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

Flavio Bazzana, Marco Bee, Ahmed Almustfa Hussin Adam Khatir

Risk Management 2023 article cited by 19

importance 4/10 ai assisted Financial Distress and Bankruptcy Prediction credit risk stage: research

Predicting loan default of small Italian companies, traditionally handled with statistical classifiers such as logistic regression.

Defaults by small firms create lender losses and affect credit access in the economy.

Various machine learning classifiers were trained on a large sample of small Italian companies, with feature selection and threshold tuning, and compared against logistic regression for default prediction.

Machine learning was found slightly better than logistic regression, but the small performance gain was deemed insufficient to abandon classical classifiers.

The evidence supports only a marginal ML advantage over logistic regression in this dataset.

Venue Risk Management
Published 2023
Work type article
Authors Flavio Bazzana, Marco Bee, Ahmed Almustfa Hussin Adam Khatir
Citations 19 (OpenAlex, at import time)
References 31
Model or method not stated in source
Organization not stated in source
Primary source https://doi.org/10.1057/s41283-023-00132-2
Open access hybrid
Keywords default prediction, machine learning, credit risk, logistic regression, small companies, feature selection
4 / 10

real but narrower advance

Default prediction matters for lenders and credit access, but abstract reports only slight ML gain over logistic regression on one national sample; narrow advance.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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