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 . We evaluate our approach using call records matched to lending outcomesin a middle income South American country . Individuals in the highest quartile of risk by our measure are 7.4 times more likely to default than those in the lowest quartile. The method is predictive for both individuals with financial histories, and those without, who cannot be scored using traditional methods. We benchmark performance on our sample of individuals with (thin) financial histories : our method performs no worse than models using credit bureau information  . The method can form the basis for new forms of lending that reach the unbanked.
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.
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