Machine Learning and Credit Risk Modelling

Machine Learning (ML) algorithms leverage large datasets to determine patterns and construct meaningful recommendations. Likewise, credit risk modelling is a field with access to a large amount of diverse data where ML can be deployed to add analytical value. In the following analysis, we explore how various ML techniques can be used for assessing probability of default (PD) and compare their performance in a real-world setting. A recent publication by the Bank of England (BoE) and the Financial Conduct Authority (FCA) reports the results of a survey on the use of ML in United Kingdom (UK) financial services.[1] Results show that two-thirds of respondents use ML in some form.