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Can we trust machine learning to predict the credit risk of small businesses?

Author

Listed:
  • Alessandro Bitetto

    (Department of Economics and Management)

  • Paola Cerchiello

    (Department of Economics and Management)

  • Stefano Filomeni

    (Essex Business School, Finance Group)

  • Alessandra Tanda

    (Department of Economics and Management)

  • Barbara Tarantino

    (Department of Economics and Management)

Abstract

With the emergence of Fintech lending, small firms can benefit from new channels of financing. In this setting, the creditworthiness and the decision to extend credit are often based on standardized and advanced machine-learning techniques that employ limited information. This paper investigates the ability of machine learning to correctly predict credit risk ratings for small firms. By employing a unique proprietary dataset on invoice lending activities, this paper shows that machine learning techniques overperform traditional techniques, such as probit, when the set of information available to lenders is limited. This paper contributes to the understanding of the reliability of advanced credit scoring techniques in the lending process to small businesses, making it a special interesting case for the Fintech environment.

Suggested Citation

  • Alessandro Bitetto & Paola Cerchiello & Stefano Filomeni & Alessandra Tanda & Barbara Tarantino, 2024. "Can we trust machine learning to predict the credit risk of small businesses?," Review of Quantitative Finance and Accounting, Springer, vol. 63(3), pages 925-954, October.
    • Handle: RePEc:kap:rqfnac:v:63:y:2024:i:3:d:10.1007_s11156-024-01278-0
    DOI: 10.1007/s11156-024-01278-0
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    More about this item

    Keywords

    Small businesses; Credit rating; Credit risk; Invoice lending; Machine learning; Fintech;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies

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