  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.
