Machine Learning and Credit Risk: Empirical Evidence from SMEs

In this paper we assess credit risk of SMEs by testing and comparing a classic parametric approach fitting an ordered probit model with a non-parametric one calibrating a machine learning historical random forest (HRF) model. We do so by exploiting a unique and proprietary dataset comprising granular firm-level quarterly data collected from a large European bank and an international insurance company on a sample of 810 Italian small- and medium-sized enterprises (SMEs) over the time period 2015-2017. Our results provide novel evidence that a dynamic Historical Random Forest (HRF) approach outperforms the traditional ordered probit model, highlighting how advanced estimation methodologies that use machine learning techniques can be successfully implemented to predict SME credit risk. Moreover, by using Shapley values for the first time, we are able to assess the relevance of each variable in predicting SME credit risk. Traditionally, credit risk evaluation of informationally-opaque SMEs has relied on soft information-intensive relationship banking. However, the advent of large banking conglomerates and the limits to successfully "harden" and transmit soft information across large banking organizations, challenge the traditional role of relationship banking, urging the need to evaluate SME credit risk by implementing alternative methodologies mostly based on hard information.