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

Alessandro Bitetto, Paola Cerchiello, Stefano Filomeni (), Alessandra Tanda and Barbara Tarantino
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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

Review of Quantitative Finance and Accounting, 2024, vol. 63, issue 3, No 5, 925-954

Abstract: 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.

Keywords: Small businesses; Credit rating; Credit risk; Invoice lending; Machine learning; Fintech (search for similar items in EconPapers)
JEL-codes: C52 C53 D82 D83 G21 G22 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11156-024-01278-0

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