Volume 9, Number 6
Insolvency Prediction Analysis of Italian Small Firms by Deep Learning
Authors
Agostino Di Ciaccio1 and Giovanni Cialone2, 1University of Rome, La Sapienza, Italy and 2Senior partner of Kairos Advisory srl., Italy
Abstract
To improve credit risk management, there is a lot of interest in bankruptcy predictive models. Academic
research has mainly used traditional statistical techniques, but interest in the capability of machine
learning methods is growing. This Italian case study pursues the goal of developing a commercial firms
insolvency prediction model. In compliance with the Basel II Accords, the major objective of the model is
an estimation of the probability of default over a given time horizon, typically one year.
The collected dataset consists of absolute values as well as financial ratios collected from the balance
sheets of 14.966 Italian micro-small firms, 13,846 ongoing and 1,120 bankrupted, with 82 observed
variables. The volume of data processed places the research on a scale like that used by Moody’s in the
development of its rating model for public and private companies, RiskcalcTM. The study has been
conducted using Gradient Boosting, Random Forests, Logistic Regression and some deep learning
techniques: Convolutional Neural Networks and Recurrent Neural Networks. The results were compared
with respect to the predictive performance on a test set, considering accuracy, sensitivity and AUC. The
results obtained show that the choice of the variables was very effective, since all the models show good
performances, better than those obtained in previous works. Gradient Boosting was the preferred model,
although an increase in observation times would probably favour Recurrent Neural Networks.
Keywords
Credit risk, Bankruptcy prediction, Deep learning