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A non‐default rate regression model for credit scoring

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  • Gladys D. C. Barriga
  • Vicente G. Cancho
  • Francisco Louzada

Abstract

In this paper, we propose a new non‐default rate survival model. Our approach enables different underlying activation mechanisms which lead to the event of interest. The number of competing causes, which may be responsible for the occurrence of the event of interest, is assumed to follow a geometric distribution, while the time to event is assumed to follow an inverse Weibull distribution. An advantage of our approach is to accommodate all activation mechanisms based on order statistics. We explore the use of maximum likelihood estimation procedure. Simulation studies are performed and experimental results are illustrated based on a real Brazilian bank personal loan portfolio data. Copyright © 2015 John Wiley & Sons, Ltd.

Suggested Citation

  • Gladys D. C. Barriga & Vicente G. Cancho & Francisco Louzada, 2015. "A non‐default rate regression model for credit scoring," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 31(6), pages 846-861, November.
    • Handle: RePEc:wly:apsmbi:v:31:y:2015:i:6:p:846-861
    DOI: 10.1002/asmb.2112
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    Cited by:

    1. Yang, Qi & He, Haijin & Lu, Bin & Song, Xinyuan, 2022. "Mixture additive hazards cure model with latent variables: Application to corporate default data," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
    2. Nehrebecka Natalia, 2018. "Predicting the Default Risk of Companies. Comparison of Credit Scoring Models: Logit Vs Support Vector Machines," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 22(2), pages 54-73, June.

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