Forecasting loss given default for peer-to-peer loans via heterogeneous stacking ensemble approach

Abstract

Peer-to-peer (P2P) lending is an emerging field in FinTech and is an alternative source of personal loans. However, P2P lending faces severe credit risk due to high information asymmetry and insufficient collateral. We develop a novel heterogeneous stacking ensemble (HSE) approach by using two real-world datasets to improve the loss given default (LGD) forecasting in the P2P lending domain. Some special data in P2P lending and macroeconomic variables are employed as supplementary data sources to further enhance the model performance. Our proposal is compared with several popular models, including parametric and non-parametric ones, in terms of predictive accuracy and capital requirement. Our finding reveals that special data in P2P lending (e.g., number of investors and loan description) and macroeconomic variables are powerful predictors of LGD in P2P lending. The proposed HSE model outperforms the benchmark models in most cases and significantly achieves optimal average ranks across all the evaluation metrics. The results remain robust under several validations.