Machine Learning for Credit Risk Management and IRB Models: Lessons from successful Case Histories

The paper is structured as follows: in §2 we discuss the main opportunities and risks associated with machine learning models, providing a quick (and necessarily incomplete) classification, a picture of the main benefits they can provide to banks and a list of the possible pitfalls that need to be addressed; we then describe their current usage by financial institutions and review the main regulatory constraints to their development and usage as part of IRB rating systems. In §3 we present five case histories showing how ML has been used in banks, each time discussing the expected benefits and challenges, as well as data, algorithms, and interpretability techniques. In §4 we set out our conclusions.