Improving Investment Suggestions for Peer-to-Peer (P2P) Lending via Integrating Credit Scoring into Profit Scoring
Statistics and Analytical Sciences
In the peer-to-peer (P2P) lending market, lenders lend the money to the borrowers through a virtual platform and earn the possible pro t generated by the interest rate. From the perspective of lenders, they want to maximize the pro t while minimizing the risk. Therefore, many studies have used machine learning algorithms to help lenders identify the “best" loans for making investments. The studies have mainly focused on two categories to guide the lenders’ investments: one aims at minimizing the risk of investment (i.e., the credit scoring perspective) while the other aims at maximizing the pro t (i.e., the pro t scoring perspective). However, they have all focused on one category only and there is seldom research trying to integrate the two categories together. Motivated by this, we propose a two-stage framework that incorporates the credit information into a pro t scoring modeling. We conducted the empirical experiment on a real-world P2P lending data from the US P2P market and used the Light Gradient Boosting Machine (lightGBM) algorithm in the two-stage framework. Results show that the proposed two-stage method could identify more pro table loans and thereby provide better investment guidance to the investors compared to the existing one-stage pro t scoring alone approach. Therefore, the proposed framework serves as an innovative perspective for making investment decisions in P2P lending.