Machine learning techniques for default prediction: an application to small Italian companies

Flavio Bazzana (), Marco Bee and Ahmed Almustfa Hussin Adam Khatir ()
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Flavio Bazzana: University of Trento
Ahmed Almustfa Hussin Adam Khatir: Tomasi Auto

Risk Management, 2024, vol. 26, issue 1, No 1, 23 pages

Abstract: Abstract Default prediction is the primary goal of credit risk management. This problem has long been tackled using well-established statistical classification models. Still, nowadays, the availability of large datasets and cheap software implementations makes it possible to employ machine learning techniques. This paper uses a large sample of small Italian companies to compare the performance of various machine learning classifiers and a more traditional logistic regression approach. In particular, we perform feature selection, use the algorithms for default prediction, evaluate their accuracy, and find a more suitable threshold as a function of sensitivity and specificity. Our outcomes suggest that machine learning is slightly better than logistic regression. However, the relatively small performance gain is insufficient to conclude that classical statistical classifiers should be abandoned, as they are characterized by more straightforward interpretation and implementation.

Keywords: Default risk; Classification; Feature selection; Imbalanced classes (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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DOI: 10.1057/s41283-023-00132-2

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