Financial Inclusion And Alternate Credit Scoring

We use unique and proprietary data from a large Fintech lender to analyze whether alternative data captured from an individual's mobile phone (mobile/social footprint) can substitute for traditional credit bureau scores. Variables that measure a borrowers' digital presence, such as the number and types of apps installed, crude measures of social connections, and measures of borrowers' “deep social footprints” based on call logs, significantly improve default prediction and outperform the credit bureau score. Using machine learning-based prediction counterfactual analysis, we show that alternate credit scoring based on the mobile and social footprints can expand credit access for individuals who lack credit scores without adversely impacting the default outcomes. Our analysis suggests that the marginal benefit of using alternative data for credit decisions are likely to be higher for borrowers with low levels of income and education, as well as borrowers residing in regions with low levels of financial inclusion.