Predicting repayment of the credit card debt

被引:22
|
作者
Ha, Sung Ho [1 ]
Krishnan, Ramayya [2 ]
机构
[1] Kyungpook Natl Univ, Sch Business Adm, Taegu, South Korea
[2] Carnegie Mellon Univ, Sch Informat Syst & Management, Pittsburgh, PA 15213 USA
关键词
Delinquency risk; Credit management; Repayment; Data mining; ENTERPRISE RISK-MANAGEMENT; NEURAL-NETWORKS; SCORING MODELS; VENDOR SELECTION; MINING APPROACH; CLASSIFICATION; LOANS;
D O I
10.1016/j.cor.2010.10.032
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The recent economic crisis not only reduces the profit of retailer stores but also incurs the significant losses caused by increasing the late-payment rate of credit cards. Under this pressure, the scope of credit prediction needs to be broadened to the customer management after delinquency occurs. In doing so, this study clusters the credit card debtors in a retail company into homogeneous segments by using a self-organizing map. This study then develops credit prediction models to recognize the repayment patterns of each segment by using a Cox proportional hazard analysis. The credit prediction models are evaluated and the managerial implications of the study are provided. (C) 2010 Elsevier Ltd. All rights reserved.
引用
收藏
页码:765 / 773
页数:9
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