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Credit Scoring for Peer-to-Peer Lending

Author

Listed:
  • Daniel Felix Ahelegbey

    (Department of Economics and Management Sciences, University of Pavia, 27100 Pavia, Italy)

  • Paolo Giudici

    (Department of Economics and Management Sciences, University of Pavia, 27100 Pavia, Italy)

Abstract

This paper shows how to improve the measurement of credit scoring by means of factor clustering. The improved measurement applies, in particular, to small and medium enterprises (SMEs) involved in P2P lending. The approach explores the concept of familiarity which relies on the notion that the more familiar/similar things are, the closer they are in terms of functionality or hidden characteristics (latent factors that drive the observed data). The approach uses singular value decomposition to extract the factors underlying the observed financial performance ratios of SMEs. We then cluster the factors using the standard k-mean algorithm. This enables us to segment the heterogeneous population into clusters with more homogeneous characteristics. The result shows that clusters with relatively fewer number of SMEs produce a more parsimonious and interpretable credit scoring model with better default predictive performance.

Suggested Citation

  • Daniel Felix Ahelegbey & Paolo Giudici, 2023. "Credit Scoring for Peer-to-Peer Lending," Risks, MDPI, vol. 11(7), pages 1-8, July.
    • Handle: RePEc:gam:jrisks:v:11:y:2023:i:7:p:123-:d:1188948
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    References listed on IDEAS

    as
    1. Christian Haddad & Lars Hornuf, 2019. "The emergence of the global fintech market: economic and technological determinants," Small Business Economics, Springer, vol. 53(1), pages 81-105, June.
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