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Who cares about your Facebook friends? Credit scoring for microfinance

Author

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  • DE CNUDDE, Sofie
  • MOEYERSOMS, Julie
  • STANKOVA, Marija
  • TOBBACK, Ellen
  • JAVALY, Vinayak
  • MARTENS, David

Abstract

Microfinance has known a large increase in popularity, yet the scoring of such credit still remains a difficult challenge. In general, retail credit scoring uses socio-demographic and credit data. We complement such data with social network data in an innovative manner i.e. with fine-grained interest and social network data from Facebook. Using a unique dataset of 4,985 microfinance loans from the Philippines, we show how the different data types can predict creditworthiness. A distinction is made between the relationships that the available data imply: (1) look-a-likes are persons who resemble one another in some manner, be it liking the same pages, having the same education, etc. (2) friends have a clearly articulated friendship relationship on Facebook, and finally (3) the \Best Friends Forever" (BFFs) are friends that interact with one another. Our analyses show two interesting conclusions for this emerging application. Firstly, applying relational learners on BFF data yields better results than considering only the friends data. Secondly, the interest-based data that defines look-a-likes, is more predictive than the friendship or BFF data. Moreover, the model built on interest data is not significantly worse than the model that uses all available data, including the friendship data. Hence begging the question: who cares about your Facebook friends when your interest data is available?

Suggested Citation

  • DE CNUDDE, Sofie & MOEYERSOMS, Julie & STANKOVA, Marija & TOBBACK, Ellen & JAVALY, Vinayak & MARTENS, David, 2015. "Who cares about your Facebook friends? Credit scoring for microfinance," Working Papers 2015018, University of Antwerp, Faculty of Business and Economics.
    • Handle: RePEc:ant:wpaper:2015018
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    References listed on IDEAS

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    1. Niels Hermes & Robert Lensink, 2007. "The empirics of microfinance: what do we know?," Economic Journal, Royal Economic Society, vol. 117(517), pages 1-10, February.
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    5. Joris Van Gool & Wouter Verbeke & Piet Sercu & Bart Baesens, 2012. "Credit scoring for microfinance: is it worth it?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 17(2), pages 103-123, April.
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    Cited by:

    1. Ulf Römer & Oliver Musshoff, 2017. "Can agricultural credit scoring for microfinance institutions be implemented and improved by weather data?," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 78(1), pages 83-97, December.
    2. Li, Yibei & Wang, Ximei & Djehiche, Boualem & Hu, Xiaoming, 2020. "Credit scoring by incorporating dynamic networked information," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1103-1112.
    3. DE CNUDDE, Sofie & MARTENS, David & EVGENIOU, Theodoros & PROVOST, Foster, 2017. "A benchmarking study of classification techniques for behavioral data," Working Papers 2017005, University of Antwerp, Faculty of Business and Economics.

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    Keywords

    Networks; Data mining; Default prediction; Microcredit;
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