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What does your Facebook profile reveal about your creditworthiness? Using alternative data for microfinance

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  • Sofie De Cnudde
  • Julie Moeyersoms
  • Marija Stankova
  • Ellen Tobback
  • Vinayak Javaly
  • David Martens

Abstract

Microfinance has known a large increase in popularity, yet the scoring of such credit still remains a difficult challenge. Credit scoring traditionally uses socio-demographic and credit data, which we complement in an innovative manner with data from Facebook. A distinction is made between the relationships that the available data imply: (1) LALs are persons who resemble one another in some manner, (2) friends have a clearly articulated friendship relationship on Facebook, and (3) BFFs are friends that interact with one another. Our analyses show two interesting conclusions for this emerging application: the BFFs have a higher predictive value then the person’s friends and secondly, the interest-based data that define LALs, yield better results than the social network data. Moreover, the model built on interest data is not significantly worse than the model that uses all available data, hence demonstrating the potential of Facebook data in a microfinance setting.

Suggested Citation

  • Sofie De Cnudde & Julie Moeyersoms & Marija Stankova & Ellen Tobback & Vinayak Javaly & David Martens, 2019. "What does your Facebook profile reveal about your creditworthiness? Using alternative data for microfinance," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(3), pages 353-363, March.
    • Handle: RePEc:taf:tjorxx:v:70:y:2019:i:3:p:353-363
    DOI: 10.1080/01605682.2018.1434402
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    Cited by:

    1. Sahab Zandi & Kamesh Korangi & Mar'ia 'Oskarsd'ottir & Christophe Mues & Cristi'an Bravo, 2024. "Attention-based Dynamic Multilayer Graph Neural Networks for Loan Default Prediction," Papers 2402.00299, arXiv.org.
    2. Silva, Diego M.B. & Pereira, Gustavo H.A. & Magalhães, Tiago M., 2022. "A class of categorization methods for credit scoring models," European Journal of Operational Research, Elsevier, vol. 296(1), pages 323-331.

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