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A class of categorization methods for credit scoring models

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  • Silva, Diego M.B.
  • Pereira, Gustavo H.A.
  • Magalhães, Tiago M.

Abstract

Credit scoring models are usually developed using logistic regression. For several reasons, professionals of this area frequently categorize the quantitative covariates before using them in the model. In this work, we introduce a class of methods for covariate categorization in regression models for binary response variables. Applications to real data and a Monte Carlo simulation study suggest that one of the methods of this class has a better predictive performance and a smaller computational cost than other methods available in the literature.

Suggested Citation

  • Silva, Diego M.B. & Pereira, Gustavo H.A. & Magalhães, Tiago M., 2022. "A class of categorization methods for credit scoring models," European Journal of Operational Research, Elsevier, vol. 296(1), pages 323-331.
    • Handle: RePEc:eee:ejores:v:296:y:2022:i:1:p:323-331
    DOI: 10.1016/j.ejor.2021.04.029
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    References listed on IDEAS

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