The first is the best-practice minimum ratio that a lender could achieve if it were fully efficient at credit-risk evaluation and loan management. The second is a ratio that reflects the difference between the observed ratio (adjusted for noise) and the minimum ratio that gauges the lender’s relative proficiency at credit analysis and loan monitoring. The third is statistical noise. In 2013 and 2016, the largest bank lenders experienced the highest ratio of nonperformance, the highest inherent credit risk, and the highest lending efficiency, indicating that their high ratio of nonperformance is driven by inherent credit risk, rather than by lending inefficiency. LendingClub’s performance was similar to small bank lenders as of 2013. As of 2016, LendingClub’s performance resembled the largest bank lenders — the highest ratio of nonperforming loans, inherent credit risk, and lending efficiency — although its loan volume was smaller. Our findings are consistent with a previous study that suggests LendingClub became more effective in risk identification and pricing starting in 2015. Caveat: We note that this conclusion may not be applicable to fintech lenders in general, and the results may not hold under different economic conditions such as a downturn.
Consumer Lending Efficiency: Commercial Banks versus a Fintech Lender
WP 19-22 - We compare the performance of unsecured personal installment loans made by traditional bank lenders with that of LendingClub, using a stochastic frontier estimation technique to decompose the observed nonperforming loans into three components.