MCLP-based Methods for Improving "Bad" Catching Rate in Credit Cardholder Behavior Analysis
Li, Aihua and 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 1568-4946Full text for this resource is not available from the Research Repository.
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.
|Uncontrolled Keywords:||ResPubID18728, ResPubID18997. credit card, fuzzy linear programming, multi criteria linear programming, balanced, risk control, catching rate|
|Subjects:||FOR Classification > 0806 Information Systems
Faculty/School/Research Centre/Department > School of Engineering and Science
FOR Classification > 0803 Computer Software
|Date Deposited:||05 Sep 2011 23:04|
|Last Modified:||07 May 2012 23:57|
|ePrint Statistics:||View download statistics for this item|
|Citations in Scopus:||14 - View on Scopus|
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