Intelligent Risk Assessment and Classification in Project Management Using Data-Driven AI Models
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V7I2P142Keywords:
Artificial Intelligence, Project Management, Risk Classification, Machine Learning, Deep Learning, Predictive Analytics, Data-Driven AI Models, Natural Language Processing, Fuzzy Logic, Hybrid AI SystemsAbstract
The rapid evolution of globalization, digitalization, and technological innovations has led to an increase in complexity of the project management domain; therefore, assessing and classifying the risks involved becomes increasingly complicated. The conventional techniques of risk management, which include risk identification, analysis, and forecasting, cannot adequately detect and control complex risks related to finance, operations, technology, strategy, and cybersecurity. In view of the aforementioned difficulties, the use of “Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), predictive analytics, and big data” has been identified as a smart way to improve the management of the project risks. This study is intended to review the contribution of AI-based solutions for risk assessment and classification in project management settings. This study discusses several types of AI algorithms and predictive models, including “Decision Tree, Random Forest, Support Vector Machine, Artificial Neural Networks, Recurrent Neural Network, and Long Short-Term Memory Models”, focusing on their uses, strengths, and weaknesses in project risk management. AI enhances the accuracy of predictions and decisions, enabling real-time monitoring and planning by uncovering patterns of risks and developing measures to mitigate them. Yet, several barriers remain that prevent the implementation of intelligent risk management practices, including data integrity problems, cybersecurity concerns, ethical issues, AI bias, lack of transparency, and absence of real-life validation studies. This paper concludes that the implementation of Explainable AI (XAI), real-time analysis capabilities, cloud computing technologies, and digital twins can improve the performance of intelligent risk management systems.
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