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MCLP-based Methods for Improving "Bad" Catching Rate in Credit Cardholder Behavior Analysis

Li, Aihua, Shi, Yong and He, Jing (2008) MCLP-based Methods for Improving "Bad" Catching Rate in Credit Cardholder Behavior Analysis. Applied Soft Computing, 8 (3). pp. 1259-1265. ISSN 1568-4946

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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.

Item Type: Article
Uncontrolled Keywords: ResPubID18728, ResPubID18997. credit card, fuzzy linear programming, multi criteria linear programming, balanced, risk control, catching rate
Subjects: Current > FOR Classification > 0806 Information Systems
Historical > Faculty/School/Research Centre/Department > School of Engineering and Science
Current > FOR Classification > 0803 Computer Software
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Depositing User: VUIR
Date Deposited: 05 Sep 2011 23:04
Last Modified: 07 May 2012 23:57
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Citations in Scopus: 24 - View on Scopus

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