Firms Default Prediction with Machine Learning
This work addresses firm default prediction for banks and financial institutions, but it is incremental as it applies existing machine-learning methods to a new dataset.
The study tackled the problem of predicting firm defaults using a large, granular credit database from the Italian Central Credit Register combined with public balance sheet data, finding that ensemble techniques and random forest provided the best results, corroborating prior findings.
Academics and practitioners have studied over the years models for predicting firms bankruptcy, using statistical and machine-learning approaches. An earlier sign that a company has financial difficulties and may eventually bankrupt is going in \emph{default}, which, loosely speaking means that the company has been having difficulties in repaying its loans towards the banking system. Firms default status is not technically a failure but is very relevant for bank lending policies and often anticipates the failure of the company. Our study uses, for the first time according to our knowledge, a very large database of granular credit data from the Italian Central Credit Register of Bank of Italy that contain information on all Italian companies' past behavior towards the entire Italian banking system to predict their default using machine-learning techniques. Furthermore, we combine these data with other information regarding companies' public balance sheet data. We find that ensemble techniques and random forest provide the best results, corroborating the findings of Barboza et al. (Expert Syst. Appl., 2017).