Behavior Revealed in Mobile Phone Usage Predicts Loan Repayment
28 Pages Posted: 11 Jun 2015 Last revised: 18 Feb 2018
Date Written: February 16, 2018
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.
Keywords: credit scoring, microfinance, mobile phones, big data
JEL Classification: O16
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