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Survival Mixture Model for Credit Risk Analysis

Author

Listed:
  • Mo Leo S. F.

    (City University of Hong Kong)

  • Yau Kelvin K. W.

    (City University of Hong Kong)

Abstract

The survival mixture model, which is an extension of the ordinary survival model that allows the existence of a fraction of the borrowers to be risk-free, is applied to credit risk analysis. In a regression setting, the effect of borrowers' characteristics on both the risk-free probability and default risk can be assessed simultaneously. Using the C statistic as a measure of accuracy, the survival mixture model shows improved power to discriminate between good' and bad' customers, when compared with other commonly used statistical models for credit risk analysis. A simulation study is conducted to assess the performance of the proposed numerical estimation method. The survival mixture model not only concentrates on the time-to-default of the borrowers, it also predicts the probability of being risk-free. It provides additional information about the borrowers' default risk in relation to their characteristics, which assists the lending institutions to better manage credit risk.

Suggested Citation

  • Mo Leo S. F. & Yau Kelvin K. W., 2010. "Survival Mixture Model for Credit Risk Analysis," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 4(2), pages 1-20, July.
  • Handle: RePEc:bpj:apjrin:v:4:y:2010:i:2:n:5
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    References listed on IDEAS

    as
    1. Wiginton, John C., 1980. "A Note on the Comparison of Logit and Discriminant Models of Consumer Credit Behavior," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 15(03), pages 757-770, September.
    2. Bart Baesens & Rudy Setiono & Christophe Mues & Jan Vanthienen, 2003. "Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation," Management Science, INFORMS, vol. 49(3), pages 312-329, March.
    3. A. C. Antonakis & M. E. Sfakianakis, 2009. "Assessing naive Bayes as a method for screening credit applicants," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(5), pages 537-545.
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