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The mainstreams of research in P2P lending can be divided into credit risk, profit scoring, and portfolio optimization. The first two are pretty straightforward that credit risk is to derive the default probability of individual loans and profit scoring is to predict the expected return of individual loans. Based on the prediction, investors can identity potential low-risk and profitable loans to invest in. Some researchers also see the investment in P2P as a portfolio optimization problem. In this regard, researchers must know the return distribution of loans, i.e., the expected return AND variance of individual loans; thereby, constructing investment portfolio based on the predicted distribution.   To the best of my knowledge, there are two ways to derive the return distribution of loans in the literature, instance-based credit risk assessment framework and mean-variance estimation. The mean-variance estimation appears in Babaei et al.'s paper in 2020 [1], where they use mac...

Title:  Instance-based Credit Risk Assessment for Investment Decisions in P2P Lending Author(s):  Yanhong Guo, Wenjun Zhou, Chunyu Luo, Chuanren Liu, Hui Xiong Journal: European Journal of Operation Research Year: 2015 URL:  https://www.sciencedirect.com/science/article/abs/pii/S0377221715004610 Abstract Objective:  Effective allocation of personal investors' money across different loans by accurately assessing the credit risk of each loan. Key contributions:  Guo et al. proposed a data-driven investment decision-making framework for the P2P market. They designed an instance-based credit risk assessment model, which has the ability to evaluate the return and risk of each individual loan. Given the estimate of return and risk, they formulated the investment decision in P2P lending as a portfolio optimization problem with boundary constraints. Data Description 2016 loan samples from Lending Club 4128 loan samples from Prosper Features include the borrower's credi...