4.7 Article

Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects

Journal

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
Volume 297, Issue 3, Pages 1178-1192

Publisher

ELSEVIER
DOI: 10.1016/j.ejor.2021.06.053

Keywords

Risk management; Credit scoring; Machine learning; Interpretability; Econometrics

Funding

  1. ANR program MultiRisk [ANR-16-CE26-0015-01]
  2. ANR program CaliBank [ANR-19-CE26-0002-02]
  3. Chair ACPR/Risk Foundation: Regulation and Systemic Risk

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In the context of credit scoring, a high-performance and interpretable credit scoring method called penalised logistic tree regression (PLTR) is proposed, which uses information from decision trees to improve logistic regression performance, allowing for capturing non-linear effects in credit scoring data while maintaining interpretability.
In the context of credit scoring, ensemble methods based on decision trees, such as the random forest method, provide better classification performance than standard logistic regression models. However, logistic regression remains the benchmark in the credit risk industry mainly because the lack of interpretability of ensemble methods is incompatible with the requirements of financial regulators. In this paper, we propose a high-performance and interpretable credit scoring method called penalised logistic tree regression (PLTR), which uses information from decision trees to improve the performance of logistic regression. Formally, rules extracted from various short-depth decision trees built with original predictive variables are used as predictors in a penalised logistic regression model. PLTR allows us to capture non-linear effects that can arise in credit scoring data while preserving the intrinsic interpretability of the logistic regression model. Monte Carlo simulations and empirical applications using four real credit default datasets show that PLTR predicts credit risk significantly more accurately than logistic regression and compares competitively to the random forest method. (c) 2021 Published by Elsevier B.V.

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