A key policy question raised by the advent of fintech lenders revolves around the impact on credit availability of credit models that employ alternative data and algorithmic underwriting. We exploit anonymized administrative data provided by a major fintech platform (Upstart Inc.) to investigate whether using alternative data to assess borrowers' creditworthiness results in broader credit access. Comparing actual outcomes of the fintech platform’s model to counterfactual outcomes based on a “traditional model” used for regulatory reporting purposes, we find that about 25% of the applications with low credit scores would have been denied if not for Upstart's underwriting model. We further find that the mismatch between the traditional and Upstart model is magnified among low-credit score borrowers. This finding highlights the potentially uneven consequences of using alternative data: those who benefit are those who are most in need.
