Risk Forecast Model for Online P2P Lending Platform
Student thesis: Doctoral Thesis
Related Research Unit(s)
In recent years, online P2P lending grows remarkably, which highlights the significance of risk evaluation and risk forecast of borrowers. Although past studies on risk evaluation in online P2P lending platform were from many aspects of hard information (i.e. loan information, borrower characteristics etc.) and soft information (i.e. social relationship, social network capital etc.) with econometric method, few research has paid attention to mining influential factors of risk regarding to student lending platform, and study considering delinquency behavior is rare. Focusing on student P2P lending platform and using actual second-hand data, this study investigated the influential factors of risk on borrowers’ delinquency and default behaviors on the level of loan and level of monthly payment respectively. We proposed the risk forecast model with decision tree analysis, and adopted another real dataset to test the forecast model. Meanwhile, we used gradient boost decision tree (GBDT) and econometric model to further validate the forecast model. Finally, based on the proposed forecast model, we conducted capital gain and loss analysis for online P2P lending platform. We found that many factors that are different from past studies were important on student borrower risk prediction. For example, the distance between school (university or college) and hometown, the economic gap between school (university or college) and hometownt, the relationship between student borrowers and their parents, housing condition, education level, gender will have direct impact on student risk (delinquency or default behaviors). The proposed forecast model will effectively improve the risk evaluation of online P2P lending platform, and bring large positive capital revenue. This study has several theoretical contributions on research object (i.e. student online P2P lending) and student-related risk factors, two-stage risk forecast on borrowers' delinquency and default behaviors, as well as adoption of data mining methodology. Moreover, the study has strong implications for online P2P lending platform on borrower risk prediction and credit assessment, P2P lending cost-benefit analysis and platform operation.