Bitetto, Alessandro and Cerchiello, Paola and Filomeni, Stefano and Tanda, Alessandra and Tarantino, Barbara (2024) Can we trust machine learning to predict the credit risk of small businesses? Review of Quantitative Finance and Accounting, 63 (3). pp. 925-954. DOI https://doi.org/10.1007/s11156-024-01278-0
Bitetto, Alessandro and Cerchiello, Paola and Filomeni, Stefano and Tanda, Alessandra and Tarantino, Barbara (2024) Can we trust machine learning to predict the credit risk of small businesses? Review of Quantitative Finance and Accounting, 63 (3). pp. 925-954. DOI https://doi.org/10.1007/s11156-024-01278-0
Bitetto, Alessandro and Cerchiello, Paola and Filomeni, Stefano and Tanda, Alessandra and Tarantino, Barbara (2024) Can we trust machine learning to predict the credit risk of small businesses? Review of Quantitative Finance and Accounting, 63 (3). pp. 925-954. DOI https://doi.org/10.1007/s11156-024-01278-0
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.
Item Type: | Article |
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Uncontrolled Keywords: | Small businesses; Credit rating; Credit risk; Invoice lending; Machine learning; Fintech |
Divisions: | Faculty of Social Sciences Faculty of Social Sciences > Essex Business School |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 19 Jul 2024 12:27 |
Last Modified: | 06 Oct 2024 17:48 |
URI: | http://repository.essex.ac.uk/id/eprint/38502 |
Available files
Filename: s11156-024-01278-0.pdf
Licence: Creative Commons: Attribution 4.0