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Private Information, Credit Risk and Graph Structure in P2P Lending Networks

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  • J. Christopher Westland
  • Tuan Q. Phan
  • Tianhui Tan

Abstract

This research investigated the potential for improving Peer-to-Peer (P2P) credit scoring by using "private information" about communications and travels of borrowers. We found that P2P borrowers' ego networks exhibit scale-free behavior driven by underlying preferential attachment mechanisms that connect borrowers in a fashion that can be used to predict loan profitability. The projection of these private networks onto networks of mobile phone communication and geographical locations from mobile phone GPS potentially give loan providers access to private information through graph and location metrics which we used to predict loan profitability. Graph topology was found to be an important predictor of loan profitability, explaining over 5.5% of variability. Networks of borrower location information explain an additional 19% of the profitability. Machine learning algorithms were applied to the data set previously analyzed to develop the predictive model and resulted in a 4% reduction in mean squared error.

Suggested Citation

  • J. Christopher Westland & Tuan Q. Phan & Tianhui Tan, 2018. "Private Information, Credit Risk and Graph Structure in P2P Lending Networks," Papers 1802.10000, arXiv.org.
    • Handle: RePEc:arx:papers:1802.10000
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    1. repec:cup:cbooks:9780511771576 is not listed on IDEAS
    2. Steven H. Strogatz, 2001. "Exploring complex networks," Nature, Nature, vol. 410(6825), pages 268-276, March.
    3. Easley,David & Kleinberg,Jon, 2010. "Networks, Crowds, and Markets," Cambridge Books, Cambridge University Press, number 9780521195331.
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    Cited by:

    1. Luisa Roa & Andr'es Rodr'iguez-Rey & Alejandro Correa-Bahnsen & Carlos Valencia, 2021. "Supporting Financial Inclusion with Graph Machine Learning and Super-App Alternative Data," Papers 2102.09974, arXiv.org.

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