the arguments of Berger and Bouwman ( 2017 ) that loose (tight) monetary policy leads to more (less) liquidity creation.

We also simultaneously estimate some of the selected indicators, and the results are presented in Table  8 . We find that the coefficients for the interaction terms for bank capital and monetary policy channels are similar with the results reported above and are statistically significant. In general, the simultaneous inclusion of variables for all channels provides the same results.

6.2 State-Owned Big 5 Versus Other Banks

We present the results of the DID analysis in Table  9 in which we test whether the indirect impact of deposit insurance differs for state-owned big 5 that already had protection through an implicit deposit insurance before 2015. We use different combinations of the selected explanatory variables in the estimated regressions. The first set (Columns 1 and 2) comprises car , relative_loan , sl_assets , and depositr . In Column 2, where we run fixed-effects regressions and find that the coefficients for all interaction terms ( car×treat×post2015 , relative_loan×treat×post2015 , sl_assets×treat×post2015 and depositr×treat×post2015 ) are statistically significant. In the second set (Columns 3 and 4), the variables are car , relative_loan , sl_assets , and lendr . Similar to the first set of results, we report that all the interaction terms ( car×treat×post2015 , relative_loan×treat×post2015 , sl_assets×treat×post2015 , and lendr×treat×post2015 ) are significant in the random and fixed effects models. The third set of regressions (Columns 5 and 6) comprises ccar , relative_loan , sl_assets , and depositr . Here we report similar findings except that sl_assets×treat×post2015 loses its statistical significance in Column 6 in the fixed effect regressions. Overall, our results indicate that the influence of deposit insurance on the selected measures’ impact on liquidity creation is statistically different for the two groups (i.e., state-owned big 5 versus the others).

To justify our methodological choice, we also provide evidence for the implicit parallel trend assumption required for the DID set-up. In our context the control group and treatment group should share common trends before the introduction of deposit insurance in 2015. Accordingly, we use an event study to address the effects of the time trend presented in Fig. 1 . We observe that for all variables the coefficients of interactions before 2015 (left section of each graphic) are not significant. However, after 2015 these coefficients become significant that provides support for the underlying assumptions of the DID strategy. Footnote 18

Testing parallel trend assumptions. Note: The points in the graphs show the coefficients for the interaction variables (Variable*Treat*Year) and the vertical dashed line represents the confidence interval at the 95% level. We observe that for all variables, the coefficient for the interactions before 2015 (left section of each graphic) are not significant. However, after 2015 these coefficients become significant. Note that we cannot present data for the bottom right graph due to multicollinearity issues

Overall, these results show that deposit insurance more positively enlarges the effect of bank capital on liquidity creation in the other banks in comparison to state-owned big 5 banks. This is probably a consequence of the implicit deposit insurance that big 5 benefited from prior to 2015. In contrast, other banks were exposed to more systematic risk before 2015. Hence, they were influenced more by the implementation of the explicit deposit insurance. Regarding the excess lending (measured by relative_loan ), we find that the introduction of the deposit insurance more negatively affected the influence of excess lending on the liquidity creation by other banks in comparison to the big 5. Thus, an explicit deposit insurance reduces certain risks that creates more incentives for the big 5 to increase lending and liquidity creation. We also find some evidence that the negative effect of explicit deposit insurance on the influence of bank competition and monetary policy on liquidity creation is larger for other banks in comparison to state-owned big 5.

7 Robustness Checks

7.1 the liquidity creation on and off-balance sheet.

Earlier, we examined liquidity creation both on- and off-balance sheet. However, there may be differences between the two on how the variables of interest affect liquidity creation. In this section, we divide the total liquidity creation into those from on-balance sheet items ( lc1_ta ) and those from off-balance sheet items ( lc2_ta ). We present the benchmark results for the first three channels in Table  10 . We find that the results for car × post2015 and ccar × post2015 are similar to our original results. This similarity shows that the deposit insurance positively reinforces the effect of bank capital on liquidity creation both on-balance sheet and off-balance sheet. Additionally, this similarity is more pronounced for the balance sheet items. The coefficient for relative_loan × post2015 is only significant and positive for the on-balance sheet liquidity. This coefficient provides some evidence about the effect of deposit insurance on the influence of excess lending on on-balance sheet liquidity creation. The results for the monetary policy channel are presented in Table  11 . We find that different variables for monetary policy have heterogeneous effects on both on- and off-balance sheet liquidity creation. The deposit insurance only mitigates the effect of the 1-year benchmark deposit rate ( depositr ) and deposit reserve ratio ( rmbdrr ) on the off-balance sheet liquidity creation. On the other hand, the 1-year benchmark loan rate ( lendr ) has a negative influence on the impact of monetary policy on both on- and off-balance sheet liquidity creation.

Finally, we simultaneously include all channels in the models. The results, presented in Table  12 , show that the deposit insurance positively enhances the effects of bank capital and excess lending on both on- and off-balance sheet liquidity creation. On the other hand, excess lending’s effect is only observed for on-balance sheet liquidity creation. The deposit insurance has no effect on the influence of bank competition on both on- and off-balance liquidity creation. Finally, in terms of the impact of monetary policy on liquidity creation, the deposit insurance reduced its effect both on- and off-balance sheet.

7.2 Using Savings Deposits as an Alternative Measure for Liquidity Creation

In this subsection, we use an alternative measure for liquidity creation. As mentioned in subsection 4.2.1, we use Sun et al.’s ( 2014 ) rather than Berger and Bouwman’s ( 2009 ) definition of liquidity creation that is more suitable in the Chinese banking context. However, it may be a strong assumption that savings deposits are classified as semi-liquid rather than as liquid liabilities. Hence, for robustness checks, we also categorize this variable as in Berger and Bouwman ( 2009 ) and re-run our analysis. The results for the DID analysis are presented in Table  13 . We find that all the signs and significances of the coefficients, with minor changes in their magnitude, are similar to the findings reported in Table  9 . Overall, our results are robust to the change in the liquidity classification of savings deposits.

7.3 The Potential Impact of Other Regulatory Changes on the Results

We also check the robustness of our results regarding the potential impact of other regulatory changes introduced at the same time in China. In particular, the PBOC made changes in 2015 to reform the interest rate market. The banks raised the deposit rate ceiling to 120% of the 1-year benchmark deposit rate from the previous 110%, giving banks more room to price the deposit rates. To address the potential confounding effects of these two regulatory changes on our setting, we re-run the DID estimations by using the loan to deposit ratio ( ldr ) for the first regulatory change and the net interest margin ( nim ) for the second regulatory change. We report the results in Tables  14 and 15 , respectively. We do not find any significant effect of these changes on liquidity creation. We conclude that these other regulatory changes do not impact the robustness of our results.

8 Conclusions

We examine how the implementation of deposit insurance indirectly influences the liquidity creation by banks through the four channels identified by the literature. We exploit China’s introduction of deposit insurance as a natural experiment that uses the panel data of 126 Chinese banks. We also investigate the heterogeneous effects for different types of banks (i.e., the largest five state-owned banks versus other banks).

We find that the deposit insurance positively influences the effect of bank capital on liquidity creation. Our results also show that deposit insurance weakens the effect of monetary policy on liquidity creation. Regarding the excess lending and bank competition channels, our empirical evidence does not capture any effect of deposit insurance. Examining the possible different effects on the state-owned five largest banks and other banks, we find that the deposit insurance has heterogeneous effects on liquidity creation.

Our work has some policy implications. First, our findings indicate that countries that may consider introducing deposit insurance (or perhaps consider the less likely scenario of removing the existing deposit insurance) should consider the externalities caused by such a move on liquidity creation in the financial system. Second, the regulatory authorities may need to closely monitor the indicators for bank capital in order to evaluate the overall effect of deposit insurance on financial stability before introducing it. Moreover, they may need to closely monitor smaller banks as they are more likely to show excessive risk-taking after the introduction of deposit insurance that may have an impact on financial stability. Third, the monetary authority should be aware of the negative influence of deposit insurance on the impact of monetary policy on liquidity creation, and possibly adjust their policy instruments accordingly.

In contrast, Down ( 2000 ) theoretically demonstrates that deposit insurance is unnecessary and incapable of achieving a superior outcome when a financial intermediary has adequate capital.

Similar result are reported by Berger and Bouwman ( 2009 ) for small US banks, but not for medium and large ones.

Relatedly, earlier literature compares the deposit insurance scheme experiences of Denmark (Pozdena 1992 ) and Japan (Hall 1999 ; Fueda and Konishi 2007 ) to that of the US.

The main purpose of the system is to protect depositors’ legal benefits, prevent and resolve the financial risks, and maintain financial stability. The regulation (5th) stipulates that the highest reimbursement amount is RMB 500,000. Among Chinese depositors, 99.7% are below this threshold (The People’s Bank of China 2013 ).

These banks are the Industrial & Commercial Bank of China, the China Construction Bank, the Bank of China, the Bank of Communications, and the Agricultural Bank of China. The government and government-controlled entities are the majority shareholders who control more than 50% of shares in these banks. We followed the classification outlined by the China Banking and Insurance Regulatory Commission to identify the stated-owned banks. We classify the banks which are held or controlled by the government and state-funded financial firms. We exclude the policy banks in our sample as these banks are non-profit organizations.

For example, one of the first cases of bank closure was the Hainan Development Bank in 1998. The central government took over its all its debt, which was around USD 2 billion (Yan and Huang 2008).

The main role of this institution is to regulate the banking institutions through formulating supervisory rules and regulations, authorizing the establishment of banking institutions, examining and enforcing rules, encouraging better/proper governance, collecting information and finding resolutions.

In September 2011, the Banking Regulatory Commission announced that the bank had been restructured and renamed the Guangdong Huaxing Bank.

Pennacchi ( 1999 ) provides a detailed discussion on the relative merits of a targeting policy and a flat-rate insurance policy.

Zhou ( 2016 ) provides a detailed description of the duties of the agency and regulations governing the deposit insurance fund.

Our interpretation of the direction of the influence that the deposit insurance has on the relationship between bank capital and liquidity creation differs from Fungáčová et al. ( 2017 ). They argue, with respect to risk absorption, that deposit insurance can reduce liquidity creation because it reduces incentives for banks to prevent runs by owning more capital, therefore, reducing their ability to create liquidity.

Bergen and Bouwman ( 2017 ) explain that retail deposits are a cheaper source of funding in comparison to Federal Reserve funds or large corporate deposits.

The second clause of Chinese deposit insurance regulation stipulates that “All financial institutions absorbing deposits such as commercial banks, rural cooperative banks and rural credit cooperatives etc. should insure their deposits in accordance with the regulation. This regulation is not applicable to the branches of insured institutions established outside the People’s Republic of China (PRC) and branches of foreign banks established inside of PRC.”

These are the China development bank, the Export-Import Bank of China, and the Agriculture Development Bank.

The lc_ta and lc1_ta are equivalent to “cat fat” and “cat nonfat” in Berger and Bouwman ( 2009 ), respectively. We do not calculate the liquidity creation based on maturity as this data are not available in the sources we used.

In unreported results, we also calculate competition by using the size of deposits or loan size. The results do not change. These results are not reported for brevity and are available on request.

It is important to note here that sl_assets has a negative coefficient in comparison to cr5_assets and hhi_assets which have positive coefficients. This difference may be due to the fact the former is an indicator that captures a broader measure of banking competition as it is calculated for the whole sample period per bank. In contrast, the latter two variables are calculated per bank per year. Given that competition in the banking sector and banks’ positions in the system are medium to long-term phenomena, our view is that the findings for China are more supportive of the competition-fragility hypothesis.

We also test whether changes in post-crisis Basel (Basel 2.5) requirements for the largest banks in China may have affected our results as larger banks may behave differently. If large banks have responded to the new Basel requirements over the period of our analysis but small banks have not, then our DID strategy may not suffice as there could be some pre-existing trends already. To remedy this concern, we run mean comparison t-tests to examine whether there were differences between the responses of the large and small groups of banks to Basel 2.5 during the introduction of the deposit insurance. Due to the lack of accounting information disclosed by banks, we use the liquidity coverage ratio (LCR) that was introduced by new Basel requirements in 2014. We find that there is a high percentage of reporting by both groups (80% for large banks and 90% for all banks) in which banks started to release their LCR information after 2015. The results of the mean comparison t-tests, not reported for brevity, show that there is no statistically significant difference between the two groups.

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*The authors thank, in particular, Yusen Kwoh and Weihong Zeng for helpful comments and discussions. Our thanks also to participants at seminars held at the University of Leicester and University of Huddersfield. Xiangyi Zhou acknowledges financial support from the Chinese Fundamental Research Funds for the Central Universities (SK2020053). We remain responsible for all errors and omissions.

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Zhou, X., Li, X., Zhou, Y. et al. Deposit Insurance and Bank Liquidity Creation: Evidence from a Natural Experiment in China*. J Financ Serv Res (2024). https://doi.org/10.1007/s10693-024-00431-z

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Analytics in banking: Time to realize the value

Consider three recent examples of the power of analytics in banking:

• To counter a shrinking customer base, a European bank tried a number of retention techniques focusing on inactive customers, but without significant results. Then it turned to machine-learning algorithms that predict which currently active customers are likely to reduce their business with the bank. This new understanding gave rise to a targeted campaign that reduced churn by 15 percent.
• A US bank used machine learning to study the discounts its private bankers were offering to customers. Bankers claimed that they offered them only to valuable ones and more than made up for them with other, high-margin business. The analytics showed something different: patterns of unnecessary discounts that could easily be corrected. After the unit adopted the changes, revenues rose by 8 percent within a few months.
• A top consumer bank in Asia enjoyed a large market share but lagged behind its competitors in products per customer. It used advanced analytics to explore several sets of big data: customer demographics and key characteristics, products held, credit-card statements, transaction and point-of-sale data, online and mobile transfers and payments, and credit-bureau data. The bank discovered unsuspected similarities that allowed it to define 15,000 microsegments in its customer base. It then built a next-product-to-buy model that increased the likelihood to buy three times over.

Results like these are the good news about analytics. But they are also the bad news. While many such projects generate eye-popping returns on investment, banks find it difficult to scale them up; the financial impact from even several great analytics efforts is often insignificant for the enterprise P&L. Some executives are even concluding that while analytics may be a welcome addition to certain activities, the difficulties in scaling it up mean that, at best, it will be only a sideline to the traditional businesses of financing, investments, and transactions and payments.

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In our view, that’s shortsighted. Analytics can involve much more than just a set of discrete projects. If banks put their considerable strategic and organizational muscle into analytics , it can and should become a true business discipline. Business leaders today may only faintly remember what banking was like before marketing and sales, for example, became a business discipline, sometime in the 1970s. They can more easily recall the days when information technology was just six guys in the basement with an IBM mainframe. A look around banks today—at all the businesses and processes powered by extraordinary IT—is a strong reminder of the way a new discipline can radically reshape the old patterns of work. Analytics has that potential.

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Why? Three factors are coming together to kick off the coming heyday. First, consider advances in technologies . The availability of information is booming: in the past few years, the amount of meaningful data—true signal, not noise—has grown exponentially, while the size and cost of processors decreased. By 2020, about 1.7 megabytes a second of new information will be created for every human being on the planet. Businesses have opened their minds, freely adapting new analytical techniques that in the past might have been dismissed as too impractical and theoretical for the real world.

And those techniques have improved radically. We are well past simple linear regressions—machine learning now features support vector machines, random forests, gradient boosting, and many other astonishing algorithms. Any company’s ability to perform these analytics has been significantly boosted by the exponential increase of  computing power (which makes it possible to undertake, in just seconds, an analysis that in the past would have taken weeks) and by new data-storage technologies, such as Hadoop.

Second, banks in many regions are under enormous economic pressure . Our latest research finds that of the top 500 institutions around the world, 54 percent are priced below book value. In 2014 we calculated that just 18 percent of banks captured all the value in the industry. Recognizing this reality, banks have tried all manner of improvements, especially digitization and cost cutting. But these moves have taken them only so far; something new is needed.

Those digitization efforts underlie the third factor pushing analytics. Much of a typical bank is now digitized and throwing off data by the terabyte. A mostly manual bank would have serious difficulty using advanced analytics; at digital banks, the highways are already paved.

Put it all together, and you get advanced analytics: industrial-scale solutions to exploit data for authentic business insights and vastly improved decision making. The tools are there; banks must now carry them forward into actions that can drive meaningful change. The canvas is as broad as a bank itself. Rich real-time data—numbers, yes, but also text, voice, and images—now exist for literally every action that customers make, every product that banks sell, and every process that banks use to deliver those products. In this article, we will explore the vast opportunities, as well as the problems of integration and scaling that keep banks from making analytics  a coherent discipline. We will then suggest the strategic and organizational elements that banks need to realize the analytics dream.

Ready for prime time

As the saying goes, “The future is already here. It’s just not evenly distributed.” Banks—and companies in every other industry—are already deploying advanced analytics to move their businesses forward. We see three ways it can generate a meaningful increase in profits (Exhibit 1).

• Advanced analytics can help banks wring small improvements out of almost all their everyday activities, boosting the traditional P&L levers. Potential moves include the following:
• Accelerating growth, even in an anemic environment. Deeper and more detailed profiles of customers, together with transactional and trading analytics, can improve the acquisition and retention of clients, as well as cross- and upselling. For example, one bank used credit-card transactional data (from both its own terminals and those of other banks) to develop offers that gave customers incentives to make regular purchases from one of the bank’s merchants. This boosted the bank’s commissions, added revenue for its merchants, and provided more value to the customer.
• Enhancing productivity. Every banking process can become faster and more effective. Among other things, banks can use advanced analytics to provide faster and more accurate responses to regulatory requests and give teams analytics-enhanced decision support. One bank we know used machine learning to understand the way the characteristics of code affected a mainframe’s running time and the resulting costs; by optimizing the code, it cut them by 15 percent. Another bank used new algorithms to predict the cash required at each of its ATMs across the country, combining this with route-optimization techniques to save money.
• Improving risk control. Banks can lower their risk costs through analytics-aided techniques, such as digital credit assessment, advanced early-warning systems, next-generation stress testing, and credit-collection analytics. The expense of compliance and control has soared in recent years, and banks can use analytics to get economic returns from their considerable investments. We estimate that G-SIBs can take out up to $1 billion a year in costs through a simplified portfolio of data repositories—building on work that most banks have already done—and through new analytics that produce more accurate regulatory reports and deliver them more quickly. D-SIBs can save up to $400 million annually. Further out, banks will be able to use analytics to reduce fraud losses.
• A second vector of impact is the way that analytics can help deliver the promise of digital banks and offer a much better customer experience at a fraction of the current cost. In some regions up to 65 percent of customers now interact with their banks via multiple channels. Their paths through them are extraordinarily complex: they often start in one channel, perform intermediate steps in others, and finish in yet another—with plenty of pauses and information-gathering loops along the way.  Successful digital banks deliver a truly seamless multichannel experience by gathering real-time data and using analytics to understand the customer and build the proper (and always consistent) journey view.
• Finally, analytics can help banks find new sources of growth , and even new business models. Banks may be able to reap income from their data—for example, by sharing customer-analytics capabilities with new ecosystem partners, such as telecom companies or retailers. Taken to a logical but not implausible extreme, banks can use data and analytics to shape a new business model and out-fintech the fintechs. The bank as data company can sit at the center of a consumer ecosystem where the revenue pools include not just banking but also many other B2C and B2B businesses. Great analytics isn’t the only requirement here: banks must get many other things right to be relevant to and trusted by customers. But that can be done, and already more than a dozen leading banks are taking positive steps in this direction.

Not without pitfalls

In our recent survey, 1 1. We interviewed executives at 13 global and regional banks based in ten countries across Europe and the Middle East. we found that almost every bank lists advanced analytics among its top five priorities. Most plan to invest further in these techniques. A few banks are already seeing the rewards. These leaders have built substantial foundations by establishing data lakes and centers of excellence and using machine-learning techniques. They and many others have spent hundreds of millions on their data (especially risk data) and on compliance. For them, advanced analytics is becoming a reflex action, with commensurate rewards of about €300 million in additional annual profit, on average.

A digital crack in banking’s business model

Most banks we surveyed, however, are struggling. A good many are “started but stuck”: they have invested significantly in data infrastructure (mostly as a result of regulation) and experimented with advanced-analytics techniques (mostly through specialized teams loosely connected to the corporate center). But the expected results have not arrived. A few banks have yet to begin.

The started-but-stuck ones are running into a number of problems. At the highest level, their efforts remain unconnected and subscale; they have not yet tied together their disparate efforts into a single, unified business discipline. Tactically, we see banks making unforced errors such as these:

• not quantifying the potential of analytics at a detailed level
• not engaging business leaders early and to develop models that really solve their problems and that they trust and will use—not a “black box”
• falling into the “pilot trap”: continually trying new experiments but not following through by fully industrializing and adopting them
• investing too much up front in data infrastructure and data quality, without a clear view of the planned use or the expected returns
• not seeking cooperation from businesses that protect rather than share their data
• undershooting the potential—some banks just put a technical infrastructure in place and hire some data scientists, and then execute analytics on a project-by-project basis
• not asking the right questions, so algorithms don’t deliver actionable insights

Making it happen

A quick take on the cio agenda.

As noted, analytics does not necessarily require a big investment in IT. But banks must provide the technologies and tools that businesses need to access an immense set of high-quality data in real time. That data must be well managed and always available. These technologies include the following:

• Data lakes to scale up and industrialize the development and delivery of use cases. There are good and bad ways to use data lakes; we’ve seen better results when they are developed with agile techniques based on use cases. At all costs, banks should avoid building another enterprise data warehouse or asking data lakes to do more than they really can. Data lakes will not unburden the banks from proper governance and quality-assurance processes, but they do offer an opportunity to get things right from the beginning.
• Machine-learning techniques, which have proved superior in many use cases—the industry will inevitably move toward machine learning supported by open-source languages. Machine-learning tools are already available in most banks but are not used to solve very specific problems. (If they are not available, there is a large market of rapidly moving open-source technologies and commercial software products.) Banks need to keep their options open to the vast innovations taking place in machine learning.
• Google-like search engines provide quick and easy access to all of a bank’s data, including the source, definition, and all other information needed to use the data effectively.
• Modern data-exploration and -visualization tools are helpful to bridge the gap between advanced business users identifying potential use cases, anomalies, or patterns in the data, on the one hand, and data scientists using advanced analytics to predict and optimize, on the other.
• Tools to analyze text, voice, video, and images can help to process new data structures—specifically, unstructured data, such as voice and text. For example, analyzing live-chat data has great business potential for retaining customers or for next-best-product-to-buy analyses.
• Capabilities to leverage real-time data are important. Deploying analytical models in live streams can help embed analytical advice and predictions into customer dialogues. For example, most banks have already added chat capabilities to call centers. Now, banks can add analytics-driven recommendations to the APIs that support chat to insert a “next best” suggestion to customers.

Avoiding the pitfalls and accessing the broad set of opportunities requires CEO leadership as banks develop two assets: a strategy for the transformation and a robust analytics organization to assist and empower the businesses as they learn to use analytics in their everyday work. (New technologies and tools are also necessary; see sidebar, “A quick take on the CIO agenda.”)

Readers may notice something that’s missing from this list: setting the aspiration. That’s because we think every institution, unless its circumstances are extraordinary, should set the same aspiration: to establish analytics as a business discipline—the go-to tool for the thousands of decision makers across the bank. Earlier we mentioned analytics as a reflex. To extend the metaphor, analytics should resemble the human nervous system; every part of the body knows what to do when presented with certain stimuli. The big difference among banks will probably be the pace at which they can build and train their systems. Just as some parts of your brain are trained and some are not, banks will find that some nerve paths are already working well, while others must be laid down and taught how to react.

There’s something else missing from our list of required assets: an additional $100 million or so of spending. Many bank leaders look at analytics and fear an outsized investment. That’s not unreasonable, since in recent years institutions have had to spend billions on things they could not have anticipated, and budgets are very thin. But analytics is not a bet-the-bank investment with no graceful exits; it’s a short-cycle flow of investments with lots of options to kill unsuccessful pilots. The small but immediate payoffs from the initial work can finance the next wave of projects, which in turn finance more and larger efforts. Once the system is built, the investment is over and the margins become enormous—like those of software or tech companies.

A clear strategy centered on high-priority applications

Three elements are essential to the strategy. First, banks need an analytics-ready mind-set . Analytics transforms everyday work in surprising ways, so leaders must open their minds to the possibilities. Our core beliefs about advanced analytics can help. 2 2. For more, see Helen Mayhew, Tamim Saleh, and Simon Williams, “ Making data analytics work for you—instead of the other way around ,” McKinsey Quarterly , October 2016.

• Great analytics starts with high-value questions, not data. To guide the discovery process, ask what problem you want to solve and how much value the solution can create. Do not launch yourself into analysis for the sake of analysis or into intellectually interesting problems whose solutions are not actionable.
• The smallest edge can make the biggest difference. Advanced analytics is not about solving your biggest problems; it’s about solving hundreds of small ones that all add up. Especially in operations, these techniques can help to redefine processes and shorten them by several steps.
• Insights live at the boundaries between data sets. Remember the bank that combined six or seven discrete data sets to build a tool predicting the next product to buy? It realized that lots of relationships become apparent only when it compared widely varying parameters. Banks have massive amounts of data scattered through different departments. Pilots to bring together small samples of information can reveal the potential.
• Loops beat lines every time. Following a process can be a slog, and to do so you sometimes have to put on blinders. But that mentality is exactly wrong for advanced analytics. Banks that use feedback loops can not only be faster to market than competitors but also arrive there with better products. Ultimately, machines learn just as we do: by trial and error.
• Design matters. You want people to use your new tools. A beautiful algorithm deserves an attractive package that catches the eye of your users. Most of them can’t read code or understand the output of a model. To act on these insights, they need easily readied and used dashboards that help them make decisions and test potential scenarios.
• Analytics isn’t enough; adoption is essential. Use whatever means necessary—incentives, role modeling, communication, more communication—to get decision makers to use the new tools. Way too often, best-in-class algorithms sit idle in computers because users do not trust what they regard as a black box, fear the impact it could have on their roles, or simply do not want to go through the discomfort of change. Creating analytics is like putting jet fuel in your car. At the end of the day, if the driver does not develop the skills needed to drive faster, the effort is wasted.
• Analytics is a team sport. The skills banks need to make analytics work cannot be contained within a single person—at least not yet. Your teams must include true experts on data science, engineering, data architecture, and design. Faking it with people who do a little bit of everything won’t work.

A second element of the strategy is a set of prioritized use cases and a mechanism to create a pipeline of them. The scope for analytics is vast. Anywhere a bank uses rules of thumb or something is done “the way we’ve always done it,” analytics can probably make improvements. The CEO must lead the hunt for these issues and help prioritize them. Critically, at the beginning, the chosen use cases should not be limited to applications in which analytics could produce a substantial uptick in results; they should also include areas where scale can be increased quickly, to avoid the “pilot trap.”

Most of the potential use cases are relevant to every banking business. They include commercial applications: cross-selling and upselling, customer acquisition, reducing churn, and winning back customers. Business-improvement levers (such as dynamic and value pricing, credit underwriting, sales-area planning, yield and claims management, fraud detection, call-center routing, and workforce planning) are also relevant for most banks. While the first couple of use cases can be introduced top-down or outside-in, it is just as important to encourage everybody in the bank to become creative and make suggestions—while always ensuring a clear path to creating value. To avoid discouragement, long validation and delivery cycles need to be shortened. Innovation labs can help accelerate the process.

Finally, a strategy should set out a vision for how the bank will use analytics applications. For each use case the bank is considering, it should start by asking what problem holds back the business from having a greater impact. It can then work through a set of five steps: identifying the source of value, considering the available data (easier to do with a data lake, as we describe in the sidebar), identifying the analytics technique that will respond to the problem and probably produce insights, considering how to integrate analytics into the workflow of the business, and anticipating the problems of adoption (Exhibit 2).

Will sellers use the tools? If not, why not? What are their needs, and how can you make the analytical tools responsive to them? Sometimes the answer involves bundling insights from algorithms with useful data for sales managers in an app that they can use on external visits. Other times, a bank will have to change the way it develops campaigns and pushes them to the front line and to customers. Finally, in many other cases the bank will have to develop a group of high-performing champions who embrace this discipline and act as role models.

Embedding analytics in the organization is not simply a matter of getting specific teams to use specific tools, though that’s essential. The CEO and the top team must do much more to communicate clearly that analytics is important to the bank and empower everyone to join the revolution. The  classical steps of successful change management will be essential: role modeling the new behavior, clearly explaining why change is needed, building the skills of the businesses so they can succeed with the new tools, and reinforcing the bank’s commitment through formal mechanisms (such as incentives).

A powerful analytics network

The businesses will need help to design analytics systems, to build applications exploiting them, and to promote adoption. Banks will want to establish a central team that supports these needs. But the last thing they should do is build another silo. What they require is a networked structure, a kind of nervous system. More than anything, banks must have open channels and accessibility to make a knowledge of analytics pass freely throughout the enterprise. An analytics center of excellence, the spine of such a system, will probably need some or all of the following components:

• New roles and responsibilities in data management, advanced analytics, and campaign execution. Perhaps the most critical role is that of a chief data officer responsible not only for analytics strategy and its integration into business units but also for defining data management’s roles and responsibilities, monitoring the quality of data, and ensuring regulatory compliance.
• Imaginative channels, such as innovation labs, to bring analytics closer to users.
• A deep pipeline of analytics talent, cited as a top priority by 60 percent of the banks in our recent survey. Banks should typically start with small teams of data scientists, who can work with external partners to absorb the necessary competences and skills, and then scale up gradually.
• A clear governance plan with a strong center of excellence (headed by the chief data officer) and well-defined responsibilities for the governance and quality of data and for the execution of analytics within the businesses.
• “Gold standard” data-management processes that define clear accountability, maintain the quality of data, and manage metadata, the data life cycle, and controls.
• Bulletproof data-quality controls, starting at the very beginning of the data life cycle. They should be automated wherever possible and monitored by a set of key quality indicators, so that the businesses are accountable for collecting the right data.

More than 90 percent of the top 50 banks around the world are using advanced analytics. Most are having one-off successes but can’t scale up. Nonetheless, some leaders are emerging. Such banks invest in talent through graduate programs. They partner with firms that specialize in analytics and have committed themselves to making strategic investments to bolster their analytics capabilities. Within a couple of years, these leaders may be able develop a critical advantage. Where they go, others must follow—and the sooner the better because success will come, more than anything else, from real-world experience.

Amit Garg is a partner in McKinsey’s New York office, Davide Grande is a partner in the Milan office, Gloria Macías-Lizaso Miranda is a partner in the Madrid office, and Christoph Sporleder is a senior expert in the Frankfurt office, where Eckart Windhagen is a senior partner.

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