Machine Learning for credit
By Svetlana Borovkova, Head of Quantitative Modelling
On November 11th 2021, The European Banking Authority published a discussion paper on Machine Learning for an Internal Rating-Based approach. This paper is the first serious step towards the acceptance of using machine learning models for credit applications.
In recent years, there has been much interest from financial institutions in exploring machine learning models for credit-related topics, ranging from assessing the creditworthiness of new credit applicants to monitoring existing loans and flagging those that are potentially ‘troublesome’.
For asset managers, these new developments can also be interesting: in search for yield, more and more asset managers invest in credit portfolios (or mortgages, SME, or other loans) and for them, machine learning models can be beneficial for estimating losses of such portfolios.
However, scepticism about such models has been quite significant. Applying machine learning in the area of credit has many potential pitfalls, which we will summarize here.