In the shadow of big tech lending

We investigate the impact of Big Tech lending on non-bank traditional lenders, which have a more overlapping clientele with Big Tech lenders than traditional banks. Our empirical meth-odology exploits geographical differences in Big Tech penetration ratios and adopts the instru-mental variable (IV) approach using the FinTech payment adoption ratio 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 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.

Automated summary

We investigate the impact of Big Tech lending on non-bank traditional lenders, finding that competition from Big Tech worsens the performance of branches facing stronger competition, reducing the number of borrowers and loan amounts. Moreover, branches in highly penetrated cities tighten lending standards by reducing loan-to-value ratios, indicating cream-skimming behavior by Big Techs. These findings highlight the changes and responses of non-bank traditional lending in the Big Tech era.