Personal Credit Management

  • Yong Shi
  • Yingjie Tian
  • Gang Kou
  • Yi Peng
  • Jianping Li
Part of the Advanced Information and Knowledge Processing book series (AI&KP)


The most commonly used methods in predicting credit card defaulters are credit scoring models. Based on their applications in credit management, the scoring models can be classified into two categories: the first category concerns about application scores and the second category concerns behavior scores. Specifically, behavior scores are used to determine “raising or lowering the credit limit; how the account should be treated with regard to promotional or marketing decisions; and when action should be taken on a delinquent account”. Behavior scoring models utilize various techniques to identify attributes that can effectively separate credit cardholders behaviors. In this chapter, using the real-life credit card dataset, we first conduct the MCQP classification, then compare the performance of MCQP with MCLP, linear discriminant analysis (LDA), decision tree (DT), support vector machine (SVM), and neural network (NN) methods in terms of predictive accuracy.


Linear Discriminant Analysis Credit Card Catch Rate Multiple Criterion Linear Programming Adjusted Boundary 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Yong Shi
    • 1
    • 2
  • Yingjie Tian
    • 1
  • Gang Kou
    • 3
  • Yi Peng
    • 3
  • Jianping Li
    • 4
  1. 1.Research Center on Fictitious Economy and Data ScienceChinese Academy of SciencesBeijingChina
  2. 2.College of Information Science & TechnologyUniversity of Nebraska at OmahaOmahaUSA
  3. 3.School of Management and EconomicsUniversity of Electronic Science and Technology of ChinaChengduChina
  4. 4.Institute of Policy and ManagementChinese Academy of SciencesBeijingChina

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