The roles of alternative data and machine learning in fintech lending: Evidence from the LendingClub consumer platform
研究对比LendingClub与银行贷款，发现金融科技平台越来越多使用替代数据（非FICO分数），评级与FICO相关性从80%降至35%，且评级能有效预测违约，使部分次贷借款人获得更低利率。
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.