Predicting Credit Risk in Peer-to-Peer Lending: A Machine Learning Approach with Few Features

Abstract

Peer-to-peer (P2P) lending provides borrowers with relatively low borrowing interest rates and gives lenders a channel for investment on an online platform. Since most P2P lending does not require any guarantees, the overdue payment of borrowers results in a massive loss of lending platforms and lenders. Many risk prediction models are proposed to predict credit risk. However, these works build models with more than 50 features, which causes a lot of computation time. Besides, in most P2P lending datasets, the number of non-default data far exceeds the number of default data. These researches ignore the data imbalance issue, leading to inaccurate predictions. Therefore, this study proposes a credit risk prediction system (CRPS) for P2P lending to solve data imbalance issues and only require few features to build the models. We implement a data preprocessing module, a feature selection module, a data synthesis module, and five risk prediction models in CRPS. In experiments, we evaluate CRPS based on the de-identified personal loan dataset of the LendingClub platform. The accuracy of the CRPS can achieve 99%, the recall reaches 0.95, and the F1-Score is 0.97. CRPS can accurately predict credit risk with less than 10 features and tackle data imbalance issues.