A recent working paper by Jagtiani and Lemieux explored the alternative data and machine learning roles in P2P lending. There have been concerns about the use of alternative data sources by P2P lenders and the impact on financial inclusion. The authors compare loans made by LendingClub and similar loans that were originated through traditional banking channels (Y-14M data reported by bank holding companies with total assets of $50 billion or more). The main finding is that a high correlation with interest rate spreads, LendingClub rating grades, and loan performance. Interestingly, the correlations between the rating grades and FICO scores have declined from about 80 percent (for loans that were originated in 2007) to only about 35 percent for recent vintages (originated in 2014–2015), indicating that nontraditional alternative data have been increasingly used by P2P lenders. The further analysis indicates that the rating grades (assigned based on alternative data) perform well in predicting loan performance over the two years after origination. The use of alternative data has allowed some borrowers who would have been classified as subprime by traditional criteria to be slotted into “better” loan grades, which allowed them to get lower-priced credit. In addition, for the same risk of default, consumers pay smaller spreads on loans from LendingClub than from credit card borrowing.
Full paper see Here