Credit Risk Evaluation with Least Square Support Vector Machine

  • Kin Keung Lai
  • Lean Yu
  • Ligang Zhou
  • Shouyang Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4062)

Abstract

Credit risk evaluation has been the major focus of financial and banking industry due to recent financial crises and regulatory concern of Basel II. Recent studies have revealed that emerging artificial intelligent techniques are advantageous to statistical models for credit risk evaluation. In this study, we discuss the use of least square support vector machine (LSSVM) technique to design a credit risk evaluation system to discriminate good creditors from bad ones. Relative to the Vapnik’s support vector machine, the LSSVM can transform a quadratic programming problem into a linear programming problem thus reducing the computational complexity. For illustration, a published credit dataset for consumer credit is used to validate the effectiveness of the LSSVM.

Keywords

Credit risk evaluation least square support vector machine 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kin Keung Lai
    • 1
    • 2
  • Lean Yu
    • 2
    • 3
  • Ligang Zhou
    • 2
  • Shouyang Wang
    • 1
    • 3
  1. 1.College of Business AdministrationHunan UniversityChangshaChina
  2. 2.Department of Management SciencesCity University of Hong KongKowloon, Hong Kong
  3. 3.Institute of Systems Science, Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijingChina

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