Fintechs are believed to help expand credit access to underserved consumers without taking on additional risk.We compare the performance efficiency of LendingClub’s unsecured personal loans with similar loans origina...Fintechs are believed to help expand credit access to underserved consumers without taking on additional risk.We compare the performance efficiency of LendingClub’s unsecured personal loans with similar loans originated by banks.Using stochastic frontier estimation,we decompose the observed nonperforming loan(NPL)ratio into three components:the best-practice minimum NPL ratio,the excess NPL ratio,and a statistical noise,the former two of which reflect the lender’s inherent credit risk and lending inefficiency,respectively.As of 2013 and 2016,we find that the higher NPL ratios at the largest banks are driven by inherent credit risk,rather than lending inefficiency.Smaller banks are less efficient.In addition,as of 2013,LendingClub’s observed NPL ratio and lending efficiency were in line with banks with similar lending volume.However,its lending efficiency improved significantly from 2013 to 2016.As of 2016,LendingClub’s performance resembled the largest banks–consistent with an argument that its increased use of alternative data and AI/ML may have improved its credit risk assessment capacity above and beyond its peers using traditional approaches.Furthermore,we also investigate capital market incentives for lenders to take credit risk.Market value regression using the NPL ratio suggests that market discipline provides incentives to make less risky consumer loans.However,the regression using two decomposed components(inherent credit risk and lending inefficiency)tells a deeper underlying story:market value is significantly positively related to inherent credit risk at most banks,whereas it is significantly negatively related to lending inefficiency at most banks.Market discipline appears to reward exposure to inherent credit risk and punish inefficient lending.展开更多
文摘Fintechs are believed to help expand credit access to underserved consumers without taking on additional risk.We compare the performance efficiency of LendingClub’s unsecured personal loans with similar loans originated by banks.Using stochastic frontier estimation,we decompose the observed nonperforming loan(NPL)ratio into three components:the best-practice minimum NPL ratio,the excess NPL ratio,and a statistical noise,the former two of which reflect the lender’s inherent credit risk and lending inefficiency,respectively.As of 2013 and 2016,we find that the higher NPL ratios at the largest banks are driven by inherent credit risk,rather than lending inefficiency.Smaller banks are less efficient.In addition,as of 2013,LendingClub’s observed NPL ratio and lending efficiency were in line with banks with similar lending volume.However,its lending efficiency improved significantly from 2013 to 2016.As of 2016,LendingClub’s performance resembled the largest banks–consistent with an argument that its increased use of alternative data and AI/ML may have improved its credit risk assessment capacity above and beyond its peers using traditional approaches.Furthermore,we also investigate capital market incentives for lenders to take credit risk.Market value regression using the NPL ratio suggests that market discipline provides incentives to make less risky consumer loans.However,the regression using two decomposed components(inherent credit risk and lending inefficiency)tells a deeper underlying story:market value is significantly positively related to inherent credit risk at most banks,whereas it is significantly negatively related to lending inefficiency at most banks.Market discipline appears to reward exposure to inherent credit risk and punish inefficient lending.
