E2023008                               2023-03-06
 

Yanting Chen

Yingwei Dong

Jiayin Hu

Abstract ：
We investigate the impact of Big Tech lending on non-bank traditional lenders, which have a more overlapping clientele with Big Techs than traditional banks do. Our empirical methodology exploits geographical differences in Big Tech penetration ratios and adopts the instrumental variable (IV) approach using FinTech payment adoption ratios and the distance to the Big Tech’s headquarter. We find that the competition from Big Tech worsens the performance of branches facing stronger Big Tech competition by reducing the number of borrowers and the amount of loans. Moreover, branches in cities highly penetrated by Big Tech lending tighten the lending standard by reducing the loan-to-value (LTV) ratios, measured as the approved loan amount per unit collateral value, while keeping the average collateral requirement unchanged. Our findings are consistent with the cream-skimming hypothesis that Big Techs possess better screening technology and reduce the quality of borrowers applying for traditional loans. Our results document novel changes in and responses of the non-bank traditional lending business in the Big Tech era.

Keywords: Big Tech, FinTech lending, traditional loans, non-bank financial institutions (NBFI), car equity loans

JEL classification: G21, G28, G51

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