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

Full text for this resource is not available from the Research Repository.

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

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.

Dimensions Badge

Altmetric Badge

Item type Article
URI https://vuir.vu.edu.au/id/eprint/3789
DOI 10.1016/j.asoc.2007.02.014
Official URL http://www.sciencedirect.com/science/article/pii/S...
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
Download/View statistics View download statistics for this item

Search Google Scholar

Repository staff login