Behavior Revealed in Mobile Phone Usage Predicts Credit Repayment
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.
| Main Authors: | , |
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| Format: | Working Paper biblioteca |
| Language: | English |
| Published: |
World Bank, Washington, DC
2019-12
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| Subjects: | CREDIT SCORING, MACHINE LEARNING, DIGITAL CREDIT, MOBILE PHONE, FINANCIAL INCLUSION, |
| Online Access: | http://documents.worldbank.org/curated/en/811881575657172759/Behavior-Revealed-in-Mobile-Phone-Usage-Predicts-Credit-Repayment https://hdl.handle.net/10986/33018 |
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