Support Vector Machines for Credit Scoring: Extension to Non Standard Cases

  • Klaus B. Schebesch
  • Ralf Stecking
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


Credit scoring is being used in order to assign credit applicants to good and bad risk classes. This paper investigates the credit scoring performance of support vector machines (SVM) with weighted classes and moderated outputs. First, we consider the adjustment of support vector machines for credit scoring to a set of non standard situations important to practitioners. Such more sophisticated credit scoring systems will adapt to vastly different proportions of credit worthiness between sample and population. Different costs for different types of misclassification will also be handled. Second, sigmoid output mapping is used to derive default probabilities, important for constructing rating systems and a step towards more “personalized” credit contracts.


False Alarm Rate Class Weight Tenfold Cross Validation Default Probability Brier Score 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. KWOK, J.T. (1999): Moderating the Outputs of Support Vector Machine Classifiers. IEEE Transactions on Neural Networks, 10,5, 1018–1031.CrossRefGoogle Scholar
  2. LIN, Y. (1999): Support Vector Machines and the Bayes rule in classification. Technical Report 1014, Dept. of Statistics, University of Wisconsin.Google Scholar
  3. LIN, Y., LEE, Y., and WAHBA, G. (2002): Support Vector Machines for Classification in Nonstandard Situations. Machine Learning, 46,1–3, 191–202.CrossRefGoogle Scholar
  4. PLATT, J.C. (1999): Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods. In: A.J. Smola, P. Bartlett, B. Schölkopf, and C. Schuurmans (Eds.): Advances in Large Margin Classifiers, MIT Press, Cambridge, MA.Google Scholar
  5. SCHEBESCH, K.B. and STECKING, R. (2003): Support Vector Machines for Credit Applicants: Detecting Typical and Critical Regions. In: Credit Scoring & Credit Control VIII, Credit Research Center, University of Edinburgh, 3–5 September 2003, 13pp.Google Scholar
  6. SCHÖLKOPF, B. and SMOLA, A. (2002): Learning with Kernels. The MIT Press, Cambridge, MA.Google Scholar
  7. STECKING, R. (2003): Credit Scoring im Baukreditwesen, in H. Schaefer (Ed.): Kredit und Risiko: Basel II und die Konsequenzen für Banken und Mittelstand, Metropolis-Verlag, Marburg, 45–56.Google Scholar
  8. STECKING, R. and SCHEBESCH, K.B. (2003): Support Vector Machines for Credit Scoring: Comparing to and Combining with some Traditional Classification Methods, in: M. Schader, W. Gaul, and M. Vichi (Eds.): Between Data Science and Applied Data Analysis, Springer, Berlin, 604–612.Google Scholar
  9. STEIN, R.M. (2002): Benchmarking Default Prediction Models: Pitfalls and Remedies in Model Validation. Technical Report 020305, Moody’s KMV, New York.Google Scholar

Copyright information

© Springer-Verlag Berlin · Heidelberg 2005

Authors and Affiliations

  • Klaus B. Schebesch
    • 1
  • Ralf Stecking
    • 1
  1. 1.Institut für Konjunktur- und StrukturforschungUniversität BremenBremenGermany

Personalised recommendations