Dynamic prediction of loss given default in Norwegian retail credit card debt: advanced machine learning versus statistical models
TL;DR
This study evaluates the performance of statistical and machine learning models for predicting and updating final Loss Given Default estimates during recovery, using proprietary data from Norwegian retail credit card portfolios, to provide new empirical evidence on dynamic LGD modeling in unsecured retail credit markets.
Abstract
The digitalization of financial services has generated large volumes of borrower data, enabling the use of advanced analytics in credit risk modeling. This study evaluates the performance of statistical and machine learning models for predicting and updating final Loss Given Default (LGD) estimates during recovery, using proprietary data from Norwegian retail credit card portfolios. Traditional regression models (Ordinary Least Squares, Ridge, and Lasso) are compared with ensemble-based machine learning techniques, including Random Forest and Extreme Gradient Boosting (XGBoost). The results show that machine learning models generally achieve lower prediction error, with XGBoost delivering the strongest predictive performance. However, linear models retain greater interpretability and higher explained variance. The findings highlight trade-offs between predictive accuracy and transparency and provide new empirical evidence on dynamic LGD modeling in unsecured retail credit markets.