Explainable fintech lending

Abstract

Lending to Small and Medium Enterprises (SME) is facilitated by the availability of advanced Machine Learning (ML) methods, embedded in financial technologies, which can accurately predict financial performance from the many data sources available. However, despite their high predictive accuracy, ML models may not provide sufficient explanations to investors and, therefore, may not be adequate for informed decision-making.

We propose a financial machine learning model that is both accurate and explainable. To reach this aim, we propose to enhance random forest models with a model selection procedure that progressively removes the least explainable variable, according to the Shapley value method.

We apply our proposal to 2,049 SMEs for which yearly financial performance indicators are available. Our results show that both the default and the expected return of SMEs can be well predicted and explained by a small set of indicators deduced from their balance sheets.

Keywords: Credit Risk Management, Explainable AI, Financial Technologies, Random Forest models.

JEL Classification: C18,G23