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classifications of Credit Cardholder Behavior by Using Multiple Criteria Non-linear Programming

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Data Mining and Knowledge Management (CASDMKM 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3327))

Abstract

Behavior analysis of credit cardholders is one of the main research topics in credit card portfolio management. Usually, the cardholder’s behavior, especially bankruptcy, is measured by a score of aggregate attributes that describe cardholder’s spending history. In the real-life practice, statistics and neural networks are the major players to calculate such a score system for prediction. Recently, various multiple criteria linear programming based classification methods have been explored for analyzing credit cardholders’ behavior. This paper proposes a multiple criteria non-linear programming (MCNP) approach to discovering the bankruptcy patterns of credit cardholders. A real-life credit database from a major US bank is used for empirical study on MCNP classification. Finally, the comparison of MCNP and other known classification methods is conducted to verify the validation of MCNP method.

This research has been partially supported by a grant from National Excellent Youth Fund under (#70028101), National Natural Science Foundation of China and a grant from the K.C. Wong Education Foundation (2003), Chinese Academy of Sciences.

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He, J., Shi, Y., Xu, W. (2004). classifications of Credit Cardholder Behavior by Using Multiple Criteria Non-linear Programming. In: Shi, Y., Xu, W., Chen, Z. (eds) Data Mining and Knowledge Management. CASDMKM 2004. Lecture Notes in Computer Science(), vol 3327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30537-8_17

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  • DOI: https://doi.org/10.1007/978-3-540-30537-8_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23987-1

  • Online ISBN: 978-3-540-30537-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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