Behavior Revealed in Mobile Phone Usage Predicts Loan Repayment

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. This draft: February 16, 2018. First draft: January 6, 2015. ACKNOWLEDGEMENTS Thanks to Entrepreneurial Finance Lab and partners for data, and Jeff Berens, Nathan Eagle, Javier Frassetto, and Seema Jayachandran for helpful discussions. We appreciate the feedback of audiences at the AEA Annual Meetings, NEUDC, Microsoft Research, and the AMID/BREAD Summer School in Development Economics. Daniel gratefully acknowledges the support of the W. Glenn Campbell and Rita Ricardo-Campbell National Fellowship at Stanford University. 1 Brown University, Department of Economics. Box B, Providence, RI 02912. E-mail: [email protected], Web: http://dan.bjorkegren.com, Phone: 650.720.5615. (corresponding author) 2 Independent. E-mail: [email protected].