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Data Mining Via Multiple Criteria Linear Programming: Applications In Credit Card Portfolio Management

Author

Listed:
  • YONG SHI

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

  • YI PENG

    (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)

  • XIAOWO TANG

    (College of Management, Chinese University of Electronic Science and Technology, Chengdu, Sichuan 610054, China)

Abstract

Data mining becomes a cutting-edge information technology tool in today's competitive business world. It helps the company discover previously unknown, valid, and actionable information from various and large databases for crucial business decisions. This paper provides a promising approach of data mining to classify the credit cardholders' behavior through multiple criteria linear programming. After reviewing the history of linear discriminant analyses, we will describe first a model for classifying two-group (e.g. bad or good) credit cardholder behaviors, and then a three-group (e.g. bad, normal, or good) credit model. Besides the discussion of the modeling structure, we will utilize the well-known commercial software package SAS to implement this technology by using a real-life credit card data warehouse. A number of potential business and financial applications will be finally summarized.

Suggested Citation

  • 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.
    • Handle: RePEc:wsi:ijitdm:v:01:y:2002:i:01:n:s0219622002000038
    DOI: 10.1142/S0219622002000038
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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. Medina-Olivares, Victor & Lindgren, Finn & Calabrese, Raffaella & Crook, Jonathan, 2023. "Joint models of multivariate longitudinal outcomes and discrete survival data with INLA: An application to credit repayment behaviour," European Journal of Operational Research, Elsevier, vol. 310(2), pages 860-873.
    3. 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.
    4. Zhang, Faming & Tadikamalla, Pandu R. & Shang, Jennifer, 2016. "Corporate credit-risk evaluation system: Integrating explicit and implicit financial performances," International Journal of Production Economics, Elsevier, vol. 177(C), pages 77-100.
    5. 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.
    6. Rafał Balina & Marta Idasz-Balina, 2021. "Drivers of Individual Credit Risk of Retail Customers—A Case Study on the Example of the Polish Cooperative Banking Sector," Risks, MDPI, vol. 9(12), pages 1-26, December.

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