Credit Card Limits Research Articles

Optimizing Test Data Management Strategies in Banking Domain Projects

The steady rise in processing power over the past 20 years has resulted in an enormous volume of data. Furthermore, anybody may now easily create and consume material in any format thanks to recent advancements in Web technology. Large volumes of data are regularly gathered by banking systems, including trade finance data, SWIFT and telex communications, client information, details about transactions, risk profiles, credit card details, limit and collateral details, the compliance or Anti Money Laundering (AML)-related data, and limit and collateral details. Every day, thousands of choices are made at banks. These choices pertain to credit, default, beginning a relationship, investments, AML, and illicit funding, among other things. To make these crucial choices, one must rely on a variety of data and drill down capabilities offered by the banking systems. We created a set of specifications for the kinds of data that should be included in a product catalogue. We ascertained what data the departments need by using a survey and questionnaire of the retailer's staff. We ensured that there was no one standard for the information organisation and then put out our own plan. This enormous amount of data may be mined for information and intriguing patterns, which can then be used to the decision-making process. This article examines and summarises a number of data mining methods that have applications in the banking industry. An overview of data mining methods and procedures is given. It also sheds light on how these methods may be applied in the banking industry to facilitate and enhance decision-making.

THE UNPRECEDENTED FALL IN U.S. REVOLVING CREDIT

AbstractAfter decades of consistent growth, U.S. revolving credit declined drastically post 2009. We study the Ability to Pay provision of the Credit CARD Act of 2009, a policy that restricts credit card limits, as a contributing factor. Extending a model of revolving credit lines, we find that the policy accounts for 54–60% of the decline in revolving credit. Furthermore, the policy accounts for lower utilization rates despite tighter credit limits and higher spreads despite lower default risk. The policy's goal of consumer protection is achieved for a few consumers with time‐inconsistent preferences; most individuals are hurt.

Credit cards, credit utilization, and consumption

We use credit bureau data to show that credit card limits grow rapidly early in life and are an important early-life liquidity source. Yet individual credit utilization is stable over the short and long term. To explain these new findings, we propose a life-cycle consumption model with heterogeneous preferences, the option to revolve, and credit cards used for payments. Using diary data to identify payment use and the revealed preference that some people with credit cards borrow at high interest, while others do not to help identify heterogeneous preferences, the estimated model matches consumption and credit use at every frequency. The model suggests that around half the population has an endogenously high marginal propensity to consume. This targetable population explains the large consumption response to unexpected cash.

Optimizing credit limit adjustments under adversarial goals using reinforcement learning

Reinforcement learning has been explored for many problems, from video games with deterministic environments to portfolio and operations management in which scenarios are stochastic; however, there have been few attempts to test these methods in banking problems. In this study, we sought to find and automatize an optimal credit card limit adjustment policy by employing reinforcement learning techniques. In particular, because of the historical data available, we considered two possible actions per customer, namely increasing or maintaining an individual’s current credit limit. To find this policy, we first formulated this decision-making question as an optimization problem in which the expected profit was maximized; therefore, we balanced two adversarial goals: maximizing the portfolio’s revenue and minimizing the portfolio’s provisions. Second, given the particularities of our problem, we used an offline learning strategy to simulate the impact of the action based on historical data from a super-app (i.e., a mobile application that offers various services from goods deliveries to financial products) in Latin America to train our reinforcement learning agent. Our results, based on the proposed methodology involving synthetic experimentation, show that a Double Q-learning agent with optimized hyperparameters can outperform other strategies and generate a non-trivial optimal policy not only reflecting the complex nature of this decision but offering an incentive to explore reinforcement learning in real-world banking scenarios. Our research establishes a conceptual structure for applying reinforcement learning framework to credit limit adjustment, presenting an objective technique to make these decisions primarily based on data-driven methods rather than relying only on expert-driven systems. We also study the use of alternative data for the problem of balance prediction, as the latter is a requirement of our proposed model. We find the use of such data does not always bring prediction gains.

A Statistical Analysis of Credit and Debit Card Usage Patterns

This analysis deals with finding relation between encountering credit and debit card frauds and different age groups, gender, area of residence, monthly income, credit card limit and several other factors. It also involves fitting a prediction model on the credit card usage pattern of individuals. It includes studying a times series model for credit and debit card usage over the last decade and developing a forecasting model to predict the forecast using the previous data available.

Several Possible Causes of Credit Card Default

Credit card default has always existed. In this case, understanding what causes credit card default has become a matter of concern for banks and the whole society. It is necessary to understand the reasons and reduce the risk of default. This paper explores the possible causes of credit card default by studying individual credit card limit, gender, education, marriage, repayment time, bill statement and consumption amount, and combining the comparison, regression, and factor analysis in SPSS. After analysis and research, the first four possible reasons are bill statement, repayment time, consumption amount and age. In this paper, the analysis of possible causes of credit card default can help banks better understand risks, improve risk control strategies and marketing strategies.

Shocked by Bank Funding Shocks: Evidence from Consumer Credit Cards

Abstract Using the near universe of U.S. consumer credit cards, we show that banks transmit their wholesale funding shocks to consumers by reducing their credit card limits. Credit-constrained consumers who are unable to hedge against the transmitted shock by accessing other credit cards experience a stronger and more persistent reduction in aggregate credit card limits at the consumer level. Consequently, these credit-constrained consumers reduce their aggregate credit card borrowing. Our results document a credit card lending channel for the transmission of adverse bank shocks and show who bears the costs of fragile bank funding structures. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

A Semiparametric Tilt Optimality Model

Practitioners often face the situation of comparing any set of k distributions, which may follow neither normality nor equality of variances. We propose a semiparametric model to compare those distributions using an exponential tilt method. This extends the classical analysis of variance models when all distributions are unknown by relaxing its assumptions. The proposed model is optimal when one of the distributions is known. Large-sample estimates of the model parameters are derived, and the hypotheses for the equality of the distributions are tested for one-at-a-time and simultaneous comparison cases. Real data examples from NASA meteorology experiments and social credit card limits are analyzed to illustrate our approach. The proposed approach is shown to be preferable in a simulated power comparison with existing parametric and nonparametric methods.

Homeowner Characteristics Associated With the Occurrence of Negative Home Equity

Negative home equity is due to declines in home values, largely driven by economic factors, and increases in mortgage debt, a decision made by individuals. Yet, empirical research assessing the individual’s role in the occurrence of negative home equity is limited. This study used the 2018 National Financial Capability Study to explore the association between financial literacy, savings, and debt at the individual level on the occurrence of negative home equity. The findings revealed that objective financial knowledge and financial security were negatively associated with the occurrence of negative home equity, while having a home equity loan, using a payday loan, having medical debt, and exceeding credit card limits were positively associated with the occurrence of negative home equity.

Segmentation of Credit Card Customers Based on Their Credit Card Usage Behavior using The K-Means Algorithm

The intensity of credit card customers in making transactions has increased in the last 10 years in Indonesia. This is both a challenge and an opportunity for the Bank. Customer segmentation information is beneficial to reduce bad debts or increasing customer credit card limit capacity. This study aims to segment credit card customers based on their usage behavior with a clustering approach using the K-means algorithm. While the process of evaluating segmentation results using the silhouette index. Based on the experimental results, six groups are the best number of clusters. The six groups are shopping hobbies, payment process at maturity, payment by installments, withdrawing cash, buying expensive goods, and types that rarely use credit cards.

Credit Limit of Unsecured Consumer Lending: Evidence from Micro Data

As credit card debts have increased in Indonesia over the past ten years, concerns over the impulsive buying behavior of Indonesian credit card holders have emerged. Therefore, more attention must be paid to credit risk management of banks as it plays an important role in analyzing the possibility of losses due to the inability of prospective borrowers to repay debts. This study provides empirical evidence about the prudence of commercial banks in Greater Jakarta in offering credit card limits. Using primary micro-data collected from credit card applications submitted to the largest foreign private bank providing retail credit in the Greater Jakarta area in 2019, this study employed multiple regression model to analyze the determinants of credit card limits in the Greater Jakarta. Our empirical findings suggest that age, home location, income, type of industry, and office location of prospective borrowers significantly influence credit card limits. Commercial banks in the Greater Jakarta, thus, have been prudent in offering credit card limits.

Does getting a mortgage affect consumer credit use?

Buying a house changes a household’s balance sheet by simultaneously reducing liquidity and introducing mortgage payments, which may leave the household more exposed to other shocks. We examine how this change impacts consumer credit beyond the mortgage. Using a large panel, we show that on acquiring a mortgage, credit card debt increases by about $1500 in the short term, severe delinquencies increase by 2.2 percentage points, and credit card utilization—the fraction of a consumer’s credit card limit that is used—increases by 11 percentage points. In the long term, credit card balances increase by $3900 and delinquencies by 9.1 percentage points. In our sample period before the 2008 financial crisis, credit limits increased faster than debt in the long run, pushing down long-term utilization. After the financial crisis, debt increased faster than credit limits in the long run, and credit card utilization rates rose upon the acquisition of a new mortgage, consistent with larger down payments leaving households more constrained.

The Determinants of Life Insurance Ownership

Life insurance ownership has been known as one of alternatives to mitigate the financial risk of a household. Several ethnicities in Indonesia are exposed to financial risk in terms of premature death of family members because there exists cultural traits expenditures. This paper examines the decision to own life insurance among Batak nese in Medan. The determinants of life insurance ownership observed in this study include socioeconomic and demographic factors. Data was analysed using Logit regression of 185 questionnaires after adjustments from 200 responses. The results show that the Batak ethnic in Medan owned personal life insurance because it was driven by the number of credit card limits, age, number of social gathering clan, education, residence ownership status, and bequest motives. Meanwhile, Saur matua life insurance (cultural traits) ownership is driven by factors in the order of the first child in the family, the number of brothers, life expectancy, and the condition of receiving inheritance from parents. Batak nese own life insurance affected by socioeconomic and demographic factors. This research can provide opportunities for the life insurance industry if it wants to add new features of life insurance products that are specific to ethnic groups with similar cultural heritage patterns. DOI: https://doi.org/10.26905/jkdp.v25i1.5112

Does Increasing Access to Formal Credit Reduce Payday Borrowing?

The use of high cost “payday loans” among subprime borrowers has generated substantial concern among policymakers. This paper provides the first evidence of substitution between “alternative” and “traditional” credit by exploiting an unexpected positive shock to traditional credit access among payday loan borrowers: the removal of a Chapter 7 bankruptcy flag. We find that the removal of a bankruptcy flag on a credit report results in a sharp increase in access to traditional credit and raises credit scores, credit card limits, and approval rates. However, despite meaningful increases in access to traditional credit, we find no evidence that borrowers reduce their use of payday loans, and our confidence intervals allow us to rule out even very small reductions in payday borrowing. Furthermore, we find evidence that flag removals increase the use of other alternative credit products such as online subprime installment loans. These results indicate that marginally improving access to less expensive formal credit is insufficient to meaningfully shift borrowers away from high cost subprime products. We discuss likely explanations for this including increased marketing of subprime products associated with the flag removal, the imperfect substitutability between cash and credit for low income borrowers, and an insufficiency in the size of the increase in credit access associated with the flag removal. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Revolving credit lines and targeted search

I propose a model of revolving credit lines and targeted search to analyze what accounts for the profitability of the U.S. credit card industry. My analyses lead to two main findings. First, the search friction has minimal impact on the level of profitability of the credit card industry. Most of the profitability is a result of the lender choosing the terms of contract. Second, improved information about consumers accounts for the fall in profitability since the 1980s. Consistent with the data, lenders respond to more information by increasing credit card limits and lowering markups.

The Unprecedented Fall in U.S. Revolving Credit

Revolving credit in the U.S. declined drastically in the last decade after several years of upward trending growth. We show that the Ability to Pay provision of the Credit CARD Act of 2009, which places restrictions on credit card limits, accounts for this decline. Extending a model of revolving credit to analyze this policy, we account for changes in credit statistics by income and age. Although the goal was consumer protection, the policy has led to welfare losses. Even consumers with time inconsistent preferences who could benefit from tighter credit constraints are worse off. An alternative policy considered by policymakers - an interest rate cap - improves welfare.

Shocked by Bank Funding Shocks: Evidence from 500 Million Consumer Credit Cards

Using the near universe of U.S. consumer credit cards, we show that banks transmit their wholesale funding shocks to consumers by reducing their credit card limits. Credit-constrained consumers who are unable to hedge against the transmitted shock by borrowing from other cards experience a stronger and more persistent reduction in their ability to smooth consumption through credit cards. Aggregate credit card balances for credit-constrained consumers decline by 42--71 cents for a $1 reduction in aggregate credit limits due to the funding shock. Our results highlight who bears the real costs of fragile bank funding structures.

Submission on improving acquisition of risk prevention and limit the bad debts of credit cards

To develop a sound personal credit market, especially credit cards, prevent and reduce the increasing bad debts in this market, it is necessary to establish, enhance and supplement the legal framework on issuance, granting credit card limits. Through the research about legal regulations on issuing conditions, granting personal credit limit via credit cards in developed countries in Europe, the U.S., and Canada, this paper aims to present findings on the above issues to contribute to the establishment, enhancement of and supplement to the Vietnam’s legal framework on the issuance and settlement of credit card bad debts.

The Role of Student Loans in Credit Card Access Across Generations

In this paper, we study how a creditor evaluates the costs and benefits of educational services in credit supply decisions. Using the Federal Reserve’s Survey of Consumer Finance data, we measure educational costs from outstanding student loan amounts and student-debt service ratios and estimate their relationship with credit card limits and credit-request rejection rates. We find that households are limited in access to unsecured credit while repaying student debt, implying a prominent role of student debt in generating liquidity constraints. Particularly, we find middle-to-older-aged households have a higher probability to be rejected for credit requests than younger households, who have recently entered the job market while repaying their own debts.

Bounds for the solution to the single-period inventory model with compound renewal process input: An application to setting credit card limits

Motivated by a desire to calculate the optimal credit limit for a credit card account in terms of the card-holder’s purchasing behaviour, we consider a single-period inventory model in which the total value of all attempted purchases increases in jumps throughout the period. If a purchase does not cause the limit to be exceeded, then that purchase is approved and the total value of approved purchases is increased by its value. On the other hand, if the limit is exceeded, then the purchase is rejected and the total value of approved purchases remains at its previous level.We derive an equation for the Laplace–Stieltjes transform of the expected total value of approved purchases at the end of the period. Unfortunately this equation appears to be intractable, a situation that we address by providing upper and lower bounds. We provide numerical examples that show that these upper and lower bounds can be close, which leads to good upper and lower approximations for the optimal limit.