Behavior Revealed in Mobile Phone Usage Predicts Credit Repayment

Daniel Björkegren and Darrell Grissen

The World Bank Economic Review, vol. 34, issue 3, 618-634

Abstract: 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.

Keywords: credit scoring; machine learning; digital credit; mobile phones; financial inclusion (search for similar items in EconPapers)
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