Corporate Default Forecasting with Machine Learning
Year: 2020
Type: article
Abstract: We compare statistical models usually employed in credit risk forecasting with machine learning algorithms (ML). Using a large dataset which includes financial ratios and credit behavioral indicators for about 300,000 Italian non-financial firms from 2011 to 2017, we show that training the models on financial statement data only, ML models record a significant improvement in discriminatory power and precision with respect to statistical models; however, this improvement is less pronounced when we enlarge the training dataset to include also credit behavioral data
Source: Risk Management Magazine
DOI: 10.47473/2020rmm0071
Authors: Banca di Italia, Mirko Moscatelli, Simone Narizzano, Banca di Italia, Fabio Parlapiano, Banca di Italia, Gianluca Viggiano, Banca di Italia
Institution: Bank of Italy
Language: English
Subfield: Accounting
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W3122595987