I reviewed a large number of papers which focus on the borrower’s side in P2P lending. Few papers discuss lender’s choice and his/her welfare. A key challenge for personal investors in P2P lending marketplaces is the effective allocation of their money across different loans by accurately assessing the credit risk of each loan. Thus, how lenders screen borrowers’ profile and make portfolios to maximise their utility?
Yanhong Guo et al. employed data from Lending Club and Prosper and proposed a method for investment decisions in P2P lending. They designed an instance-based credit risk assessment model, which has the ability of evaluating the return and risk of each individual loan. Moreover, they formulated the investment decision in P2P lending as a portfolio optimisation problem with boundary constraints. To validate the proposed model, they performed extensive experiments on real-world datasets from two notable P2P lending marketplaces. The results revealed that the proposed model can effectively improve investment performances compared with existing methods in P2P lending.
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