Title: Behavior Revealed in Mobile Phone Usage Predicts Loan Repayment,
Abstract: Many households in developing countries lack formal financial histories,
making it difficult for banks to extend loans, and for potential borrowers to
receive them. However, many of these households have mobile phones, which
generate rich data about behavior. This paper shows that behavioral signatures
in mobile phone data predict loan default, using call records matched to loan
outcomes. In a middle income South American country, individuals in the highest
quintile of risk by our measure are 2.8 times more likely to default than those
in the lowest quintile. On our sample of individuals with (thin) financial
histories, our method outperforms models using credit bureau information, both
within time and when tested on a different time period. The method forms the
basis for new forms of lending that reach the unbanked.