Machine learning techniques for default prediction: an application to small Italian companies
Problem
Predicting loan default of small Italian companies, traditionally handled with statistical classifiers such as logistic regression.
Why it matters
Defaults by small firms create lender losses and affect credit access in the economy.
What the AI did
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.
Results
Machine learning was found slightly better than logistic regression, but the small performance gain was deemed insufficient to abandon classical classifiers.
Impact
The evidence supports only a marginal ML advantage over logistic regression in this dataset.
Record
| 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 |
Importance rating
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.
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