MCLP-based methods for improving "Bad" catching rate in credit cardholder behavior analysis

Authors:
Aihua Li;Yong Shi;Jing He
Affiliations:
Research Centre on Fictitious Economy & Data Science, Chinese Academy of Sciences, Beijing 10080, China and School of Management Science and Engineering, Central University of Finance and Economic ...;School of Management Science and Engineering, Central University of Finance and Economics, Beijing 100080, China and College of Information Science and Technology, University of Nebraska at Omaha, ...;School of Management Science and Engineering, Central University of Finance and Economics, Beijing 100080, China
Venue:
Applied Soft Computing
Year:
2008

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Abstract

Cardholders' behavior prediction is an important issue in credit card portfolio management. As a promising data mining approach, multiple criteria programming (MCLP) has been successfully applied to classify credit cardholders' behavior into two groups. In order to better control credit risk for financial institutes, this paper proposes three methods based on MCLP to improve the ''Bad'' catching accuracy rate. One is called MCLP with unbalanced training set selection, the second is called fuzzy linear programming (FLP) method with moving boundary, and the third is called penalized multi criteria linear programming (PMCLP). The experimental examples demonstrate the promising performance of these methods.