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The impact of class imbalance in logistic regression models for low-default portfolios in credit risk

Author

Listed:
  • Willem D. Schutte
  • Charl Pretorius
  • Neill Smit
  • Leandra van der Merwe
  • Robert Maxwell

Abstract

In this paper, we study how class imbalance, typical of low-default credit portfolios, affects the performance of logistic regression models. Using a simulation study with controlled data-generating mechanisms, we vary (i) the level of class imbalance and (ii) the strength of association between the predictors and the response. The results show that, for a given strength of association, achievable classification accuracy deteriorates markedly as the event rate decreases, and the optimal classification cut-off shifts with the level of imbalance. In contrast, the Gini coefficient is comparatively stable with respect to class imbalance once sample sizes are sufficiently large, even when classification accuracy is strongly affected. As a practical guideline, we summarise attainable classification performance as a function of the event rate and strength of association between the predictors and the response.

Suggested Citation

  • Willem D. Schutte & Charl Pretorius & Neill Smit & Leandra van der Merwe & Robert Maxwell, 2026. "The impact of class imbalance in logistic regression models for low-default portfolios in credit risk," Papers 2602.19663, arXiv.org.
    • Handle: RePEc:arx:papers:2602.19663
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    References listed on IDEAS

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    1. King, Gary & Zeng, Langche, 2001. "Logistic Regression in Rare Events Data," Political Analysis, Cambridge University Press, vol. 9(2), pages 137-163, January.
    2. Thomas, Lyn C., 2009. "Consumer Credit Models: Pricing, Profit and Portfolios," OUP Catalogue, Oxford University Press, number 9780199232130.
    3. Crone, Sven F. & Finlay, Steven, 2012. "Instance sampling in credit scoring: An empirical study of sample size and balancing," International Journal of Forecasting, Elsevier, vol. 28(1), pages 224-238.
    4. Misuk Kim & Kyu-Baek Hwang, 2022. "An empirical evaluation of sampling methods for the classification of imbalanced data," PLOS ONE, Public Library of Science, vol. 17(7), pages 1-22, July.
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