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A logistic regression model for consumer default risk

Author

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  • Eliana Costa e Silva
  • Isabel Cristina Lopes
  • Aldina Correia
  • Susana Faria

Abstract

In this study, a logistic regression model is applied to credit scoring data from a given Portuguese financial institution to evaluate the default risk of consumer loans. It was found that the risk of default increases with the loan spread, loan term and age of the customer, but decreases if the customer owns more credit cards. Clients receiving the salary in the same banking institution of the loan have less chances of default than clients receiving their salary in another institution. We also found that clients in the lowest income tax echelon have more propensity to default. The model predicted default correctly in 89.79% of the cases.

Suggested Citation

  • Eliana Costa e Silva & Isabel Cristina Lopes & Aldina Correia & Susana Faria, 2020. "A logistic regression model for consumer default risk," Journal of Applied Statistics, Taylor & Francis Journals, vol. 47(13-15), pages 2879-2894, November.
    • Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2879-2894
    DOI: 10.1080/02664763.2020.1759030
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    Cited by:

    1. García-Céspedes, Rubén & Moreno, Manuel, 2022. "The generalized Vasicek credit risk model: A Machine Learning approach," Finance Research Letters, Elsevier, vol. 47(PA).
    2. Jianhua Jiang & Xianqiu Meng & Yang Liu & Huan Wang, 2022. "An Enhanced TSA-MLP Model for Identifying Credit Default Problems," SAGE Open, , vol. 12(2), pages 21582440221, April.

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