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Behavior Revealed in Mobile Phone Usage Predicts Loan Repayment

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  • Daniel Bjorkegren
  • Darrell Grissen

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, 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.

Suggested Citation

  • Daniel Bjorkegren & Darrell Grissen, 2017. "Behavior Revealed in Mobile Phone Usage Predicts Loan Repayment," Papers 1712.05840, arXiv.org, revised Dec 2019.
  • Handle: RePEc:arx:papers:1712.05840
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    References listed on IDEAS

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    1. Marta C. González & César A. Hidalgo & Albert-László Barabási, 2009. "Understanding individual human mobility patterns," Nature, Nature, vol. 458(7235), pages 238-238, March.
    2. Berger, Allen N. & Udell, Gregory F., 2006. "A more complete conceptual framework for SME finance," Journal of Banking & Finance, Elsevier, vol. 30(11), pages 2945-2966, November.
    3. Declan Butler, 2013. "When Google got flu wrong," Nature, Nature, vol. 494(7436), pages 155-156, February.
    4. Daniel Björkegren & Darrell Grissen, 2018. "The Potential of Digital Credit to Bank the Poor," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 68-71, May.
    5. Irani Arráiz & Miriam Bruhn & Rodolfo Stucchi, 2017. "Psychometrics as a Tool to Improve Credit Information," The World Bank Economic Review, World Bank, vol. 30(Supplemen), pages 67-76.
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    Cited by:

    1. De Mel,Suresh & Mckenzie,David J. & Woodruff,Christopher M., 2019. "Micro-Equity for Microenterprises," Policy Research Working Paper Series 8799, The World Bank.
    2. Milusheva, Sveta, 2020. "Managing the spread of disease with mobile phone data," Journal of Development Economics, Elsevier, vol. 147(C).
    3. Asif M. Islam & Silvia Muzi, 2022. "Does mobile money enable women-owned businesses to invest? Firm-level evidence from Sub-Saharan Africa," Small Business Economics, Springer, vol. 59(3), pages 1245-1271, October.
    4. Alberto Tron & Maurizio Dallocchio & Salvatore Ferri & Federico Colantoni, 2023. "Corporate governance and financial distress: lessons learned from an unconventional approach," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 27(2), pages 425-456, June.
    5. Elinor Benami & Michael R. Carter, 2021. "Can digital technologies reshape rural microfinance? Implications for savings, credit, & insurance," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 43(4), pages 1196-1220, December.
    6. Daniel Bjorkegren & Burak Ceyhun Karaca, 2020. "The Effect of Network Adoption Subsidies: Evidence from Digital Traces in Rwanda," Papers 2002.05791, arXiv.org.

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