Towards Automation of Short-Term Financial Distress Detection: A Real-World Case Study

Abstract

The bankruptcy prediction research domain continues to evolve with the main aim of developing a model suitable for real-world application in order to detect early stages of financial distress of a company. The recent developments in computing, combined with the potential applications of big data technologies and artificial intelligence solutions have already made possible the integration of timely and recent information about business activities in order to monitor the financial health of companies. Therefore, this paper focuses on the predictions made a few months prior to the potential default of a company with the aim of identifying the determinants that signal about the insolvency. The experiments include in-depth analysis of model performances using different dataset configurations.