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Published: 18 February 2021

Neural mechanisms of credit card spending

Sachin Banker 1 , 2 ,
Derek Dunfield 2 ,
Alex Huang 2 &
Drazen Prelec 2 , 3 , 4 , 5

Scientific Reports volume 11 , Article number: 4070 ( 2021 ) Cite this article

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Human behaviour
Neuroscience

Credit cards have often been blamed for consumer overspending and for the growth in household debt. Indeed, laboratory studies of purchase behavior have shown that credit cards can facilitate spending in ways that are difficult to justify on purely financial grounds. However, the psychological mechanisms behind this spending facilitation effect remain conjectural. A leading hypothesis is that credit cards reduce the pain of payment and so ‘release the brakes’ that hold expenditures in check. Alternatively, credit cards could provide a ‘step on the gas,’ increasing motivation to spend. Here we present the first evidence of differences in brain activation in the presence of real credit and cash purchase opportunities. In an fMRI shopping task, participants purchased items tailored to their interests, either by using a personal credit card or their own cash. Credit card purchases were associated with strong activation in the striatum, which coincided with onset of the credit card cue and was not related to product price. In contrast, reward network activation weakly predicted cash purchases, and only among relatively cheaper items. The presence of reward network activation differences highlights the potential neural impact of novel payment instruments in stimulating spending—these fundamental reward mechanisms could be exploited by new payment methods as we transition to a purely cashless society.

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Introduction.

Since their introduction in the 1960s, credit cards have gradually replaced cash and check transactions as the default payment method for consumer purchases, and are now the fastest growing method in the United States 1 . In the future credit cards may find themselves overtaken by digital wallets and other devices. From an economic perspective, it is not surprising that technological changes in payment transactions have some impact on macroeconomic variables, notably on U.S. household debt, which has been steadily rising over the last two decades 2 , 3 . This historical debt increase may be, in part, a rational household response to new lines of credit and to the other benefits of credit cards, in terms of convenience, security, and reward points.

However, evidence is accumulating that suggests credit cards take advantage of cognitive biases and other psychological mechanisms. Many, if not most consumers overestimate their future ability to repay and are surprised by the high interest charges when these come due 4 , 5 , 6 . Empirical studies show that shoppers with credit cards are willing to spend more on items 7 , 8 , check out with bigger baskets 9 , focus on and remember more product benefits rather than costs 10 , 11 , and make more indulgent and unplanned purchase choices 12 , 13 .

Do credit cards then serve to “release the brakes” on spending or instead act to “step on the gas”? Prior evidence indicates that, in fact, both mechanisms may be involved, such that spending facilitation effects are likely to be driven by combination of these processes. For instance, most relevant to this paper are reports that mere exposure to credit card logos can stimulate spending 14 , 15 , 16 . As first argued by Feinberg 14 , spending facilitation via mere exposure implicates classical conditioning mechanisms and cue-triggered cravings associated with addiction 17 , 18 . Salient cues can often trigger a motivational urge to pursue its reward, such as the pleasure associated with consumption—in this way fueling greater spending.

Yet, recent literature has focused greater attention upon an alternative mechanism derived from the mental accounting literature. That is, credit cards may instead weaken brakes on spending by lessening the pain associated with making payments. This “pain-of-payment” hypothesis was originally proposed in a metaphorical sense 19 , 20 , however more literal interpretations have also taken root more recently. With credit card purchases, the act of payment is temporally removed from the act of acquisition, and is further decoupled when multiple transactions, perhaps spread over many months, are represented as a single consolidated balance. This dissociation of purchasing from payment may put costs out of mind and reduce the influence of price on product purchase decisions.

Understanding the brain mechanisms that are responsible for these effects is important, as they are not likely to be confined to credit cards only. By tapping these mechanisms, any new payment technology can disturb old expenditure patterns in ways that people fail to anticipate, and may come to regret.

In this exploratory study, we provide the first evidence of differences in brain activation in the presence of real credit and cash purchase opportunities, presented in an fMRI shopping task. Participants used their own personal credit card or cash funds to make real purchases of products while we simultaneously observed brain activity. Our study focuses on the purchase of everyday products with cash and credit at relatively small dollar values, similar to those examined within prior literature on payment methods. We find that activation in the classical reward networks (the striatum) differentiates credit card purchases from non-purchases, and, importantly, bears little relation to price. In contrast, activation in these same networks is a weak predictor of cash purchases, but interacts with price to predict purchases of cheaper instead of more expensive items. Activation in the insula, a brain region previously linked to pain-of-paying 21 , 22 , 23 , 24 , does not differentiate credit from cash purchases in our study.

As we discuss in the conclusion, we cannot rule out that a reduction in pain-of-paying is responsible for credit card overspending at higher dollar amounts than those used in the study. However, our results suggest that classical cue-conditioning and the resulting sensitization of neural reward networks may have a separate role in motivating credit card purchases. Even if credit cards do “release the brakes” on spending, as argued by mental accounting, it appears that they could also help to “step on the gas.”

To facilitate comparisons with previous results, our study builds on the established SHOP (“Save Holdings Or Purchase”) fMRI paradigm 21 , 22 , 25 . In the task, participants make a series of purchase decisions for products offered at a steep discount relative to market price. A trial begins with a screenshot of a product that the participant has not seen previously in the study, followed by the product price, and concludes with a “buy” versus “no-buy” decision screen. Neural signals in the task have been shown to dissociate reward-related from price-related decision processing 21 . For this reason, it is a natural protocol for assessing competing hypotheses about credit card purchase facilitation mechanisms.

In the SHOP task, a decision to buy is marked by three neural signals (see Fig. 1 for SHOP trial structure; Fig. 2 top panel for activation pattern in the original SHOP study). Two signals come from the classic dopaminergic reward network involving the striatum and the ventromedial prefrontal cortex (VMPFC). Striatal activation is a leading predictor of purchase, appearing during the product and price screens, but losing significance by the decision point. Activation in the VMPFC predicts purchase during the price presentation and decision points, and also correlates with post-scanner estimates of consumer surplus (defined as the difference between stated willingness-to-pay for a product and its price). Thus, VMPFC activity has been interpreted as a net-value signal within a range of decision making contexts 21 , 22 , 25 , 26 , 27 , 28 .

Shopping task trial structure in the current study. Participants viewed the product for 4 s, the payment method for 4 s, the price for 4 s, and then made a choice to purchase within 4 s. Post-decisional periods consisted of a confirmation, 4 s, and a pay response, 4 s. Purchase trial shown; if not purchased, the confirmation indicated “basket unchanged” and the pay screen indicated “no payment necessary.” Intertrial interval jittered 2–8 s. This study added the Method, Confirm, and Pay phases to the original SHOP paradigm.

Comparison with Knutson et al. 21 . Neural activation time courses in the striatum, VMPFC, and rAIC distinguishing purchase (black) from non-purchase (grey), y-axis labeled with percent signal change. Above: Fig. 2 from Knutson et al. 21 . Below: time courses from the current study collapsed across payment methods. Phases: * = product, M = method, $ = price, ? = choice, C = confirm, P = pay.

A separate neural indicator of product purchase is reduced activity in the right anterior insula cortex (rAIC), when the price appears 21 , 22 , 25 . Because the rAIC has been previously implicated in the processing of negative emotions and pain 29 , 30 , 31 , 32 , 33 , 34 , its activation in the SHOP task has been interpreted as evidence consistent with a “pain-of-paying” caused by high price, acting as a brake on spending 21 , 22 . Paying for products has been thought to elicit an affective pain experience associated with activation in the anterior portion of the right insular cortex, in contrast to the posterior portion of the right insular cortex which has been linked to representation of physical pain experiences 23 .

Behavioral findings

The independent variables, product price and payment method, had the expected effects on purchase behavior in the fMRI shopping task. A hierarchical logistic regression predicting purchase decisions yielded parameters on price ( b = − 0.334, se = 0.116, p = 0.004), payment method ( b = − 0.036, se = 0.099, p = 0.715) and their interaction ( b = 0.251, se = 0.120, p = 0.037) in the anticipated direction. This interaction follows predictions based on a prior test conducted in a similar context 35 . Consistent with previous empirical studies 7 , 8 , participants were more willing to purchase higher-price items with credit rather than with cash, and thus they spent more overall when using credit card (average basket = $87.41, SD = 61) rather than cash ($84.19, SD = 51). These behavioral findings supported the notion that credit cards facilitate purchasing behavior, and our analysis presented below focuses primarily on the associated neural activation evidence.

Neural activation

The current fMRI design follows the approach in the original SHOP article 21 . For comparison, Fig. 2 displays the activation time course in the current study alongside the Knutson et al. 21 results. Collapsing across payment methods, the time course in each region of interest (ROI) tracks the original results to a remarkable degree. The key regions of interest—striatum, VMPFC, and rAIC—are shown graphically within Fig. 3 . While we do not consider the current findings to be an exact replication of the original SHOP results, the neural activation patterns from the earlier study provide a benchmark reference, as discussed below.

Regions of interest examined within the current study. Ventromedial prefrontal cortex shown in green and striatum shown in blue, from Bartra et al. 26 meta-analysis; right anterior insular cortex (rAIC) shown in red, from Kelly et al. 34 parcellation analysis; MNI x = − 6, y = 10, z = − 6.

Figure 4 breaks apart the time courses by payment method, and shows that the reward network differential buy signal is clearly present with credit card purchases, but is negligible with cash purchases. Logistic regressions of the purchase decision on the ROI signal change, payment method, and their interaction confirms that credit purchases were associated with greater differential striatal activation, beginning with the payment method screen and extending up until the decision screen (shown in the bottom panel of Fig. 4 ). The same pattern holds directionally but not significantly, for the VMPFC. However, if the neural signal in each ROI is collapsed across buy and no-buy decisions, there is no significant difference between credit card and cash trials, at any time point, suggesting that presentation of the credit card stimulus per se does not affect brain activity in the target ROIs.

Above: ROI signal intensity time courses illustrating purchase (black) versus non-purchase (grey). Below: Buy decision regressed on ROI signal intensity, payment method, and interaction at each TR. Red indicates a negative coefficient. Parameter significance denoted by *** p < .001, ** p < .01, * p < .05, ^ p < .10. Phases: * = product, M = method, $ = price, ? = choice, C = confirm, P = pay.

Looking at the cash trials only, the reward signals are weaker predictors of purchases than in the original SHOP task 21 , even though the earlier study also required cash payments. However, in Knutson et al. 21 , participants tapped their experimental endowment—money they did not have before the study—potentially creating a house-money effect. In contrast, participants in the current study paid out-of-pocket with the $50 in cash they brought to the experiment.

As evident in Fig. 2 , collapsing across payment methods in the current study reveals activation time courses that track the Knutson et al. 21 results. This appears to be primarily due to purchase decisions using a credit card, not cash. Accordingly, comparing the current findings to past SHOP results indicates that credit card purchase decisions resemble house-money purchases. Thus, one interpretation of these findings is that when shopping with credit card, individuals act as if drawing on an endowment (from the financial institution backing the card).

An additional analysis shows that prices modulate the association of neural signals and the decision to purchase. The y-axis in Fig. 5 displays the differential purchasing signal. That is, we take the average ROI activation on purchase trials and the average ROI activation on non-purchase trials, and plot the difference between these means; this is plotted separately for high-price items and for low-price items, when using cash and when using credit (see Figure S2 in Supplementary Information for further information). Accordingly, points plotted at the zero line indicate that neural activation did not differ between purchase and non-purchase decisions on average. Points plotted above the zero line instead indicate that purchases were associated with greater activation in the ROI relative to non-purchase decisions (and conversely for points below the zero line). The significance levels in the table in Fig. 5 come from logistic regressions of the buy decision on ROI signal change and its interaction of signal with item price (a continuous variable).

Above: y-axis plots the difference between average purchase and average non-purchase ROI signal intensity, for high-price (black) and low-price (grey) items by payment method. Below: Buy decision regressed on ROI signal intensity and the interaction between price (continuous) and ROI signal intensity at each TR, separately for credit and for cash. Red indicates a negative coefficient. Parameter significance denoted by *** p < .001, ** p < .01, * p < .05, ^ p < .10.

Focusing on decisions using cash, positive reward-related ROI activation in the striatum was associated with purchasing only among lower-priced items, and this differential purchasing signal is near zero for higher-priced items (see the left panel in Fig. 5 ). Confirmed by the interactions in the regression analysis, buying items with cash has a price-dependent neural signature that is clearest in the striatum. In contrast, the neural signature associated with credit purchases is not price-contingent, and is instead reflected by differential activation in reward-related ROIs, regardless of the price. Regression analyses that directly compare the differential sensitivity to price when using cash and credit are reported within the Supplementary Information; these findings suggest that credit cards reduce sensitivity to price information via heightened striatal activation, exhibited during the periods in which product price is presented to participants.

Although a single experiment is rarely definitive with respect to behavior outside of the lab, the results reported here provide clear clues about the neural mechanisms that differentiate credit card from cash purchases and that may be implicated in credit card overspending.

A leading hypothesis within recent literature is that credit cards facilitate purchasing by diminishing a pain-of-payment that would otherwise keep spending in check. The intuition behind it is that card transactions “decouple” (disassociate) payments from consumption 19 , 20 . The decoupling occurs because the payment is delayed, can be postponed repeatedly, and the actual repayment date may be ambiguous if diverse expenditures are lumped into a rolling balance. Decoupling of payments from consumption allows people to keep the cost of the item “out of mind,” creating a kind of analgesic at the moment of purchase.

We do not find neural evidence for this explanation, at least if pain is defined as a physical sensation and insula activity treated as its neural marker, as has been suggested in the past 21 , 22 , 23 . Although insular activation does differentiate purchase from non-purchase decisions, it does so only after the decision point, and does not clearly interact with either payment method or item price (Figs. 4 , 5 ). Insular activation seems to reflect simple product rejection in our study, perhaps similar to the rejection of bad offers in economic games 36 , 37 , 38 . Yet, our evidence is consistent with the more metaphorical interpretation of the pain-of-payment account. That is, while we did not observe credit cards to influence pain processing networks in the brain, our evidence did indicate that price information failed to have any modulating influence on neural mechanisms associated with credit card purchases (i.e., costs were out of mind).

At the same time, there are a number of important constraints within the current study that offer worthy directions for further exploration. For instance, it is possible that spending cash could elicit stronger negative affective responses at higher price levels than those examined within the current study. Some interesting exploratory research suggests that observing others make cash payments at higher price levels is associated with increased activation in the insula 24 . As applied within prior literature, our study design also mimics typical retail shopping environments in which participants add items to their basket and subsequently checkout (rather than parting with money at the moment the purchase decision is made) which may diminish the salience of cash payments. Furthermore, in conveying the payment method to participants, we also used an icon that included both Visa and Mastercard logos; additional research could help to clarify the role of brand logos in eliciting spending facilitation effects. As participants in this study had reasonable levels of financial literacy, additional research focusing on consumers with lower, or higher, levels of financial literacy and experience would be valuable to pursue. Additionally, while our study aimed to stick closely to prior SHOP tasks, more highly powered designs could offer greater insight into the role of the insula.

Taken altogether, the hypothesis that gains most support from the current evidence is that the reward network—the striatum in particular—has been chronically sensitized by prior experience with credit cards. In line with cue-triggered accounts of cravings, exposure to conditioned credit card cues may trigger sensitivity to rewards 14 , 17 , 18 , 39 , 40 . Such sensitization would show up in a reward anticipation increase following onset of the credit card logo in expectation of an imminent buy decision, a pattern that we indeed observe within striatal activity. Under this hypothesis, credit card cues may in part activate the pursuit of rewarding products rather than merely alleviating the pain associated with paying for them.

The difference in reward network activation between credit and cash conditions is notable in light of the small prices and modest behavioral effects. Self-reports taken after the shopping task suggest that participants were largely unaware of the influence of payment methods on their decisions, disagreeing with statements that they were more impulsive and less price-conscious when shopping with credit cards (see Supplementary Information). The differences in reward-related neural purchasing signals observed between payment methods do not appear to reflect inconveniences in using cash itself; indeed, prior SHOP studies examining cash purchases 21 documented similar reward-related neural purchasing signals, so long as participants were spending house-money from an experimental endowment. Further research could help to clarify the extent to which consumers consider shopping with credit card to be akin to spending house money. We do find that the impact of credit cards on behavior (purchase likelihood) and neural activity increases with price. Extrapolating on this price-related trend, one might expect greater credit card effects for big-ticket items in an actual marketplace.

Although recent literature largely interprets credit card facilitation of spending through a pain-of-payment lens, a considerable body of existing behavioral evidence is consistent with a cue-triggered account. Findings that credit card cues serve to heighten attention and memory toward the positive elements and away from the negative elements of product stimuli 10 , 11 , 41 fall in line with conditioning processes that have long been understood at both psychological and neurobiological levels 42 , 43 . Exposure to credit card logos has also been shown to increase the willingness to pay for items even when people pay with cash 14 , 15 , 16 , consistent with the idea that credit cards can serve as cues that trigger spending behavior. Moreover, while traditional mental accounting theories suggest that credit cards lessen pain-of-paying for all types of products, people are in fact more inclined to purchase vice products when shopping with credit cards 12 , as is suggested by an account in which credit cards prompt the pursuit of products that satisfy cue-triggered cravings. A conditioned spending response can lead individuals to become more attuned to consumption cues and also raise the marginal utility of consumption 39 , 40 .

Behavioral economic models with expectation-based reference points could potentially accommodate our findings and allow analytical extrapolation from the lab to the marketplace. The general idea in these models is that experience with a transaction instrument generates expectations, which then serve as a reference point 39 , 40 , 44 . If the expectations are to purchase, then failing to purchase becomes a loss relative to the reference point. Such models have explained addictive behavior in the past, however the expectation formation could be localized to a combination of card, product category, and physical environment (e.g., retail or online). In principle, any distinct transaction method: cash, credit, check or digital wallet, could stamp in its own unique set of “local preferences,” as the consumer accumulates experience.

It is notable that the neural mechanisms involved in facilitating credit card spending share similarities to neural mechanisms that have in the past been implicated in addictive behaviors. Specifically, our evidence indicates that credit card cues led to reward network sensitization in the striatum, a distinguishing feature of cue-triggered mechanisms that has emerged in studies of chemical addiction to substances 17 , 18 , 45 . While we certainly do not claim that consumers are “addicted” to credit cards, an appreciation of the overlapping physical substrates may offer insights into important individual differences in vulnerabilities to more extreme forms of credit card overspending. For example, the genetic factors involved in dopaminergic reward network function that have been linked to drug addiction 46 could also contribute to greater risk of credit card abuse, due to the underlying role they play in learning and conditioning processes.

Credit cards are now an established instrument, but similar neural effects may arise with any disruptive payment technology. New payment methods and digital currencies can sensitize reward networks in unexpected ways, removing the financial guardrails created by old purchasing habits and routines. Many new payment technologies have the ability to strengthen reinforcement mechanisms through the use of unique sounds heard when acquiring an item, visual notifications received on mobile devices, and even haptic stimuli that can simultaneously provide physical feedback. Such multisensory stimuli 47 can drive speedier conditioning in a way that could very quickly begin to impact consumer purchasing processes. Payment methods that are integrated within mobile devices could also exploit prior conditioning with the device and fuel more unrestrained purchasing behavior 48 .

This is a cautionary message for the consumer finance and payment industries, as well as for economic welfare analysis based on revealed preference. If neural mechanisms operate under the radar, one cannot assume that technical improvements in payment methods will make all consumers better off. Our study does not discuss consumer protection and related policy issues, but underlines the importance of keeping policy eyes open to neuroscience evidence as it comes in. Because novel payment methods have the potential to take advantage of the neurobiological processes that drive purchase behavior, developing guardrails to prevent misuse may enable consumers to fully benefit from advancements in payment technology.

Although payment methods are involved in every consumer purchase decision, the underlying mechanisms through which they operate have not been well understood. The current findings highlight considerable differences in brain mechanisms responsible for the influence of payment methods on purchasing decisions, and expose important consumer vulnerabilities that will require attention as payment methods rapidly evolve. Ultimately, each of the many billions of consumer financial transactions that occur across the world each year are made by individuals who share the neural mechanisms studied here.

Participants

A total of twenty-eight participants (ages 20–54; age M = 28.7, SD = 10.6; 18 women) completed the study. One participant was excluded from the analysis due to excessive head motion during the scan (more than 3 mm). The experimental procedures were approved by the MIT Institutional Review Board and were performed in accordance with relevant guidelines and regulations. All participants provided informed consent. Participants were compensated at least $75 for their time and received payment after 1–2 weeks of the study.

Median participants in the study had a childhood household income between $75,000 and $100,000, current household income between $25,000 and $44,999, and reported saving 5–10% of their current income. Median participants were also college graduates, and 77% of participants reported having not experienced extended unemployment in the past 2 years. Participants were also asked to respond to financial knowledge questions 49 probing their understanding of credit ratings and investments. On average, participants correctly answered 73%, or 11 of the 15 financial knowledge questions ( SD = 2.4).

Our experimental design approach inherits heavily from prior publications adopting the SHOP paradigm 21 , 22 , 25 . To facilitate comparisons with benchmark SHOP studies, we retained the basic trial structure and added a payment method screen and two payment review screens (Fig. 1 ). The payment method screen (cash or credit card) was inserted between the product presentation and price screens. This sequencing was informed by results of a study showing that payment method matters if presented together with price information, but does not matter at the final checkout stage, after the consumer has presumably formed the intention to purchase 35 . The placement of the payment method prior to the price phase enabled us to examine whether the payment method modulated price-related or reward-related neural signals during the price differential computation. The trial ended with separate confirmation and checkout screens that required endorsement responses, giving participants a chance to “reflect on” but not change their decision, simulating the experience of receiving a receipt after a purchase. These additional stages were included to mimic the full sequence of a retail shopping experience and facilitate observation of post-decisional hedonics.

Each participant arrived to the study with their personal credit card and at least $50 in cash. Participants were told that they would be shopping within the lab’s experimental store, and that any purchases using cash or credit would be made through the lab at the end of the study. Therefore, any payments for purchases would come from a participant’s out-of-pocket funds rather than experimental endowments as in prior SHOP studies 21 . All products were offered at prices well below the minimum $50 cash on hand, with a median product price offer of $5.40 ( M = $6.39, SD = $3.73, min = $1.50, max = $18.00). Similar to prior SHOP studies, these offered prices were at a fixed 70% discount relative to actual retail price (i.e., corresponding to retail prices between $5 to $60). Participants were required to bring at least $50 in cash to the study in order to minimize differential liquidity constraints; that is, participants did not reject items simply because they did not have enough cash with them, as we structured all products to have price offers to be below the $50 that participants had on hand. The prices examined within this study are at the high end in relation to previous literature applying the SHOP paradigm 21 , 22 , 25 and behavioral research on payment method effects 41 , 41 , 50 . Yet, as we discuss within the conclusion, it is possible that other mechanisms could be at play when studying big-ticket items at prices higher than those examined in the current study.

To increase interest and simulate a typical retail experience, each participant faced a tailored set of product offerings. We populated a database of over 22,000 top selling items, drawing on product information from Amazon. An independent online sample then rated which categories they perceived to be most appealing, which reduced the database to approximately 4000 items, covering a wide range of categories, including beauty, kitchen, books, etc. Prior to entering the scanner, participants selected and rated the desirability of 42 categories from the lab’s experimental store on a 7-point scale. Products in personally more desirable categories were more likely to be offered in the fMRI shopping task.

The scanning task involved three shopping “runs,” with 28 trials each, or 84 in total. Within a trial, participants indicated whether they would buy a specific product at a stated price. If so, the product was added to the participant’s “shopping basket.” No products were repeated. Each product had a 50% chance of being offered for purchase with credit or with cash, pseudorandomly determined such that each payment method constituted half of the trials. At the end of the task, one product was randomly selected. If it was in the basket, the participant was asked to pay for the product at the stated price. Participants paid using their own personal credit card or out-of-pocket cash, as specified in the product offer. Regardless of payment method, items were shipped to participants by mail within 2–3 days of the study.

Each 24 s (s) trial consisted of six 4 s periods, followed by a jittered 2–8 s intertrial interval (see Fig. 1 for an illustration). Participants viewed a product in period 1; the payment method was introduced in period 2 with a cash or credit icon, the price in period 3. Participants signaled their decision to buy or not to buy in period 4. Following a buy decision, the participants saw a 4 s confirmation screen stating “this item has been added to your basket,” and a 4 s payment screen that required them to press a button to “commit to pay.” Following a no buy decision, the confirmation screen indicated “basket unchanged” and the payment screen required participants to press a button to acknowledge “no payment necessary.”

After exiting the scanner, participants reported their willingness to pay for each product shown in the scanner task by completing a separate incentive compatible auction procedure 51 and also completed several psychological scales. Post-scan measures were not recorded for one participant due to a technical error.

fMRI acquisition

All participants were right handed, native English speakers, with no history of neurological disorders. Participants were verified to have no magnetically reactive matter present in or on the body prior to scanning. All scans were performed using a 3 T Siemens Magnetom Tim Trio MRI System with a phase-array 32-channel head coil (Siemens Medical, Erlangen, Germany). Structural scans were acquired using a three-dimensional T1-weighted multi-echo MP-RAGE pulse sequence (TR = 2530 ms; TE = 1.64 ms, 3.5 ms, 5.36 ms, 7.22 ms; flip angle = 7°; slices = 176; thickness = 1 mm; matrix = 256 × 256). Task-based functional scans were collected using T2* weighted EPI sequence images sensitive to blood oxygen level-dependent (BOLD) contrast (TR = 2000 ms; TE = 30 ms; flip angle = 90°; slices = 32; thickness = 3 mm; matrix = 64 × 64). Analyses were conducted using the FMRIB Software Library, FSL, version 6.00 52 .

Behavioral analysis

To model the effects of price and payment method on purchase decisions, we conducted a hierarchical logistic regression in which purchase decision was predicted by price, payment method, their interaction, and demographic controls. The hierarchical model included random slopes for the price × payment method interaction and participant-level random effects, following prior work 35 . Price was a continuous, z-normalized regressor, normed at the participant-level price distribution. The demographic variables (age, marital status, education level, and amount of savings) controlled for differences in shopping behavior across participants.

ROI analysis

Region of interest analyses examined activity in a priori determined focal brain areas selected based on past observations that have isolated neural purchasing signals, as described above 21 , 22 , 25 . To specify the precise regions for analysis, we applied masks from meta-analyses of the striatum and VMPFC (see Fig. 9 within Bartra et al. 26 for brain maps depicting these regions), as well as the rAIC 34 , k = 2, cluster 2. Notably, the striatum contains the nucleus accumbens, an ROI referred to in past research 21 , 22 , 25 . See Fig. 3 for a graphical display of the ROIs.

These meta-analytically determined brain regions match the ROIs examined in prior SHOP experiments while offering interpretive advantages through the application of sample-independent functional definitions rather than sample-dependent anatomical definitions. Furthermore, automated ROI selection served to minimize potential experimenter bias associated with the manual adjustment of ROI coordinates for individual participants. ROIs for the ventral striatum and VMPFC were generated based on a five-way conjunction analysis identifying regions of the brain carrying a monotonic, modality-independent subjective value signal on the basis of thousands of independent brain scans 26 . The right anterior insula ROI was determined by applying a task-evoked coactivation-based parcellation analysis with hundreds of independent scans 34 . Whole brain contrast analyses verified that striatum activation was associated with product preference, VMPFC activation was associated with choice, and right anterior insula activation was associated with higher prices within our sample (see Supplementary Information).

Prior findings in the SHOP paradigm established that differential neural purchasing signals emerge during the price and choice phases 21 , 22 , 25 . Thus, we anticipated that the payment method would impact these neural purchasing signals at the price and choice phases, following presentation of the payment method. We focus our analysis and interpretation on these stages of the time course in which payment methods were predicted to modulate neural purchasing signals (in addition to the payment method phase), but we also provide results at all other stages of the time course for the reader’s reference (that is, including stages prior to the presentation of payment method itself and stages after participants already recorded a purchase decision). Our goal was to understand how payment method influenced the previously identified ROIs when making purchase decisions. In order to present these effects intuitively, we report the results of logistic regressions conducted separately for each ROI and at each acquisition point. The figures report parameter significance from logistic regression results without corrections; please note that the key interaction effects in the striatum remain significant after Bonferroni corrections.

Specifically, within each region of interest, we analyzed the relationship between signal change and purchasing behavior at each acquisition point (TR). Following prior literature applying the SHOP paradigm 21 , 22 , 25 , time courses were lagged by 4 s to compensate for the delay in the hemodynamic response; the time courses depicted in the figures reflect this 4 s lag. To identify the differential purchase signal associated with credit versus cash purchases, we first conducted logistic regressions of the purchase decision on the ROI signal change, payment method, and their interaction at each acquisition point (results shown in Fig. 4 ). In specific, for each ROI and acquisition point, we fit the following regression equation: $Buy=logit({b}_{0}+{b}_{1}\,*\,ROIactivation+{b}_{2}\,*\,PaymentMethod+{b}_{3}\,*\,ROIactivation\,*\,PaymentMethod)$ ; Buy corresponds to the decision to purchase (Buy = 1, NoBuy = 0), ROIactivation refers to the activation in the particular ROI at the acquisition point on the trial, PaymentMethod refers to the contrast coded treatment (Credit = 1, Cash = − 1).

We next evaluated the relationship between ROI activity and purchase behavior by conducting logistic regressions of the purchase decision on the ROI signal change and its interaction with price (a continuous, z-normalized variable; results shown in Fig. 5 ). Specifically, for the price interaction analysis in Fig. 5 we fit the following regression equation: $Buy=logit({b}_{0}+{b}_{1}\,*\,ROIactivation+{b}_{2}\,*\,Price+{b}_{3}\,*\,ROIactivation\,*\,Price)$ . These analyses allowed us to directly examine the effects of payment method on previously identified ROIs involved in making purchase decisions.

Notably, all regression results apply price as a continuous, z-normalized regressor, normed based on the participant-level price distribution. Participant price distributions had minimum offer prices that ranged from $1.50 to $1.96 across participants and maximum price values that ranged from $12.78 to $18.00. “High-price” and “low-price” categories were included for graphical displays only (i.e., Fig. 5 ) and were defined relative to the median of each participant’s price distribution; binary price variables were not used as regressors in any significance tests. Further details regarding whole brain analyses as well as additional participant characteristics are provided within the Supplementary Information.

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Acknowledgements

The research was funded by the MIT Sloan School of Management, through the MIT Sloan Neuroeconomics Lab. Derek Dunfield was supported by the MIT Intelligence Initiative and the National Science and Engineering Council of Canada. The authors gratefully acknowledge the Athinoula A. Martinos Imaging Center at the McGovern Institute for Brain Research, MIT for their support in data collection, and constructive manuscript comments by three anonymous referees and Danica Mijovic-Prelec.

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S.B., D.D., A.H., and D.P. contributed to the design and implementation of the study. S.B. and A.H. collected and analyzed the neural data. S.B. and D.P. drafted the manuscript.

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Consumers and credit cards: A credit cards: A review of the empirical literature review of the empirical literature

Research in the area of consumer credit card abundance of literature in the business, psychology, and public policy fields. 1960s, the work revolved around descriptive characteristics and evolved as scholars probed deeper by investigating relationships between credit cards and psychological constructs, and the need for consumer policy. While the scope of credit card research has broadened, there is a need to pause and reflect on what we actually know about the phenomenon, given its proclivity in society. This paper identifies the empirical research conducted over the past four decades in order to provide insights and recommendations for additional research. A total of 537 refereed journal articles from 8 databases were reviewed and evaluate to credit cards, with a final working 2012. Emerging trends are identified and suggestions for future research are provided. Research in the area of consumer credit card attitude and behavior has provided an abundance of literature in the business, psychology, and public policy fields. Beginning in the 1960s, the work revolved around descriptive characteristics and evolved as scholars probed onships between credit cards and psychological constructs, and the While the scope of credit card research has broadened, there is a need to pause and reflect on what we actually know about the phenomenon, given its proclivity in This paper identifies the empirical research conducted over the past four decades in order to provide insights and recommendations for additional research. A total of 537 refereed journal articles from 8 databases were reviewed and evaluated within specific parameters related with a final working sample of 103 journal articles published between 1969 and 2012. Emerging trends are identified and suggestions for future research are provided. attitude and behavior has provided an Beginning in the 1960s, the work revolved around descriptive characteristics and evolved as scholars probed onships between credit cards and psychological constructs, and the While the scope of credit card research has broadened, there is a need to pause and reflect on what we actually know about the phenomenon, given its proclivity in This paper identifies the empirical research conducted over the past four decades in order to provide insights and recommendations for additional research. A total of 537 refereed thin specific parameters related published between 1969 and 2012. Emerging trends are identified and suggestions for future research are provided.

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Research department working papers, credit card utilization and consumption over the life cycle and business cycle.

Nearly 80 percent of U.S. adults have a credit card, and more than half of them revolve their debt from month to month. Using a large sample of credit bureau data, this paper documents a tight link between available credit (the limit) and credit card debt, and then it offers a model-based interpretation of this linkage. Credit limits change frequently for individuals, increase rapidly on average as people age, and show large changes over the business cycle. Yet credit card debt changes nearly proportionately to credit and at about the same time, so the fraction of credit used is relatively stable over time. The authors build a life-cycle consumption model that includes the joint use of credit cards to pay directly for expenditures, to help smooth consumption against income shocks, and to borrow longer term (revolving indefinitely). The authors estimate the parameters of the model using several data sources, including a large credit bureau database and a new daily diary of consumer payment choices.

Implications

People experience important changes in credit throughout their life, especially between the ages of 20 and 40, when their credit limits soar. These changes in credit are in effect changes in liquidity, and observing how people react to them provides insight into the more general savings and consumption decisions they make.

Although people use credit cards for different purposes, all uses contribute to stable credit utilization. Payment use is proportional to consumption, and when an increase in income leads to an increase in credit limits, a convenience user will increase consumption and payments use. People who use credit cards to borrow because of impatience see a rise in their credit limit as an increase in wealth and increase their consumption (and debt) accordingly. And those who use credit cards for smoothing purposes early in life—when income rises more slowly than credit limits—increase their credit card debt at about the same rate as their credit limits rise.

The revolving credit available to consumers changes substantially over the business cycle, life cycle, and for individuals. We show that debt changes at the same time as credit, so credit utilization is remarkably stable. From ages 20–40, for example, credit card limits grow by more than 700 percent, and yet utilization holds steadily at around 50 percent. We estimate a structural model of life-cycle consumption and credit use in which credit cards can be used for payments, precautionary smoothing, and life-cycle smoothing, uniting their monetary and revolving credit functions. Our estimates predict stable utilization closely matching the individual, life-cycle, and business-cycle relationships between credit and debt. The preference heterogeneity implied by the different uses of credit cards drives our results. The revealed preference that some people with credit cards borrow at high interest, while others do not, suggests that around half the population is living nearly hand to mouth.

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An Approach for Credit Card Churn Prediction Using Gradient Descent

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A very important asset for any company in the business sectors such as banking, marketing, etc. are its customers. For them to stay in the game, they have to satisfy their customers. Customer retention plays an important role in attracting and retaining the customers. Customer retention means to keep the customer satisfied so that they do not stop using their service/product in the domain of banking; the banks provide various kinds of services to the customers especially in the electronic banking sector. For this study, we have selected the service of credit card. For a bank to give a loan or amount on credit basis, the e-bank should make sure if its customers are eligible and can repay their money. The purpose of this project is to implement a neural network model to classify the churners and non-churners.

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Churn and Non-churn of Customers in Banking Sector Using Extreme Learning Machine

Comparative Study on Different Classification Models for Customer Churn Problem

Investigation on Customer Churn Prediction Using Machine Learning Techniques

Altinisik, F.: Predicting Customers Intending to Cancel Credit Card Subscriptions Using Machine Learning Algorithms: A Case Study. IEEE (2020)

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Saanchay, P.M., Thomas, K.T. (2022). An Approach for Credit Card Churn Prediction Using Gradient Descent. In: Senjyu, T., Mahalle, P., Perumal, T., Joshi, A. (eds) IOT with Smart Systems. Smart Innovation, Systems and Technologies, vol 251. Springer, Singapore. https://doi.org/10.1007/978-981-16-3945-6_68

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Beyond One-Size-Fits-All: Exclusive Research Reveals how to Capitalize on Differences in Credit Card Customer Loyalty Preferences Across Diverse Global Markets

Research highlights universal takeaways and regional preferences in consumer loyalty drivers across North America, Middle East–Africa, South America, Europe, and Asia–Pacific.

VANCOUVER, British Columbia (July 9, 2024) – The credit card loyalty program market is booming, with the majority of U.S. credit card holders now owning at least one card that offers a rewards program. A global survey by WillowTree, a TELUS International Company – rebranding to TELUS Digital Experience later in the third quarter (NYSE and TSX: TIXT) – reveals the driving forces behind increasing consumer interest in credit card loyalty programs, highlighting universal takeaways and distinct regional trends. Above all, the research underscores the need for financial brands to adopt advanced customer segmentation and tailored experience strategies for effective customer retention and growth.

"In a crowded marketplace, financial service brands, particularly those looking to launch a new global loyalty program, or expand existing programs to other countries, cannot rely on a one-size-fits-all approach. Instead, deep market-specific research is crucial to understanding and meeting the diverse needs of customers across regions," said Tobias Dengel, President of WillowTree, a TELUS International Company. "By creating shared infrastructure that enhances efficiency, while maintaining the flexibility to adapt to regional nuances, brands can develop truly personalized experiences. This research-baked approach is core to our commitment to human-centered design. When brands partner with experts who can harness data, AI, and automation, it not only strengthens customer loyalty but transforms these programs into dynamic tools for sustained business growth. A well-executed loyalty program provides insightful data on consumer behaviors and preferences, enabling brands to tailor their offerings and elevate engagement in every market."

WillowTree’s survey included nearly 500 English-speaking loyalty credit card members from across five regions: North America, Middle East–Africa, South America, Europe, and Asia–Pacific. Respondents were asked about personal, cultural, and financial factors that may influence their perceptions and preferences regarding credit card loyalty programs. The research team then ranked consumer preferences based on the types of products and services where respondents prefer to redeem their loyalty points.

The research highlights that to launch personalized card offers that excite audiences in new markets, financial services companies need a data-backed resource that shows where consumer preferences overlap between international markets — and where they diverge.

Universal Takeaways

Emerging markets like China, India, and Southeast Asian nations, along with Latin America and parts of Africa, are spearheading global growth in credit card usage, and the U.S. now ranks 9th worldwide in terms of credit card ownership. The surge in global credit card growth is largely fueled by increased smartphone use, online shopping, and the availability of secure, convenient, and cashless payment methods.

Across all global regions surveyed, the study uncovered a few universally consistent insights that offer financial brands a coherent set of values around which to begin crafting a global loyalty program that transcends geography and resonates with audiences worldwide:

A consumer’s perception of their financial stability will impact that consumer's approach to loyalty programs. A robust consumer data platform enabling segmentation by income and spending habits will help surface relevant loyalty offers and promotions.
With this in mind, the most popular loyalty redemption choices are typically cash back and essential goods, which ranked highest in four of the five regions. Brands should prioritize and localize these reward categories and make them easy to redeem.
Consumers worldwide trust and prefer to engage with loyalty programs via mobile apps rather than desktop or web-based experiences, underscoring the need for frictionless, personalized mobile experiences.

"To meet consumers' high expectations for how they interact with brands, we recommend that financial institutions develop mobile apps with intuitive, user-friendly interfaces. This includes advanced voice-enabled technology, which enables customers to engage with GenAI customer service tools as though they're in a natural conversation," said Dengel. "In our collaborations with financial services brands, we prioritize a data-first approach. By integrating real-time data from customer interactions into our comprehensive digital CX strategy, we are able to craft personalized experiences that adeptly reflect regional preferences and cultural nuances, ensuring relevance across the globe."

Regional Trends: Differentiated Consumer Preferences Across Five Global Markets

Beyond the universally consistent findings above, the research revealed a host of regionally specific behaviors and preferences that can be used as predictors of brand loyalty and commitment, highlighting how data-driven customization is crucial.

Global consumer survey reveals distinct regional preferences for loyalty point redemptions across five global regions. Graphic by WillowTree, a TELUS International Company.

North America : Prefer Redeeming Points in their Communities

In the U.S. and Canada, consumers show a strong preference for loyalty programs that offer easy point redemption and clear visibility of accrued benefits. Higher-tier, aspirational rewards like luxury vacations, exclusive events, or once-in-a-lifetime experiences motivate North American members to accumulate points over time. Consumers also value rewards they can redeem at local businesses or services within their communities. North American consumers are an outlier in that this is the only region where consumers have a marked preference for redeeming points for fashion and apparel.

Middle East – Africa: Looking for Alignment with Personal Spending

Consumers in these regions exhibit a high level of “uncertainty avoidance,” so credit card companies must be extremely transparent (and provide step-by-step guidance) on how consumers’ regular spending can accumulate points. They prefer loyalty programs that align with their personal spending habits and provide transparency and clear communication about how to earn and redeem points. Consumers in this region perceive the value of their money as more unstable from week to week and they least frequently spend points on unique experiences, such as concerts. Similar to their counterparts in South America, consumers here are interested in earning and redeeming points on big-ticket, high-price items.

South America : Appreciate Saving on Essentials

Consumers in this area enjoy loyalty programs that help them reduce spending and offer cash back on everyday essentials like groceries and utilities. In places like Brazil, where installment payments are popular, loyalty programs that provide bonus rewards or discounts for clearing installment balances are valued. Additionally, respondents in this region uniquely prioritize earning and using their points at bars and restaurants.

Europe: Seeking Clarity and Convenience

Consumers in this region exhibit a more relaxed attitude towards uncertainty and prefer only a moderate level of guidance rather than overly detailed instructions when attempting to redeem points. Despite having the least optimistic long-term financial outlook among all regions, consumers still prioritize loyalty programs that help them save money and easily redeem points. European respondents were also the only ones in the survey who prioritized earning and redeeming points on food delivery.

Asia – Pacific: Cultural Nuance Highly Valued

Consumers here are the only respondents in the survey who prioritize using loyalty points for travel above all other rewards. Additionally, culturally tailored promotions have a significant impact, particularly in India, where 32% of respondents report that their religious beliefs shape their credit card usage. This influence may be reflected in a preference for avoiding debt or choosing purchases that align with their spiritual values. Consumers here are also more likely to redeem loyalty points for unique experiences, such as concerts, in comparison to other regions.

Leveraging Loyalty Programs as a Continuous Data Source for Personalization

With extensive customer data, financial organizations are in an advantageous position to capture and leverage consumer data to deliver highly personalized experiences. This helps to make each customer transaction feel tailored to their individuality. And this ongoing data stream can sustain personalization efforts, ensuring financial loyalty programs adapt and grow as customer behaviors and preferences change.

"By incorporating data from customers and grouping them based on behaviors and characteristics, we can deliver highly effective one-to-one messaging,” added Dengel. "Our ultimate goal is to deliver personalized content at an individual level, and to continually refine that content based on user interactions. This is how we can help financial brands achieve personalization at scale, and ensure loyalty programs delight customers for years to come."

For full survey insights, download the report: Global Credit Card Rewards Programs: How Consumer Loyalty Preferences Differ by Region

WillowTree, a TELUS International Company – rebranding to TELUS Digital Experience later in the third quarter (NYSE and TSX: TIXT) – offers comprehensive support in developing impactful loyalty programs by analyzing customer behavior and industry trends to develop structured, personalized strategies that boost engagement and revenue. The company’s integrated campaign teams are able to deliver real-time, personalized experiences through data-driven automation at every stage of the customer journey, ensuring that financial services brands can provide exceptional and consistent customer experiences. Learn how WillowTree, a TELUS International Company, helps financial services companies maximize loyalty and rewards ROI with loyalty strategy and customer experience consulting.

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Political ads on social media rife with misinformation and scams, new research finds

By david klepper associated press - | jul 10, 2024.

WASHINGTON (AP) -- The online advertisement to Donald Trump supporters was clear enough: Click here, and receive a free Trump 2024 flag and a commemorative coin. All in exchange for taking a quick survey and providing a credit card number for the $5 shipping and handling.

"You'll get two free gifts just by taking this quick poll in support of Trump," says the ad's narrator.

The ad -- which has appeared on Facebook, YouTube and other platforms -- didn't mention the $80 charge that would later appear on credit card statements. Those that clicked were scammed.

Political advertisements on social media are one of the best ways for candidates to reach supporters and raise campaign cash. But as a new report from Syracuse University shows, weak regulations governing online ads and haphazard enforcement by tech companies also make ads a prime source for misleading information about elections -- and a tantalizingly easy way for con artists to target victims.

"There is very little regulation on the platforms," said Jennifer Stromer-Galley, the professor who led the research for the ElectionGraph Project at Syracuse University's Institute for Democracy, Journalism & Citizenship. "It leaves the American public vulnerable to misinformation, disinformation and propaganda."

Stromer's research examined more than 2,200 groups on Facebook or Instagram that ran ads between September and May mentioning one of the presidential candidates. Combined, the ads cost nearly $19 million and were seen more than 1 billion times.

Data connected to the ads (and made public by Meta, Facebook's owner) shows that both right- and left-leaning ads targeted older voters more than younger ones. Right-leaning ads were more likely to target men, progressive ads were more likely to target women.

Overall, conservative-leaning organizations bought more ads than progressive-leaning groups. Immigration was the top issue raised in right-leaning ads while the economy dominated progressive ads.

Many of the ads contained misleading information, or deepfake video and audio of celebrities supposedly crying during a speech by former First Lady Melania Trump. Stromer-Galley noted that falsehoods in ads about urban crime and immigration were especially common.

While most of the groups paying for the ads are legitimate, others seemed more interested in getting a user's personal financial data than boosting any particular candidate. Using a partnership with the data science firm Neo4j, Stromer-Galley found that some of the pages shared common creators, or ran virtually identical ads. When one page disappeared -- perhaps removed by Facebook moderators -- another would pop up quickly to take its place.

Many of the pages sold Trump-related merchandise such as flags, hats, banners and coins or advertised fictitious investment schemes. The true motive, apparently, was to get a user's credit card information.

The ads promising a free Trump flag were placed by a group called Liberty Defender Group. Emails sent to several addresses listed for the company were not returned, and a phone number for a company representative could not be found. One website associated with the group has moved on from politics, and is now selling devices which claim to improve home energy efficiency.

Meta removed most of the network's ads and pages earlier this year after researchers noticed their activity, but the ads are still visible on other platforms. The company says it prohibits scams or content that could interfere with the operation of an election and removes ads that violate the rules. In addition, the company urges its users not to click on suspicious links, or to hand over personal information to untrustworthy sources.

"Don't answer messages asking for your password, social security number, or credit card information," the company said.

The Trump campaign, which has no known ties to the network, did not respond to a message seeking comment.

The researchers at Syracuse were only able to study ads on Meta platforms because other companies do not make such information public. As a result, Stromer-Galley said the public is in the dark about the true amount of misinformation and scams spreading on social media.

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Get the Most Out of Your Credit Card Rewards Points This Summer

Published on July 9, 2024

By: Kailey Hagen

Be strategic about which card you use for which purchases, so you can maximize your rewards earning potential.
Summer can be a great time to go for a new credit card's sign-up bonus, especially if you've already planned for a large purchase.
Consider your reward redemption options carefully to ensure you're getting the greatest bang for your buck.

Summer is a time when many of us like to get out and about, taking road trips, attending festivals and community events, and trying new activities. But all of that fun can come with high price tags.

Choose the right credit card for your spending

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Choosing a card that offers bonus rewards in your areas of greatest spending is one of the best ways to maximize your rewards. For example, if you're spending a lot on gas because you're driving more, use a card that gives you extra cash back or miles on gas purchases.

This requires you to be strategic about which card you use for which purchases and you may even want to open a new rewards credit card if you realize it could offer you greater earning potential. Just be careful not to apply for new credit too often. Limit yourself to opening a new card every six months or so.

Go for a new sign-up bonus

If you plan to make a big-ticket purchase this summer and you've been eyeing up a new credit card, now might be a good time to open it, especially if it offers a sign-up bonus.

Sign-up bonuses are large numbers of points or miles new cardholders can earn by completing qualifying activities -- usually spending a certain amount -- within the first few months of card opening.

If you're planning a summer vacation, for example, worth several thousand dollars, you could easily earn some credit card sign-up bonuses in a single month. Then, you can redeem those rewards on additional purchases throughout the summer.

But before you do this, make sure the bonus is achievable for your spending and that you like the card's regular perks. Remember, you'll still have the card long after earning the bonus. If it doesn't appeal to you, shop around for better options.

Choose your redemption options carefully

Most credit cards offer several redemption options. For example, cash back cards enable you to apply your rewards as a statement credit, redeem them for gift cards, or even pay with points directly at select retailers. Travel rewards cards let you use your miles on flights, hotels, and other travel-related purchases and some enable you to transfer your points to partner programs as well.

But these redemption strategies aren't all created equal. You might get a lower dollar value per point if you choose to pay with points compared to redeeming for a statement credit or a gift card, for example.

Compare all your options and see which gives you the greatest dollar value for your points. Choose this option unless you have a very good reason for choosing a different one.

Keep in mind that some points or miles may have expiration dates. Check with your card issuer if you're not sure how this works. Aim to use your points before their expiration date whenever possible.

Watch what you spend

If you want your credit card rewards to work for you, paying your balance off in full each month is essential. If you carry a balance, the interest charges you'll pay could easily exceed what you're earning in rewards each month.

In this case, it might be best to use your points to pay down your debt when that's an option. Try to limit your charges to your credit card to avoid increasing your debt further and consider a balance transfer card or a personal loan to help you wipe it out for good.

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Kailey Hagen has been covering personal finance topics, including banks, insurance, and retirement since 2013.

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We're firm believers in the Golden Rule, which is why editorial opinions are ours alone and have not been previously reviewed, approved, or endorsed by included advertisers. The Ascent, a Motley Fool service, does not cover all offers on the market. The Ascent has a dedicated team of editors and analysts focused on personal finance, and they follow the same set of publishing standards and editorial integrity while maintaining professional separation from the analysts and editors on other Motley Fool brands.

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Beyond One-Size-Fits-All: Exclusive Research Reveals how to Capitalize on Differences in Credit Card Customer Loyalty Preferences Across Diverse Global Markets

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Political ads on social media rife with misinformation and scams, new research finds

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Watch what you spend

Alert: our top-rated cash back card now has 0% intro APR until 2025

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