Abstract

Project risk management is a process of identifying, assessing, and responding to potential adverse events throughout the life cycle of a project to minimize their impacts and capitalize on opportunities. Traditional project risk management approaches often rely on subjective expert judgment, static risk models, siloed data, and reactive strategies. However, with the increasing complexity and dynamism of projects, new sophisticated project risk management approaches are necessary to address the limitations of traditional methods. Artificial intelligence (AI), particularly through machine learning, can reimagine how risks are managed in projects, offering enhanced predictive capabilities, real-time insights, and adaptive strategies. While there is ongoing research, existing studies have only provided partial insights, leading to several debates on the suitable model for project risk management. Moreover, existing research has largely focused on generic risk prediction, while other important aspects of project risk management, such as risk prediction explainability, scoring, prioritization, and mitigation, remain largely unexplored. To address these knowledge gaps, we develop an intelligent AI-driven project risk management framework that combines AI models, tools, and techniques with risk identification, evaluation, and mitigation capabilities.

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