employee
Russian Federation
employee
Russian Federation
The article examines modern risk management tools and strategies in project activities aimed at minimizing losses and improving the efficiency of project implementation. Based on the analysis of international research – the development of AI tools for predictive risk management, Active Risk Management methodology and empirical data on the lack of real risk visibility - traditional and innovative approaches to identifying, assessing and responding to project risks are considered. Special attention is paid to the integration of artificial intelligence technologies into risk management processes, including automated risk identification based on project characteristics and the generation of context-sensitive mitigation strategies. Based on empirical data, two tables are presented that characterize the state of risk management practices and the key components of active risk management. It is concluded that there is a need to move from reactive fragmented risk management to an integrated proactive model based on a unified data architecture and predictive analytics.
risk management, loss minimization, artificial intelligence, active risk management, predictive analytics, risk-based approach
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