Abstract
In this paper, we present a set of classification models by using multiple criteria linear programming (MCLP) to discover the various behaviors of credit cardholders. In credit card portfolio management, predicting the cardholder’s spending behavior is a key to reduce the risk of bankruptcy. Given a set of predicting variables (attributes) that describes all possible aspects of credit cardholders, we first present a set of general classification models that can theoretically handle any size of multiple-group cardholders’ behavior problems. Then, we implement the algorithm of the classification models by using SAS and Linux platforms. Finally, we test the models on a special case where the cardholders’ behaviors are predefined as five classes: (i) bankrupt charge-off; (ii) non-bankrupt charge-off; (iii) delinquent; (iv) current and (v) outstanding on a real-life credit card data warehouse. As a part of the performance analysis, a data testing comparison between the MCLP and induction decision tree approaches is demonstrated. These findings suggest that the MCLP-data mining techniques have a great potential in discovering knowledge patterns from a large-scale real-life database or data warehouse.
This research has been partially supported by a grant under (DUE-9796243), the National Science Foundation of USA, a National Excellent Youth Fund under (#70028101), National Natural Science Foundation of China and a grant from the K.C. Wong Education Foundation, Chinese Academy of Sciences.
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Kou, G., Peng, Y., Shi, Y., Xu, W. (2003). A Set of Data Mining Models to Classify Credit Cardholder Behavior. In: Sloot, P.M.A., Abramson, D., Bogdanov, A.V., Gorbachev, Y.E., Dongarra, J.J., Zomaya, A.Y. (eds) Computational Science — ICCS 2003. ICCS 2003. Lecture Notes in Computer Science, vol 2658. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44862-4_7
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DOI: https://doi.org/10.1007/3-540-44862-4_7
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