A change detection model for credit card usage behavior

Authors:
Chieh-Yuan Tsai;Jing-Chung Wang;Chih-Jung Chen
Affiliations:
Department of Industrial Engineering and Management, Yuan-Ze University, Chung-Li, Taoyuan, Taiwan, R.O.C.;Department of Industrial Engineering and Management, Yuan-Ze University, Chung-Li, Taoyuan, Taiwan, R.O.C.;Department of Industrial Engineering and Management, Yuan-Ze University, Chung-Li, Taoyuan, Taiwan, R.O.C.
Venue:
CIMMACS'06 Proceedings of the 5th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics
Year:
2006

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Abstract

In recent year, credit card has been one of the most attractive financial products all over the world. The magnificent increase in credit card market leads card issuers put more attentions on how to understand their customers. This research proposes a detection model to identify usage behavior change patterns of credit card customers in two time periods. In the model, customer profiles and purchase transactions of two time periods are retrieved from card issuer databases. Then, a usage behavior rule set for each time period is generated using Apriori association rule algorithm. Finally, significant usage behavior change patterns are identified through rule set comparison using defined similarity and difference measures. The proposed model has been successfully implemented using real credit card data provided by a commercial bank in Taiwan. Several marketing strategies are suggested according the analysis finding. With the proposed model, card issuers can detect critical usage behavior changes and allocate their limited resource to establish suitable marketing strategy for their customers.