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

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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


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.

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Item type Article
DOI 10.1016/j.asoc.2007.02.014
Official URL
Subjects Historical > FOR Classification > 0806 Information Systems
Historical > Faculty/School/Research Centre/Department > School of Engineering and Science
Historical > FOR Classification > 0803 Computer Software
Keywords ResPubID18728, ResPubID18997. credit card, fuzzy linear programming, multi criteria linear programming, balanced, risk control, catching rate
Citations in Scopus 26 - View on Scopus
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