Small and Medium Size Enterprises (SMEs) are critical actors in the fabric of the economy. Their growth is often limited by the difficulty in obtaining financing. Basel II accords enforced the obligation for banks to estimate the probability of default of their obligors. Currently used models are limited by the simplicity of their architecture and the available data. State of the art machine learning models are not widely used because they are often considered as black boxes that cannot be easily explained or interpreted. We propose a methodology to combine high predictive power and powerful explainability using various Gradient Boosting Decision Trees (GBDT) implementations such as the LightGBM algorithm and SHapley Additive exPlanation (SHAP) values as post-prediction explanation model. SHAP values are among the most recent methods quantifying with consistency the impact of each input feature over the credit score. This model is developed and tested using a nation-wide sample of French companies, with a highly unbalanced positive event ratio. The performances of GBDT models are compared with traditional credit scoring algorithms such as Support Vector Machine (SVM) and Logistic Regression. LightGBM provides the best performances over the test sample, while being fast to train and economically sound. Results obtained from SHAP values analysis are consistent with previous socio-economic studies, in that they can pinpoint known influent economical factors among hundreds of other features. Providing such a level of explainability to complex models may convince regulators to accept their use in automated credit scoring, which could ultimately benefit both borrowers and lenders.
UNIVERSITÉ PARIS NANTERRE
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