COSLE: Cost Sensitive Loan Evaluation for P2P Lending

Abstract The loan evaluation is a fundamental task in peer-to-peer (P2P) lending. Effective loan evaluation can help lenders make informed investment decisions. Existing methods do not consider the return of loans in the core learning stage and thus fail to explore the relationship between the return of loans and their final loan payoff outcomes. In this study, we propose a systematic loan evaluation framework called COst Sensitive Loan Evaluation (COSLE). Specifically, we first develop an instance-aware misclassification cost (IMCO) matrix, which specifies personalized cost for each loan. Then, we present a differential labelling algorithm called DILA cost for assigning node labels and assessing the corresponding cost. By integrating these enhancements into the tree-induction process, we construct a node splitting measurement called COG index. It exploits the relationship between the return information and the final payoff outcome. Additionally, we design the LER evaluation metric to measure the ability of a loan evaluation model to increase the lender’s return. Finally, the COSLE is used to improve popular tree models. Extensive experiments based on the Lending Club dataset show that our COSLE can effectively increase the lender’s return.