The use of Machine Learning (ML) in Risk Management and, in particular, in IRB systems has sparked a heated international debate, as evidenced by the various publications on the subject from both the world of academia and the banking market. In terms of banking, the European Banking Authority published a consultation paper on the possible use of ML for internal ratings-based (IRB) models in November 2021.
At the heart of the discussions are some fundamental issues concerning the adoption of machine learning methodologies:
With the aim of providing greater clarity and to respond to the challenges posed by the adoption of Machine Learning in Risk Management, Banca Intesa Sanpaolo and CRIF have shared their experience in a position paper entitled “Machine Learning for Credit Risk management and IRB models: lessons from successful case histories”.
After a brief introduction to machine learning and the potential relationship with the banking market, the paper proposes a careful analysis of the reference regulatory context to respond to the “practical” challenges of adopting such methodologies through five case histories.
The work carried out by CRIF-Intesa Sanpaolo was presented to the European Banking Authority (EBA) at the end of March 2022.