The roles of alternative data and machine learning in fintech lending: Evidence from the LendingClub consumer platform

TLDR

Fintech lenders have raised concerns about the use of alternative data sources. The study compares LendingClub loans with comparable bank‑originated loans. The analysis shows that correlations between LendingClub rating grades and FICO scores have dropped from about 80 % to 35 % over time, yet the grades still predict default accurately and the use of alternative data lets subprime borrowers secure better loan grades and lower rates.

Abstract

Abstract There have been concerns about the use of alternative data sources by fintech lenders. We compare loans made by LendingClub and similar loans that were originated by banks. The correlations between the rating grades (assigned by LendingClub) and the borrowers’ FICO scores declined from about 80% (for loans originated in 2007) to about 35% for recent vintages (originated in 2014–2015), indicating that nontraditional data (not already accounted for in the FICO scores) have been increasingly used by fintech lenders. The rating grades perform well in predicting loan default. 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, allowing them to obtain lower priced credit.