APPLICATION OF RISK PREDICTION SYSTEMS IN THE FOREIGN ECONOMIC ENVIRONMENT: METHODOLOGICAL FOUNDATIONS AND TRANSFORMATION VECTORS IN THE CONTEXT OF INDUSTRY 4.0
Abstract
The purpose of this study is to analyze the methodological foundations and transformation vectors of risk forecasting systems in the foreign economic environment under the conditions of Industry 4.0. The research highlights existing methodological approaches to risk management from statistical models to hybrid systems that combine artificial intelligence, big data analytics, blockchain, digital twins and foresight technologies turning forecasting into a tool of strategic anticipation. The influence of Industry 4.0 technologies is analyzed, demonstrating that the integration of sensor networks, cloud services, edge analytics and AI forms a multi-level ecosystem in which speed, accuracy and reliability mutually reinforce each other. It is proven that the combination of digital twins and blockchain enables the transition toward an integrated trust infrastructure. The study presents international experience in applying risk forecasting systems in logistics, trade and financial sectors, showing the effectiveness of flexible, self-learning and cross-sector synchronized platforms. Measures have been developed to adapt this experience to Ukraine, combining data centralization, risk monitoring, digital modernization, simulation forecasting and generative intelligence. Such an approach integrates Ukraine into the global risk management ecosystem, enhancing its resilience and transparency. Transformation vectors of risk forecasting systems have been formulated, encompassing generative AI, quantum computing, edge analytics, simulators, the Internet of Everything, hyper-personalized platforms, as well as preventive and ethical models. It is demonstrated that their synergy ensures the transition from local analytics to global cognitive ecosystems capable of adapting to and preventing risks in real time.
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