Rodion Poliakov

Given rapid changes in global financial and economic processes caused by rapid transformations in the institutional environment and the onset of force majeure circumstances, there is a need to develop new approaches to assessing the level of bankruptcy. Most models that estimate the probability of enterprise bankruptcy are based on internal information, while external information is used to a limited extent. The growing threat of force majeure requires using not only the existing discrete models but also those that consider the external environment of enterprises when assessing the probability of bankruptcy. The purpose of this study is to develop a model for preventingbankruptcy of Ukrainian enterprises in force majeure conditions based on the use of artificial intelligence methods ‒ the theory of fuzzy logic– which allows for a comprehensive assessment of bankruptcy prevention. The paper uses analytical data from the World Bank. The model consists of interrelated groups of factors: organizational, informational and legal, and economic. As a result, a comprehensive indicator of prevention of corporate bankruptcies (D) was calculated on a neurolinguistic scale from 0 to 10; the indicator was estimated for Ukraine (5.644) and Romania (4.520) (as countries close in terms of economic and geopolitical development). The simulation results show that the level of prevention of enterprises’ bankruptcy in Ukraine falls into the average interval.

In 2020, due to the COVID-19 pandemic, a moratorium was imposed on launching bankruptcy proceedings for enterprises in Ukraine. It was canceled in 2022 because of the war to encourage the company management to improve the efficiency of liquidity and solvency management, seeking ways to increase companies’ profitability and reduce the probability of bankruptcy. The study aims to determine the impact of liquidity on unprofitability, which can be considered an element in the management decision-making system to prevent bankruptcies of Ukrainian companies. The correlation-regression analysis was based on statistical data from Ukrainian companies for 2012–2019 and 2013–2020. The study found practically no connection between the unprofitability of Ukrainian companies and the decrease in the number of court cases in which a decision was made to recognize the bankruptcy of Ukrainian companies. On the other hand, there is a strong connection between Ukrainian companies’ liquidity and unprofitability. The constructed regression equation is statistically reliable and characterized by a high level of adequacy to real economic processes and phenomena. An increase in the general liquidity ratio by 1% leads to an increase in the unprofitability of Ukrainian companies by 0.0346%. According to the company size construct, the most substantial connection is recorded for medium-sized companies (the correlation coefficient is 0.927, the coefficient of determination is 0.860, and the built correlation-regression equation is characterized by statistical reliability and adequacy). In contrast, large, small, and micro enterprises have a weak and moderate connection.

Small and medium-sized enterprises (SMEs) create more than half of the added value, providing about two-thirds of employment in most countries. However, they need more liquidity, access to credit resources, and significant outstanding loans. This study aims to identify the impact of interest rates on outstanding loans of enterprises on their structure as a way to prevent bankruptcy. The correlation-regression analysis used OECD statistical data for 2008‒2019 sampling individual countries; it showed an ambiguous situation between the interest rate and the share of outstanding loans of SMEs in the overall structure of outstanding loans. The paper verified constructed regression equations and estimated their parameters. The regression equations for Belgium, the Czech Republic, Estonia, and Latvia are statistically reliable. Thus, in Belgium and the Czech Republic, a negative relationship was recorded (r = –0.822; D = 0.675; r = –0.9274; D = 0.794; F-criterion > Ft, respectively), and in Estonia and Latvia – a positive one (r = 0.876; D = 0.767; F-criterion > F•Ft; r = 0.800; D = 0.641; F-criterion > Ft , respectively). Australia, Italy, Slovakia, and France practically do not have a corresponding relationship. The regression equations make it possible to estimate the change in the level of interest rate on the share of outstanding loans of enterprises in the overall structure of outstanding loans, make predictions of the corresponding performance indicator, and develop measures of restoring the solvency of enterprises as an essential task of preventing their bankruptcy.