Project CEMAPRE internal
| Title | Interpretable models of loss given default |
| Participants | João Bastos (Principal Investigator), Sara M Matos |
| Summary | Machine learning based predictive techniques are increasingly being adopted in Finance, and particularly in the area of Credit Risk. However, due to their complexity and high number of degrees of freedom, their predictions are often difficult to explain and validate. This is referred to as the “black box” problem. In the area of Credit Risk, it is important that the model decisions can be inspected and interpreted, not only to guarantee that the deployed models are compliant with the prudential requirements defined by the regulators, but also to detect any potential bias and discrimination. In this project we address the problem of the interpretability of machine learning models to predict the loss given default: the proportion of outstanding credit that is lost by a lender when the debtor defaults. This is a crucial parameter to calculate the amount of regulatory capital a financial institution is required to hold to ensure that it does not take on excess leverage and become insolvent. |