Use of Big Data in economic risk forecasting

Abstract

The relevance of the study is driven by the growing level of economic uncertainty, increasing instability of financial markets, accelerating digitalization of economic processes, and the need to improve the effectiveness of economic risk forecasting under conditions of dynamic changes in the global economic environment. Traditional approaches to risk analysis are increasingly proving insufficiently adaptive to the rapid renewal of information flows, which intensifies the need to use Big Data as a tool of comprehensive digital analytics. Particular importance is attached to the ability of Big Data to ensure the оперативe identification of risk trends, integration of heterogeneous data, and support for well-grounded managerial decision-making. The purpose of the study is to analyze the specific features of using Big Data in economic risk forecasting and to substantiate directions for improving the effectiveness of analytical support for management under conditions of economic digitalization. Methods. The study employs methods of generalization, systematization, comparative analysis, structural-functional approach, and analytical modeling to assess the functional capabilities of Big Data in the economic risk forecasting system. Results. The essential characteristics of economic risk forecasting and the functional capabilities of Big Data within the analytical support system of risk management are investigated. The influence of Big Data on increasing the accuracy, efficiency, and adaptability of forecasting models in the contemporary economic environment is identified. Methodological approaches to integrating Big Data tools into the processes of analysis and forecasting of economic risks are substantiated. It is established that the use of Big Data provides a more comprehensive identification of risk trends, contributes to rapid responses to crisis factors, and increases the effectiveness of preventive economic risk management. At the same time, key problems of using Big Data are identified, among which the dependence of forecasting results on data quality, the complexity of integrating information flows, insufficient transparency of self-learning algorithms, and risks of information overload dominate. Conclusions. The expediency of implementing integrated analytical systems, adaptive forecasting models, and early risk detection systems is proved. The practical significance of the obtained results lies in the possibility of applying the proposed approaches to improve systems of economic analysis and enhance the effectiveness of managerial decision-making. Prospects for further research are associated with the development of adaptive digital forecasting models and the improvement of mechanisms for integrating Big Data into economic security systems.