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Credit Prediction Based on Kohonen Network and Survival Analysis  

Ha, Sung-Ho (경북대학교 경상대학 경영학과)
Yang, Jeong-Won (경북대학교 일반대학원 경영학과)
Min, Ji-Hong (경북대학교 일반대학원 경영학과)
Publication Information
Journal of the Korean Operations Research and Management Science Society / v.34, no.2, 2009 , pp. 35-54 More about this Journal
Abstract
The recent economic crisis not only reduces the profit of department stores but also incurs the significance losses caused by the increasing late-payment rate of credit cards. Under this pressure, the scope of credit prediction needs to be broadened from the simple prediction of whether this customer has a good credit or not to the accurate prediction of how much profit can be gained from this customer. This study classifies the delinquent customers of credit card in a Korean department store into homogeneous clusters. Using this information, this study analyzes the repayment patterns for each cluster and develops the credit prediction system to manage the delinquent customers. The model presented by this study uses Kohonen network, which is one of artificial neural networks of data mining technique, to cluster the credit delinquent customers into clusters. Cox proportional hazard model is also used, which is one of survival analysis used in medical statistics, to analyze the repayment patterns of the delinquent customers in each cluster. The presented model estimates the repayment period of delinquent customers for each cluster and introduces the influencing variables on the repayment pattern prediction. Although there are some differences among clusters, the variables about the purchasing frequency in a month and the average number of installment repayment are the most predictive variables for the repayment pattern. The accuracy of the presented system leaches 97.5%.
Keywords
Kohonen Network; Clustering; Cox Proportional Hazard Model; Credit Prediction System;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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