ANALYTICAL SUPPORT FOR CORPORATE RISK MANAGEMENT IN THE CONTEXT OF CONTEMPORARY CHALLENGES
Abstract
The article investigates the theoretical and methodical foundations of analytical support for corporate risk management in the context of increasing uncertainty, digital transformation, and the growing complexity of the contemporary risk environment. The study substantiates the need to move from the use of separate analytical tools toward the formation of an integrated analytical support system that combines information resources, analytical methods, predictive models, digital technologies, and decision-support mechanisms within a unified information and analytical environment. The purpose of the research is to substantiate the theoretical and methodological foundations of analytical support for corporate risk management, clarify its conceptual essence, identify its key functions and structural components, systematize modern analytical tools, and develop a conceptual model of an integrated analytical support system. The study proposes an original definition of analytical support for corporate risk management, which, unlike existing approaches, conceptualizes it as an integrated system that ensures the continuous identification, analysis, assessment, forecasting, and monitoring of risks while supporting risk-oriented managerial decision-making. The study identifies the key functions and structural components of the system, classifies contemporary analytical tools according to their functional purpose and analytical complexity, and develops a conceptual model reflecting the interaction of its main components and the continuous analytical cycle of risk management. The proposed approach contributes to a more comprehensive understanding of the role of analytical support in enhancing the effectiveness and adaptability of corporate risk management systems. The practical significance of the obtained results lies in the possibility of applying the proposed conceptual approach and model to improve corporate risk management systems, strengthen information and analytical support, enhance the quality of managerial decision-making, and increase the resilience and long-term competitiveness of companies under contemporary challenges.
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Zio E., Miqueles L. (2024). Digital twins in safety analysis, risk assessment and emergency management. Reliability Engineering & System Safety, vol. 246. Available at: https://doi.org/10.1016/j.ress.2024.110040
Copyright (c) 2026 Ірина Чирак
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