AI-Driven Risk Management in OBOR Infrastructure Projects

Lai Mun Keong
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Lai Mun Keong: Faculty of Accountancy, Finance and Business, Tunku Abdul Rahman University of Management and Technology, Jalan Genting Klang, Setapak, 53300, Kuala Lumpur, Malaysia

International Journal of Research and Innovation in Social Science, 2025, vol. 9, issue 2, 1221-1232

Abstract: The “One Belt, One Road†(OBOR) initiative, now widely referred to as the Belt and Road Initiative (BRI), represents one of the most ambitious infrastructure and economic development projects in modern history, encompassing over 140 participating countries. Despite its potential for fostering global connectivity and economic growth, OBOR projects face significant risks, including financial, operational, geopolitical, and environmental uncertainties. This study explores the potential of artificial intelligence (AI) to revolutionize risk management in OBOR infrastructure projects, addressing challenges such as cost overruns, project delays, and political instability. By leveraging AI technologies such as machine learning, natural language processing, predictive analytics, and risk assessment models, stakeholders can enhance their ability to identify, quantify, and mitigate risks in real-time. AI tools offer unparalleled capabilities in processing vast amounts of data from multiple sources, including financial reports, satellite imagery, and social media, to predict and analyse risks. For instance, AI-driven algorithms can monitor geopolitical developments to assess the likelihood of conflicts or trade disruptions affecting project timelines. Similarly, predictive models can forecast weather patterns and environmental hazards, enabling project planners to implement proactive strategies for mitigating potential disruptions. This study employs a mixed-methods approach, combining quantitative data analysis and qualitative case studies of OBOR infrastructure projects that have successfully implemented AI-driven risk management solutions. The findings demonstrate that AI significantly enhances decision-making accuracy, improves resource allocation, and reduces the probability of adverse events. Case studies from railway and port construction projects in Southeast Asia and Central Asia illustrate how AI tools have enabled project managers to optimize operations, minimize delays, and reduce costs.

Date: 2025
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