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Classifications Of Credit Cardholder Behavior By Using Fuzzy Linear Programming

Author

Listed:
  • JING HE

    (Institute of Systems Science, Academy of Mathematics and Systems Science, The Chinese Academy of Sciences, Beijing 100080, China)

  • XIANTAO LIU

    (School of Business Administration, Southwest Petroleum Institute, Chengdu, Sichuan 610500, China)

  • YONG SHI

    (College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE 68182, USA)

  • WEIXUAN XU

    (Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100080, China)

  • NIAN YAN

    (College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE 68182, USA)

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 real-life practice, statistics and neural networks are the major players to calculate such a score system for prediction. Recently, various multiple linear programming-based classification methods have been promoted for analyzing credit cardholders' behaviors. As a continuation of this research direction, this paper proposes a heuristic classification method by using the fuzzy linear programming (FLP) to discover the bankruptcy patterns of credit cardholders. Instead of identifying a compromise solution for the separation of credit cardholder behaviors, this approach classifies the credit cardholder behaviors by seeking a fuzzy (satisfying) solution obtained from a fuzzy linear program. In this paper, a real-life credit database from a major US bank is used for empirical study which is compared with the results of known multiple linear programming approaches.

Suggested Citation

  • Jing He & Xiantao Liu & Yong Shi & Weixuan Xu & Nian Yan, 2004. "Classifications Of Credit Cardholder Behavior By Using Fuzzy Linear Programming," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 3(04), pages 633-650.
    • Handle: RePEc:wsi:ijitdm:v:03:y:2004:i:04:n:s021962200400129x
    DOI: 10.1142/S021962200400129X
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    References listed on IDEAS

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    1. Yong Shi, 2001. "Multiple Criteria and Multiple Constraint Levels Linear Programming:Concepts, Techniques and Applications," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 4000, February.
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    Citations

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    Cited by:

    1. Zhang, Zhiwang & Gao, Guangxia & Shi, Yong, 2014. "Credit risk evaluation using multi-criteria optimization classifier with kernel, fuzzification and penalty factors," European Journal of Operational Research, Elsevier, vol. 237(1), pages 335-348.
    2. Nikolaos Sariannidis & Stelios Papadakis & Alexandros Garefalakis & Christos Lemonakis & Tsioptsia Kyriaki-Argyro, 2020. "Default avoidance on credit card portfolios using accounting, demographical and exploratory factors: decision making based on machine learning (ML) techniques," Annals of Operations Research, Springer, vol. 294(1), pages 715-739, November.

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