INTELLIGENT METHODS FOR ASSESSING RISKS AND FINANCIAL STABILITY OF ENTERPRISES BASED ON BIG DATA
DOI:
https://doi.org/10.32782/ecovis/2026-1-2Keywords:
financial risks, financial stability of the enterprise, Big Data, artificial intelligence, machine learning, predictive analytics, financial modelingAbstract
In the current conditions of digitalization of the economy and the increasing level of uncertainty in the business environment, the problem of effective assessment of financial risks and ensuring the financial stability of enterprises is of particular relevance. The purpose of the study is to substantiate the theoretical foundations and develop conceptual approaches to the application of intelligent methods of assessing financial risks and financial stability of enterprises based on the analysis of big data. The study used a complex of general scientific and special methods, in particular methods of system analysis, economic and mathematical modeling, comparative analysis, generalization and methods of intelligent data analysis. The article analyzes modern scientific approaches to assessing the financial stability of enterprises and identifies the main trends in the development of methods for analyzing financial risks in the context of the digital transformation of the economy. Intelligent methods for assessing financial risks are systematized, in particular, machine learning algorithms (Random Forest, Gradient Boosting, Support Vector Machine, Logistic Regression), neural networks and big data analysis technologies. Their main advantages compared to traditional statistical methods of financial analysis are identified, in particular, the ability to process large volumes of heterogeneous information, identify complex nonlinear dependencies between financial indicators and increase the accuracy of forecasting the financial instability of enterprises. A conceptual model of an intelligent system for assessing the financial stability of enterprises is proposed, which is based on the use of Big Data technologies and machine learning algorithms. The developed model involves the integration of several functional modules, in particular, a data collection module, a pre-processing module, an analytical module and a management decision support module. This approach allows for a comprehensive analysis of the financial condition of enterprises, the formation of financial risk indicators and the prediction of possible financial crises. The paper also proposes an econometric model for assessing the impact of the level of digitalization and the use of intelligent technologies on the financial performance of enterprises, based on the analysis of return on assets (ROA) and return on equity (ROE). The results of the econometric analysis indicate a positive impact of the use of artificial intelligence technologies and big data analytics on increasing the financial performance of enterprises.
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