A Novel Reject Inference Model Using Outlier Detection and Gradient Boosting Technique in Peer-to-Peer Lending

Credit scoring is an efficient tool in handling the information asymmetry of peer-to-peer (P2P) lending. Credit scoring models are typically built only with the accepted applicants, which may cause sample bias and further hinder the predictive performances. Reject inference methods utilize the information contained in the rejected samples by inferring their potential status and incorporate them with the accepted samples. In this study, we propose a novel reject inference model (i.e., OD-LightGBM) that combines an outlier detection technique (i.e., isolation forest) and a state-of-the-art gradient boosting decision tree algorithm. The model is evaluated on two real-world P2P lending datasets, and the results of predictive performances demonstrate that our proposed model significantly outperforms the benchmarks in terms of discriminative capability. The analysis of computational cost shows the great potential of our proposed model in handling large-sized problems. The proposed framework remains robust under different parameter settings and provides stable results given various combinations of outlier detection algorithms and classifiers.