The impact of fintech lending on credit access for U.S. small businesses

Summary

Focus 

Financial technology (fintech) has allowed for new market entrants in many areas of financial services, including small business lending. New fintech lenders often use alternative data sources and machine learning to assess the credit quality of small firms, thus complementing the traditional credit scores and soft information used by traditional banks. In the United States, the growth of fintech lenders has been part of a broader trend in the growth of non-bank lenders amid a pullback by traditional banks. A key question is whether such lenders have enhanced credit access to small businesses which are likely to be underserved by traditional lenders. 

Contribution 

With a unique, proprietary data set from two fintech lenders (Funding Circle and LendingClub) over 2016–19, we are able to assess the geographical footprint of fintech loan origination and the effectiveness of their credit scoring methods in predicting default. By combining these two aspects, we can gain a deeper understanding of the potential impact of fintech credit on financial inclusion for small businesses. In particular, we can assess how fintech lending compares with credit by traditional intermediaries, and the added value of alternative data in credit risk evaluation and lending decisions. 

Findings 

We find that the fintech lending platforms lent more to small businesses in ZIP codes with higher unemployment rates and higher business bankruptcy filings. Their internal credit scores were able to predict future delinquencies (at a 12- and 24-month horizon) more accurately than traditional FICO scores or VantageScores, with a large uplift in the area under the receiver operating characteristics curve. Notably, we find that the improvement in predictive performance was highest in areas with higher unemployment. This indicates that the use of alternative data and machine learning is probably a key factor in fintech lenders' contribution to improving credit access for small businesses.  

Abstract

Small business lending (SBL) plays an important role in funding productive investment and fostering local economic growth. Recently, nonbank lenders have gained market share in the SBL market in the United States, especially relative to community banks. Among nonbanks, fintech lenders have become particularly active, leveraging alternative data for their own internal credit scoring. We use proprietary loan-level data from two fintech SBL platforms (Funding Circle and LendingClub) to explore the characteristics of loans originated pre-pandemic (2016-2019). Our results show that fintech SBL platforms lent more in zip codes with higher unemployment rates and higher business bankruptcy filings. Moreover, fintech platforms' internal credit scores were able to predict future loan performance more accurately than the traditional approach to credit scoring, particularly in areas with high unemployment. Using Y-14M loan-level bank data, we also compare fintech SBL with traditional bank business cards in terms of credit access and interest rates. Overall, fintech lenders have a potential to create a more inclusive financial system, allowing small businesses that were less likely to receive credit through traditional lenders to access credit and to do so at lower cost. 

JEL classification: G18, G21, G28, L21.

Keywords: fintech credit, peer-to-peer (P2P) lending, marketplace lending, small business lending (SBL), Funding Circle, LendingClub, alternative data, credit access, credit scoring.