Fintech Expansion

I study credit market outcomes with different competing lending technologies: A fintech lender who learns from data and is able to seize on-platform sales, and a banking sector that relies on physical collateral. I analyze how the distribution of the borrowers' initial wealth and their latent productivities affect the fintech's information acquisition, market specialization, equilibrium interest rates and lender profits. Despite flexible pricing strategies and a rich information acquisition technology, the fintech only sets a screening standard so that borrowers below a certain threshold are rejected. The model predicts that the fintech will enter markets with poor borrowers who are rejected by banks and with rich borrowers whom the bank squeezes the most, before entering markets between these two borrower populations. The model sheds light on how the fintech expansion depends on the big data technology which identifies latent characteristics across observationally heterogeneous groups