Credit Risk Evaluation Using a C-Variable Least Squares Support Vector Classification Model

  • Lean Yu
  • Shouyang Wang
  • K. K. Lai
Part of the Communications in Computer and Information Science book series (CCIS, volume 35)

Abstract

Credit risk evaluation is one of the most important issues in financial risk management. In this paper, a C-variable least squares support vector classification (C-VLSSVC) model is proposed for credit risk analysis. The main idea of this model is based on the prior knowledge that different classes may have different importance for modeling and more weights should be given to those classes with more importance. The C-VLSSVC model can be constructed by a simple modification of the regularization parameter in LSSVC, whereby more weights are given to the lease squares classification errors with important classes than the lease squares classification errors with unimportant classes while keeping the regularized terms in its original form. For illustration purpose, a real-world credit dataset is used to test the effectiveness of the C-VLSSVC model.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Altman, E.I.: Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance 23, 89–609 (1968)Google Scholar
  2. 2.
    Wiginton, J.C.: A note on the comparison of logit and discriminant models of consumer credit behaviour. Journal of Financial Quantitative Analysis 15, 757–770 (1980)CrossRefGoogle Scholar
  3. 3.
    Grablowsky, B.J., Talley, W.K.: Probit and discriminant functions for classifying credit applicants: A comparison. Journal of Economic Business 33, 254–261 (1981)Google Scholar
  4. 4.
    Glover, F.: Improved Linear Programming Models for Discriminant Analysis. Decision Science 21, 771–785 (1990)CrossRefGoogle Scholar
  5. 5.
    Henley, W.E., Hand, D.J.: A k-NN classifier for assessing consumer credit risk. Statistician 45, 77–95 (1996)CrossRefGoogle Scholar
  6. 6.
    Yu, L., Wang, S.Y., Lai, K.K.: Credit risk assessment with a multistage neural network ensemble learning approach. Expert Systems with Applications 34(2), 1434–1444 (2008a)CrossRefGoogle Scholar
  7. 7.
    Chen, M.C., Huang, S.H.: Credit scoring and rejected instances reassigning through evolutionary computation techniques. Expert Systems with Applications 24, 433–441 (2003)CrossRefGoogle Scholar
  8. 8.
    Yu, L., Wang, S.Y., Lai, K.K., Zhou, L.G.: Bio-Inspired Credit Risk Analysis - Computational Intelligence with Support Vector Machines. Springer, Berlin (2008b)CrossRefGoogle Scholar
  9. 9.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)CrossRefMATHGoogle Scholar
  10. 10.
    Suykens, J.A.K., Vandewalle, J.: Least squares support vector machine classifiers. Neural Processing Letters 9(3), 293–300 (1999)MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    Zhou, L.G., Lai, K.K., Yu, L.: Credit scoring using support vector machines with direct search for parameters selection. Soft Computing 13, 149–155 (2009)CrossRefMATHGoogle Scholar
  12. 12.
    Yu, L., Wang, S.Y., Lai, K.K.: An Intelligent-Agent-Based Fuzzy Group Decision Making Model for Financial Multicriteria Decision Support: The Case of Credit Scoring. European Journal of Operational Research 195(3), 942–959 (2009)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Lean Yu
    • 1
  • Shouyang Wang
    • 1
  • K. K. Lai
    • 2
  1. 1.Institute of Systems Science, Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijingChina
  2. 2.Department of Management SciencesCity University of Hong KongHong KongChina

Personalised recommendations