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The Neural Network Predictive Technologies for Crisis Classification and Bankruptcy Assessment of Trade Industry Entities
Bilotserkivskyi O. B., Momotkov I. S., Gudymenko V. P.

Bilotserkivskyi, Oleksandr B., Momotkov, Ihor S., and Gudymenko, Viacheslav P. (2024) “The Neural Network Predictive Technologies for Crisis Classification and Bankruptcy Assessment of Trade Industry Entities.” Business Inform 7:196–206.
https://doi.org/10.32983/2222-4459-2024-7-196-206

Section: Economic and Mathematical Modeling

Article is written in Ukrainian
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UDC 004.032.26:336.64

Abstract:
The ongoing war in Ukraine has led to a significant increase in the number of business bankruptcies. Timely assessment of bankruptcy risk is critical for making effective management decisions. The aim of the study is to develop an adaptive model for assessing the propensity of an enterprise to bankruptcy based on fuzzy neural networks, taking into account the specifics of the Ukrainian market in wartime. To achieve the aim, several stages were carried out: formation of a sample of enterprises in the trade industry; determination of crisis classes using cluster analysis; building a fuzzy neural network using the Fuzzy Logic Toolbox package in MATLAB; training the network on historical data and testing on new data. At the first stage, a sample of enterprises is formed, with the help of which the model will be trained. At the second stage, classes of crisis (threat of bankruptcy) are formed. At the third stage, with the help of the apparatus of fuzzy neural networks, a membership function with a certain set of parameters is formed. With the help of this function, it is possible to attribute this or that enterprise with a certain degree of belonging to one of the classes of crisis formed at the previous stage. The fourth stage is to check the adequacy of the obtained model and the possibility of its practical use. At the fifth stage, the practical implementation of the model is carried out: with the help of the membership function, the propensity of the researched enterprise to bankruptcy is determined, that is, to what extent the studied enterprise belongs to one or another class of crisis. As a result of the application of the proposed algorithm, a fuzzy neural network was built, which used the financial indicators of Ukrainian enterprises. To train the model, a dataset was used that included 12 enterprises in the trade industry. The developed model allows to classify enterprises by the level of financial sustainability with high accuracy. Fuzzy neural networks have been shown to be an effective tool for predicting bankruptcy under uncertainty. The use of fuzzy neural networks made it possible to take into account uncertainty and adapt to changes in the external environment. The obtained results indicate the perspective of using fuzzy neural networks to assess the financial sustainability of enterprises. The developed model can be used as a tool for early detection of signs of crisis and making timely managerial decisions.

Keywords: bankruptcy, classification, crisis, fuzzy neural networks, financial sustainability, forecasting.

Fig.: 7. Tabl.: 1. Formulae: 3. Bibl.: 19.

Bilotserkivskyi Oleksandr B. – Candidate of Sciences (Engineering), Associate Professor, Associate Professor, Department of Entrepreneurship, Trade and Logistics, National Technical University «Kharkiv Polytechnic Institute» (2 Kyrpychova Str., Kharkіv, 61002, Ukraine)
Email: [email protected]
Momotkov Ihor S. – Postgraduate Student, Department of Entrepreneurship, Trade and Logistics, National Technical University «Kharkiv Polytechnic Institute» (2 Kyrpychova Str., Kharkіv, 61002, Ukraine)
Email: [email protected]
Gudymenko Viacheslav P. – Postgraduate Student, Department of Entrepreneurship, Trade and Logistics, National Technical University «Kharkiv Polytechnic Institute» (2 Kyrpychova Str., Kharkіv, 61002, Ukraine)
Email: [email protected]

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