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Archive » February 2025 » Explainable Machine Learning for the estimation of default probabilities of Italian Smes

Explainable Machine Learning for the estimation of default probabilities of Italian Smes

Federico Beltrame, Alex Sclip
February 2025 - n. 2
Keywords: Machine Learning, Credit Risk, interpretability
Jel codes: G21, G28

Sophisticated Machine Learning (Ml) models have seen to increase predictive accuracy of linear regression models in the context of credit risk modelling. Nevertheless, linear regression models remain popular in the credit risk industry, because of the lack of transparency of Ml models. In this study, we propose a way to interpret, tuning and extract default probabilities from Ml technologies in the context of credit risk. Using a sample of Italian Small and Medium sized Enterprises' (Smes), we show how much and why Ml models increase predictions and precision of default probabilities.

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