IDEAS home Printed from https://ideas.repec.org/a/wsi/ijitdm/v01y2002i01ns0219622002000038.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0219622002000038
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0219622002000038?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wsi:ijitdm:v:01:y:2002:i:01:n:s0219622002000038. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/ijitdm/ijitdm.shtml .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.