Explainable Machine learning in Credit Risk Management

Abstract

The paper proposes an explainable AI model that can be used in credit risk management and, in particular, in measuring the risks that arise when credit is borrowed employing credit scoring platforms. The model applies similarity networks to Shapley values, so that AI predictions are grouped according to the similarity in the underlying explanatory variables.

The empirical analysis of 15,000 small and medium companies asking for credit reveals that both risky and not risky borrowers can be grouped according to a set of similar financial characteristics, which can be employed to explain and understand their credit score and, therefore, to predict their future behaviour.

Keywords: Credit risk management, Explainable AI , Financial Technologies , Similarity networks, Financial Networks, Machine Learning

JEL Classification: G00, G32, O33, C4, C69