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Discovering Credit Cardholders’ Behavior by Multiple Criteria Linear Programming

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Listed:
  • Gang Kou
  • Yi Peng
  • Yong Shi
  • Morgan Wise
  • Weixuan Xu

Abstract

In credit card portfolio management, predicting the cardholder’s spending behavior is a key to reduce the risk of bankruptcy. Given a set of attributes for major aspects of credit cardholders and predefined classes for spending behaviors, this paper proposes a classification model by using multiple criteria linear programming to discover behavior patterns of credit cardholders. It shows a general classification model that can theoretically handle any class-size. Then, it focuses on a typical case where the cardholders’ behaviors are predefined as four classes. A dataset from a major US bank is used to demonstrate the applicability of the proposed method. Copyright Springer Science + Business Media, Inc. 2005

Suggested Citation

  • Gang Kou & Yi Peng & Yong Shi & Morgan Wise & Weixuan Xu, 2005. "Discovering Credit Cardholders’ Behavior by Multiple Criteria Linear Programming," Annals of Operations Research, Springer, vol. 135(1), pages 261-274, March.
    • Handle: RePEc:spr:annopr:v:135:y:2005:i:1:p:261-274:10.1007/s10479-005-6245-5
    DOI: 10.1007/s10479-005-6245-5
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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.
    2. Yachen Lin, 2002. "Improvement On Behavior Scores By Dual-Model Scoring System," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 1(01), pages 153-164.
    3. Freed, Ned & Glover, Fred, 1981. "Simple but powerful goal programming models for discriminant problems," European Journal of Operational Research, Elsevier, vol. 7(1), pages 44-60, May.
    4. Yong Shi & Yi Peng & Weixuan Xu & Xiaowo Tang, 2002. "Data Mining Via Multiple Criteria Linear Programming: Applications In Credit Card Portfolio Management," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 1(01), pages 131-151.
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    Citations

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    Cited by:

    1. Wikil Kwak & Yong Shi & Gang Kou, 2012. "Bankruptcy prediction for Korean firms after the 1997 financial crisis: using a multiple criteria linear programming data mining approach," Review of Quantitative Finance and Accounting, Springer, vol. 38(4), pages 441-453, May.
    2. Po-Lung Yu & Yen-Chu Chen, 2012. "Dynamic multiple criteria decision making in changeable spaces: from habitual domains to innovation dynamics," Annals of Operations Research, Springer, vol. 197(1), pages 201-220, August.
    3. Lean Yu & Zebin Yang & Ling Tang, 2016. "A novel multistage deep belief network based extreme learning machine ensemble learning paradigm for credit risk assessment," Flexible Services and Manufacturing Journal, Springer, vol. 28(4), pages 576-592, December.
    4. Maria A. S. Xavier & Fernando A. F. Ferreira & José P. Esperança, 2021. "An intuition-based evaluation framework for social credit applications," Annals of Operations Research, Springer, vol. 296(1), pages 571-590, January.
    5. Lean Yu & Xinxie Li & Ling Tang & Zongyi Zhang & Gang Kou, 2015. "Social credit: a comprehensive literature review," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 1(1), pages 1-18, December.
    6. Gang Kou & Chunwei Lou, 2012. "Multiple factor hierarchical clustering algorithm for large scale web page and search engine clickstream data," Annals of Operations Research, Springer, vol. 197(1), pages 123-134, August.
    7. Yu, Lean & Huang, Xiaowen & Yin, Hang, 2020. "Can machine learning paradigm improve attribute noise problem in credit risk classification?," International Review of Economics & Finance, Elsevier, vol. 70(C), pages 440-455.

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