Credit card

About: Credit card is a research topic. Over the lifetime, 16998 publications have been published within this topic receiving 347688 citations. The topic is also known as: 💳 & App-O-Rama.

Credit scoring with a data mining approach based on support vector machines

Abstract: The credit card industry has been growing rapidly recently, and thus huge numbers of consumers' credit data are collected by the credit department of the bank. The credit scoring manager often evaluates the consumer's credit with intuitive experience. However, with the support of the credit classification model, the manager can accurately evaluate the applicant's credit score. Support Vector Machine (SVM) classification is currently an active research area and successfully solves classification problems in many domains. This study used three strategies to construct the hybrid SVM-based credit scoring models to evaluate the applicant's credit score from the applicant's input features. Two credit datasets in UCI database are selected as the experimental data to demonstrate the accuracy of the SVM classifier. Compared with neural networks, genetic programming, and decision tree classifiers, the SVM classifier achieved an identical classificatory accuracy with relatively few input features. Additionally, combining genetic algorithms with SVM classifier, the proposed hybrid GA-SVM strategy can simultaneously perform feature selection task and model parameters optimization. Experimental results show that SVM is a promising addition to the existing data mining methods.

Contract Design and Self-Control: Theory and Evidence

Abstract: How do rational firms respond to consumer biases? In this paper we analyze the profit-maximizing contract design of firms if consumers have time-inconsistent preferences and are partially naive about it. We consider markets for two types of goods: goods with immediate costs and delayed benefits (investment goods) such as health club attendance, and goods with immediate benefits and delayed costs (leisure goods) such as credit card-financed consumption. We establish three features of the profit-maximizing contract design with partially naive time-inconsistent consumers. First, firms price investment goods below marginal cost. Second, firms price leisure goods above marginal cost. Third, for all types of goods firms introduce switching costs and charge back-loaded fees. The contractual design targets consumer misperception of future consumption and underestimation of the renewal probability. The predictions of the theory match the empirical contract design in the credit card, gambling, health club, life insurance, mail order, mobile phone, and vacation time-sharing industries. We also show that time inconsistency has adverse effects on consumer welfare only if consumers are naive.

Centralized consumer cash value accumulation system for multiple merchants

Abstract: A centralized system of accumulating cash value for consumers based upon point-of-sale transactions with multiple merchants is disclosed wherein for each transaction, the consumer's account number (such as the Social Security number) which may be different from the consumer's credit card account number, for example, is transmitted to a central system along with data identifying the merchant and a credit value for the transaction The credit value may be based upon predetermined incentives associated with the transaction such as coupons, rebates or discounts, and/or upon a credit rate determined by the merchant applied to the amount of the transaction At the central location, a cash value for that consumer is incremented by the credit value and a bill value for that merchant is similarly incremented Periodically, the merchants are billed for the accumulated bill value or credited for any third party incentive amounts confirmed at the central location Also, at selected intervals, consumers are given access to their respective accumulated cash values by either a check in that amount or through a funds dispensing electronic terminal access or the like

The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients

Abstract: This research aimed at the case of customers' default payments in Taiwan and compares the predictive accuracy of probability of default among six data mining methods. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of classification - credible or not credible clients. Because the real probability of default is unknown, this study presented the novel ''Sorting Smoothing Method'' to estimate the real probability of default. With the real probability of default as the response variable (Y), and the predictive probability of default as the independent variable (X), the simple linear regression result (Y=A+BX) shows that the forecasting model produced by artificial neural network has the highest coefficient of determination; its regression intercept (A) is close to zero, and regression coefficient (B) to one. Therefore, among the six data mining techniques, artificial neural network is the only one that can accurately estimate the real probability of default.