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Machine Learning or Econometrics for Credit Scoring: Let's Get the Best of Both Worlds

Author

Listed:
  • Elena Dumitrescu

    (EconomiX - EconomiX - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique)

  • Sullivan Hué

    (LEO - Laboratoire d'Économie d'Orleans - UO - Université d'Orléans - UT - Université de Tours)

  • Christophe Hurlin

    (LEO - Laboratoire d'Économie d'Orleans - UO - Université d'Orléans - UT - Université de Tours)

  • Sessi Tokpavi

    (LEO - Laboratoire d'Économie d'Orleans - UO - Université d'Orléans - UT - Université de Tours)

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 pro- pose to obtain the best of both worlds by introducing 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 pairs of 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. JEL Classification: G10 C25, C53

Suggested Citation

  • Elena Dumitrescu & Sullivan Hué & Christophe Hurlin & Sessi Tokpavi, 2021. "Machine Learning or Econometrics for Credit Scoring: Let's Get the Best of Both Worlds," Working Papers hal-02507499, HAL.
    • Handle: RePEc:hal:wpaper:hal-02507499
    Note: View the original document on HAL open archive server: https://hal.science/hal-02507499v3
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    Cited by:

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    2. Bastien Lextrait, 2021. "Scaling up SME's credit scoring scope with LightGBM," EconomiX Working Papers 2021-25, University of Paris Nanterre, EconomiX.
    3. Giuseppe Cascarino & Mirko Moscatelli & Fabio Parlapiano, 2022. "Explainable Artificial Intelligence: interpreting default forecasting models based on Machine Learning," Questioni di Economia e Finanza (Occasional Papers) 674, Bank of Italy, Economic Research and International Relations Area.

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    More about this item

    Keywords

    Credit scoring; Machine Learning; Risk management; Interpretability; Econometrics; Machine learning; Econo- metrics;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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