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

Kristina Sutiene, Kestutis Luksys and Kristina Kundeliene
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Kristina Sutiene: Dept. of Mathematical Modeling, Kaunas University of Technology, Studentu st. 50-144, 51368 Kaunas, Lithuania
Kestutis Luksys: Dept. of Applied Mathematics, Kaunas University of Technology, Studentu st. 50-327a, 51368 Kaunas, Lithuania
Kristina Kundeliene: School of Economics and Business, Kaunas University of Technology, Gedimino st. 50-505, 44239 Kaunas, Lithuania

International Journal of Information Technology & Decision Making (IJITDM), 2021, vol. 20, issue 04, 1299-1333

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.

Keywords: Insolvency; short-term prediction; machine learning; real-world data; feature importance (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1142/S0219622021500334

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