Predicting loss given default using post-default information

Abstract

The loss given default (LGD) is an essential component for estimating credit risk according to the international regulatory Basel Accord. Traditionally, LGD models are built based on the characteristics of the loan and the borrower prior to default, a practice which fails to consider the post-default information revealed during the repayment process. We start by uncovering a predictive post-default variable (i.e., a flag that indicates whether the defaulted borrower had cooperated with a debt-settlement company) in the defaulted data from the online lending platform. We then propose a stratified modelling framework to incorporate this variable into LGD prediction. The experimental results demonstrate that LGD prediction is significantly improved by the inclusion of post-default information under the proposed stratified modelling framework. We further show that the predictive performance of the proposed models is robust for the choice of training set and input variables. Our results imply the importance of using post-default information in LGD prediction.