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Annals of Operations Research

, Volume 135, Issue 1, pp 261–274 | Cite as

Discovering Credit Cardholders’ Behavior by Multiple Criteria Linear Programming

  • Gang Kou
  • Yi Peng
  • Yong Shi
  • Morgan Wise
  • Weixuan Xu
Article

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.

Keywords

data mining classification multi-criteria linear programming credit cardholders’ behavior SAS algorithms 

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Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  • Gang Kou
    • 1
  • Yi Peng
    • 1
  • Yong Shi
    • 2
    • 3
  • Morgan Wise
    • 4
  • Weixuan Xu
    • 5
  1. 1.College of Information Science and TechnologyUniversity of Nebraska at OmahaOmahaUSA
  2. 2.College of Information Science and TechnologyUniversity of Nebraska at OmahaOmahaUSA
  3. 3.Graduate School of Chinese Academy of SciencesBeijingChina
  4. 4.First National Bank of OmahaOmahaUSA
  5. 5.Institute of Policy and ManagementChinese Academy of SciencesBeijingChina

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