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Many households in developing countries lack access to credit: physically providing small loans to poor and remote populations is costly. However, the digitization of developing countries enables a new model: digital credit delivered directly via mobile phones. Mobile money enables inexpensive financial transfers, and mobile phones capture behavior that can predict repayment when mined with machine learning. This paper evaluates the potential for digital credit to reach those excluded from current financial systems.

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, this article's method actually outperforms models using credit bureau information, both within-time and when tested on a different time period. But the 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 the measure used in this article 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.