EconPapers    
Economics at your fingertips  
 

Machine Learning for Credit Scoring: Improving Logistic Regression with Non Linear Decision Tree Effects

Elena Ivona Dumitrescu, Sullivan Hué, Christophe Hurlin and Sessi Tokpavi ()
Additional contact information
Sessi Tokpavi: LEO - Laboratoire d'Économie d'Orleans - UO - Université d'Orléans - UT - Université de Tours

Post-Print from HAL

Abstract: 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

Keywords: Risk management; Credit scoring; Machine learning; Interpretability; Econometrics (search for similar items in EconPapers)
Date: 2022
New Economics Papers: this item is included in nep-ban, nep-big, nep-cmp, nep-ecm and nep-rmg
Note: View the original document on HAL open archive server: https://hal.science/hal-03331114
References: Add references at CitEc
Citations: View citations in EconPapers (42)

Published in European Journal of Operational Research, 2022, 297 (3), pp.1178-1192. ⟨10.1016/j.ejor.2021.06.053⟩

Downloads: (external link)
https://hal.science/hal-03331114/document (application/pdf)

Related works:
Journal Article: Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects (2022) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03331114

DOI: 10.1016/j.ejor.2021.06.053

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().

 
Page updated 2025-03-22
Handle: RePEc:hal:journl:hal-03331114