A survey of machine learning in credit risk

Joseph L. Breeden

Abstract

Machine learning algorithms have come to dominate several industries. After decades of resistance from examiners and auditors, machine learning is now moving from the research desk to the application stack for credit scoring and a range of other applications in credit risk. This migration is not without novel risks and challenges. Much of the research is now shifting from how best to make the models to how best to use the models in a regulator-compliant business context. This paper surveys the impressively broad range of machine learning methods and application areas for credit risk. In the process of that survey, we create a taxonomy to think about how different machine learning components are matched to create specific algorithms. The reasons for where machine learning succeeds over simple linear methods are explored through a specific lending example. Throughout, we highlight open questions, ideas for improvements and a framework for thinking about how to choose the best machine learning method for a specific problem.