Improving Investment Suggestions for Peer-to-Peer (P2P) Lending via Integrating Credit Scoring into Profit Scoring
This addresses the need for better investment guidance for lenders in P2P lending markets, though it is incremental as it combines existing categories rather than introducing a new paradigm.
The paper tackles the problem of improving investment suggestions in peer-to-peer lending by integrating credit scoring into profit scoring, resulting in a two-stage framework that identifies more profitable loans compared to existing one-stage methods.
In the peer-to-peer (P2P) lending market, lenders lend the money to the borrowers through a virtual platform and earn the possible profit generated by the interest rate. From the perspective of lenders, they want to maximize the profit while minimizing the risk. Therefore, many studies have used machine learning algorithms to help the 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 profit (i.e., the profit 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 profit 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 profitable loans and thereby provide better investment guidance to the investors compared to the existing one-stage profit scoring alone approach. Therefore, the proposed framework serves as an innovative perspective for making investment decisions in P2P lending.