2020/02/17 by Tesi Aliaj, Aliaj, Tesi, Aris Anagnostopoulos +3
Business, Management and Accounting · Computer Science · Economics, Econometrics and Finance · #Credit Risk and Financial Regulations #FOS: Computer and information sciences #FOS: Economics and business #Financial Distress and Bankruptcy Prediction #Imbalanced Data Classification Techniques #J.4 #Machine Learning (cs.LG) #Risk Management (q-fin.RM)
paper · pdf · doi:10.48550/arxiv.2002.11705
openalex publication_date 2020/02/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 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).