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Classifying Credit Card Accounts For Business Intelligence And Decision Making: A Multiple-Criteria Quadratic Programming Approach

Author

Listed:
  • YONG SHI

    (Chinese Academy of Sciences Research Center on Data Technology and Knowledge Economy, Beijing 100039, China;
    Peter Kiewit Institute of Information Science, Technology & Engineering, University of Nebraska, Omaha, NE 68182, USA)

  • YI PENG

    (Peter Kiewit Institute of Information Science, Technology & Engineering, University of Nebraska, Omaha, NE 68182, USA)

  • GANG KOU

    (Peter Kiewit Institute of Information Science, Technology & Engineering, University of Nebraska, Omaha, NE 68182, USA)

  • ZHENGXIN CHEN

    (Peter Kiewit Institute of Information Science, Technology & Engineering, University of Nebraska, Omaha, NE 68182, USA)

Abstract

A major challenge in credit card portfolio management is to classify and predict credit cardholders' behaviors in a reliable precision because cardholders' behaviors are rather dynamic in nature. This is crucial for creditors because it allows them to take proactive actions and minimize charge-off and bankruptcy losses. Although the methods used in the area of credit portfolio management have improved significantly, the demand for alternative and sophisticated analytical tools is still strong.The objective of this paper is to propose a multiple criteria quadratic programming (MCQP) to classify credit card accounts for business intelligence and decision making. MCQP is intended to predict credit cardholders' behaviors from a nonlinear perspective that is justifiable because both the objective functions and constraints in credit card accounts classification may be nonlinear. Using a real-life credit card dataset from a major US bank, the MCQP method is compared with popular and similar classification methods: linear discriminant analysis, decision tree, multiple criteria linear programming, support vector machine, and neural network. The results indicate that MCQP is a promising business intelligence method in credit card portfolio management.

Suggested Citation

  • Yong Shi & Yi Peng & Gang Kou & Zhengxin Chen, 2005. "Classifying Credit Card Accounts For Business Intelligence And Decision Making: A Multiple-Criteria Quadratic Programming Approach," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 4(04), pages 581-599.
    • Handle: RePEc:wsi:ijitdm:v:04:y:2005:i:04:n:s0219622005001775
    DOI: 10.1142/S0219622005001775
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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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    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. 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.
    3. 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.
    4. Fu-Ling Cai & Xiuwu Liao & Kan-Liang Wang, 2012. "An interactive sorting approach based on the assignment examples of multiple decision makers with different priorities," Annals of Operations Research, Springer, vol. 197(1), pages 87-108, August.

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