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Behavior Revealed in Mobile Phone Usage Predicts Credit Repayment

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  • Daniel Björkegren
  • 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, 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.

Suggested Citation

  • Daniel Björkegren & Darrell Grissen, 0. "Behavior Revealed in Mobile Phone Usage Predicts Credit Repayment," The World Bank Economic Review, World Bank, vol. 34(3), pages 618-634.
    • Handle: RePEc:oup:wbecrv:v:34:y::i:3:p:618-634.
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    File URL: http://hdl.handle.net/10.1093/wber/lhz006
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    Citations

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    Cited by:

    1. Panle Jia Barwick & Yanyan Liu & Eleonora Patacchini & Qi Wu, 2019. "Information, Mobile Communication, and Referral Effects," NBER Working Papers 25873, National Bureau of Economic Research, Inc.
    2. 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.
    3. Evan Munro, 2020. "Treatment Allocation with Strategic Agents," Papers 2011.06528, arXiv.org, revised Apr 2023.
    4. Pauline Affeldt, 2019. "EU Merger Policy Predictability Using Random Forests," Discussion Papers of DIW Berlin 1800, DIW Berlin, German Institute for Economic Research.
    5. Croux, Christophe & Jagtiani, Julapa & Korivi, Tarunsai & Vulanovic, Milos, 2020. "Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform," Journal of Economic Behavior & Organization, Elsevier, vol. 173(C), pages 270-296.
    6. Adair Morse & Karen Pence, 2021. "Technological Innovation and Discrimination in Household Finance," Springer Books, in: Raghavendra Rau & Robert Wardrop & Luigi Zingales (ed.), The Palgrave Handbook of Technological Finance, pages 783-808, Springer.
    7. Susan Athey, 2018. "The Impact of Machine Learning on Economics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 507-547, National Bureau of Economic Research, Inc.
    8. Milusheva,Sveta, 2020. "Using Mobile Phone Data to Reduce Spread of Disease," Policy Research Working Paper Series 9198, The World Bank.
    9. De Mel,Suresh & Mckenzie,David J. & Woodruff,Christopher M., 2019. "Micro-Equity for Microenterprises," Policy Research Working Paper Series 8799, The World Bank.
    10. Marthe Uwamariya & Claudia Loebbecke & Stefan Cremer, 2019. "Mobile Banking Impacting the Performance of Microfinance Institutions: A Case Study from Rwanda," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 17(01), pages 1-18, December.
    11. Milusheva, Sveta, 2020. "Managing the spread of disease with mobile phone data," Journal of Development Economics, Elsevier, vol. 147(C).
    12. 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.
    13. 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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