A Research on Constructing a Real-time AlertSystem of Credit Card Issuing Bank

• Main Authors: wen-sheng zhang, 張文生
• Other Authors: Heitmann G.F. Yen
• Format: Others
• Language: zh-TW
• Published: 2001
• Online Access: http://ndltd.ncl.edu.tw/handle/55511761978752390945

碩士 === 中原大學 === 企業管理研究所 === 89 === Comparing to the traditional banking loan, credit card business is characterized as high spread, high risk and globalized business with enormous market potential. While facing the global market competition and economic recession, the core competence for the issuing banks is to conduct credit risk management so as to minimize operating risk and to maximize profit. Credit risk management is a very critical and helpful safeguard against the possible bad debts. Under a well-developed credit risk management alert system, the credit officers will, in time, be able to freeze the credit limit or to reject the related on-going transaction to prevent the potential bad debts and losses when the card holders try to conduct high-risky transaction, to over-utilize the credit limit, and/or to do the funding illegally. This research defines the 162,173 cards issued by one of the issuing banks in Taiwan during the period of 1992~2000 as the population. And the sample size of 1,000 cards are collected by simple random sampling and were assessed by the basic data of card holders, the payment and historical records to analyze the reasons of incurred bad debts. Furthermore, according to the statistical results of Discriminant and Logistic Regression Analyses, the author tries to construct credit risk management alert models for the credit card and then choose one optimal model based on the accuracy of forecasting. The captioned samplings of 1,000 card holders, 900 for experimental purpose and 100 for compared purpose. The experiment group was analyzed and to build the alert models by Discriminant Analysis and Logistic Regression using the SPSS software. And the comparison group was used to test these two models. The accuracy of Logistic Regression was scored at 95%, higher than that of Discriminant Analysis of 94%. Because the fewer variables was required and the higher accuracy for using the Logistic Regression, this analysis is chosen as the optimal alert model in this research. The implementation of a real-time alert system is required to coordinate the authorization system of credit card business. The variables, such as basic data payment record and consumption item of card holders are input into the alert model built in the front-end authorization system to predict the possibility of bad debit associated with the related transaction conducted by the card holders. If the predicted value is located within the set-up alert value, the authorization system will automatically approve the transaction to speed up the authorization procedure for most normal transactions and to improve the transaction efficiency. If not, the front-end system will transmit the related data to the back-end system of issuing bank, the host system or expert judgment will take over for the exceptional or abnormal transactions by the credit control officers adopting appropriate measure to minimize bad debits and losses.