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.
Many households in developing countries lack formal financial histories, making it difficult for firms to extend credit , and for potential borrowers to receive it . However, many of these households have mobile phones, which generate rich data about behavior. This article shows that behavioral signatures in mobile phone data predict default, using call records matched to repayment outcomes for credit extended by a South American telecom. On a sample of individuals with (thin) financial histories, our method actually outperforms models using credit bureau information, both within time and when tested on a different time period. But our method also attains similar performance on those without financial histories, who cannot be scored using traditional methods. Individuals in the highest quintile of risk by our measure are 2.8 times more likely to default than those in the lowest quintile. The method forms the basis for new forms of credit that reach the unbanked.
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banks to extend loans
firms to extend credit
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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
repayment outcomes for credit extended by a South American telecom. On a
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actually
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But our method also attains similar performance on those without financial histories, who cannot be scored using traditional methods. Individuals in the highest quintile of risk by our measure are 2.8 times more likely to default than those in the lowest quintile.
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lending
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