Credit Risk Modeling in the Age of Machine Learning

Abstract

Based on the world’s largest loss database of corporate defaults, we perform a comparative analysis of machine learning (ML) methods in credit risk modeling across the globe. We find substantial benefits of ML methods for different credit risk parameters, even though we use a uniform modeling framework for the ML methods, which potentially facilitates a massive reduction in operational resources required for model development and validation. We analyze the economic drivers of the credit risk models using explainable ML methods and find large variations in feature importance suggested by different ML methods. We propose to implement a nonlinear forecast ensemble, which not only boosts predictive performance but also produces more stable forecasts and economic sensitivities, thereby mitigating model uncertainty. Our results provide guidance for financial institutions, regulatory authorities, and academics.

Keywords: risk management, credit risk modeling, machine learning, forecasting

JEL Classification: G17, G21