eISSN: 2791-173X editor@grpublishing.org
All Journals
Submit your article

Global Multidisciplinary Journal

Open Access Peer Review International
Open Access

The Convergence of Artificial Intelligence and Multi-Sectoral Risk Management: A Comprehensive Analysis of Algorithmic Governance, Predictive Analytics, And Operational Resilience

DOI
PDF
Hugo Martin Lefevre 1 ,
4 Department of Data Science and Strategic Management, University of Edinburgh, United Kingdom

Abstract

The rapid integration of Artificial Intelligence (AI) and Machine Learning (ML) across diverse industrial sectors has fundamentally altered the landscape of risk management. This research article provides a comprehensive investigation into how AI-driven frameworks are being deployed to mitigate financial, operational, and environmental risks. By synthesizing evidence from the construction industry, financial services, healthcare, and public sector governance, the study evaluates the transition from reactive risk mitigation to proactive, predictive intelligence. Key focus areas include the use of neural networks for construction safety, deep learning for financial fraud detection, and the role of AI in climate change adaptation. The research further explores the ethical dimensions of AI governance, focusing on fairness, transparency, and the reduction of algorithmic bias. Through an extensive review of contemporary literature and patent data, this article identifies the systemic shifts in organizational structures necessitated by AI adoption. The findings suggest that while AI significantly enhances the accuracy of risk scoring and the efficiency of data migration in cloud environments, challenges related to explainability (XAI) and human-resource integration remain. The study concludes with a strategic roadmap for embedding AI into long-term risk management frameworks to ensure competitive advantage and societal resilience.

Keywords

Artificial Intelligence, Risk Management, Predictive Analytics, Financial Technology

References

📄 Abioye, S. O., Oyedele, L. O., Akanbi, L., Ajayi, A., Delgado, J. M. D., Bilal, M., Akinade, O. O., & Ahmed, A. (2021). Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges. Journal of Building Engineering, 44, 103299.
📄 Adedokun, O., Egbelakin, T., & Omotayo, T. (2024). Random forest and path diagram taxonomies of risks influencing higher education construction projects. International Journal of Construction Management, 24(1), 66-74.
📄 Aggabou, L. K., Lakehal, B., & Mouda, M. (2024). An artificial neural network approach for construction project risk management. International Journal of Safety and Security Engineering, 14(2), 553-561.
📄 Aghimien, D., Aigbavboa, C., & Oke, A. (2019). A review of the application of data mining for sustainable construction in Nigeria. Energy Procedia, 158, 1016.
📄 Akinosho, T. D., Oyedele, L. O., Bilal, M., Ajayi, A. O., Delgado, M. D., Akinade, O. O., & Ahmed, A. A. (2020). Deep learning in the construction industry: A review of present status and future innovations. Journal of Building Engineering, 32, 101827.
📄 Alekseytsev, A. V., & Nadirov, S. H. (2022). Scheduling optimization using an adapted genetic algorithm with due regard for random project interruptions. Buildings, 12(12), 10.3390/buildings12122051.
📄 Ali, O., Abdelbaki, W., Shrestha, A., Elbasi, E., Alryalat, M. A. A., & Dwivedi, Y. K. (2023). A systematic literature review of artificial intelligence in the healthcare sector: benefits, challenges, methodologies, and functionalities. Journal of Innovation & Knowledge, 8(1), 100333.
📄 Alkaissy, M., Arashpour, M., E. M. Golafshani, Hosseini, M. R., Khanmohammadi, S., Bai, Y., & Feng, H. (2023). Enhancing construction safety: machine learning-based classification of injury types. Safety Science, 162, 106102.
📄 Ammirato, S., Felicetti, A. M., Linzalone, R., Corvello, V., & Kumar, S. (2023). Still our most important asset: A systematic review on human resource management in the midst of the fourth industrial revolution. Journal of Innovation & Knowledge, 8(3), 100403.
📄 An, X., Zheng, F., Jiao, Y., Li, Z., Zhang, Y., & He, L. (2024). Optimized machine learning models for predicting crown convergence of plateau mountain tunnels. Transportation Geotechnics, 46, 101254.
📄 Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975.
📄 Armetti, G., & Panciera, A. (2023). Risk management process for underground works. Expanding Underground - Knowledge and Passion to Make a Positive Impact on the World- Proceedings of the ITA-AITES World Tunnel Congress, WTC 2023, 2974-2981.
📄 Arnarsson, I. Ö., Frost, O., Gustavsson, E., Jirstrand, M., & Malmqvist, J. (2021). Natural language processing methods for knowledge management-Applying document clustering for fast search and grouping of engineering documents. Concurrent Engineering-Research and Applications, 29(2), 142-152.
📄 Ashtari, M. A., Ansari, R., Hassannayebi, E., & Jeong, J. (2022). Cost overrun risk assessment and prediction in construction projects: A bayesian network classifier approach. Buildings, 12(10), 1660.
📄 Benbya, H., Davenport, T. H., & Pachidi, S. (2020). Artificial intelligence in organizations: Current state and future opportunities. MIS Quarterly Executive, 19(4).
📄 Bouchetara, M., Zerouti, M., & Zouambi, A. R. (2024). LEVERAGING ARTIFICIAL INTELLIGENCE (AI) IN PUBLIC SECTOR FINANCIAL RISK MANAGEMENT: INNOVATIONS, CHALLENGES, AND FUTURE DIRECTIONS. EDPACS, 1–21.
📄 Devan, M., Shanmugam, L., & Tomar, M. (2021). AI-Powered Data Migration Strategies for Cloud Environments: Techniques, Frameworks, and Real-World Applications. Australian Journal of Machine Learning Research & Applications, 1(2), 79-111.
📄 Giudici, P., & Raffinetti, E. (2022). Explainable AI methods in cyber risk management. Quality and reliability engineering international, 38(3), 1318–1326.
📄 Jones, K., Spaeth, J., Rykowski, A., Manjunath, N., Vudutala, S. K., Malladi, R., & Mishra, A. (2018). U.S. Patent No. 10,057,117. Washington, DC: U.S. Patent and Trademark Office.
📄 Leal Filho, W., Wall, T., Mucova, S. A. R., Nagy, G. J., Balogun, A. L., Luetz, J. M., ... & Gandhi, O. (2022). Deploying artificial intelligence for climate change adaptation. Technological Forecasting and Social Change, 180, 121662.
📄 Lee, M. S. A., Floridi, L., & Denev, A. (2021). Innovating with confidence: embedding AI governance and fairness in a financial services risk management framework. In Ethics, governance, and policies in artificial intelligence (pp. 353–371). Cham: Springer International Publishing.
📄 Milojević, N., & Redzepagic, S. (2021). Prospects of artificial intelligence and machine learning application in banking risk management. Journal of Central Banking Theory and Practice, 10(3), 41–57.
📄 Sanil, H. S., Singh, D., Raj, K. B., Choubey, S., Bhasin, N. K. K., Yadav, R., & Gulati, K. (2021). Role of machine learning in changing social and business eco-system–a qualitative study to explore the factors contributing to competitive advantage during COVID pandemic. World Journal of Engineering, 19(2), 238-243.
📄 Singh, S., Mohan, R., Deshpande, A., Nukala, S., Dwadasi, V. S. A., & Jilani, S. (2024). Artificial Intelligence and Machine Learning in Financial Services: Risk Management and Fraud Detection. Journal of Electrical Systems, 20(6s), 1418–1424.
📄 Varanasi, S. R. (2025). AI for CAB Decisions: Predictive Risk Scoring in Change Management. International Research Journal of Advanced Engineering and Technology, 2(06), 16-22. https://doi.org/10.55640/irjaet-v02i06-03
📄 Wang, B. (2024). A financial risk identification model based on artificial intelligence. Wireless Networks, 30(5), 4157–4165.
📄 Zigienė, G., Rybakovas, E., & Alzbutas, R. (2019). Artificial intelligence based commercial risk management framework for SMEs. Sustainability, 11(16), 4501.

How to Cite

Hugo Martin Lefevre. (2026). The Convergence of Artificial Intelligence and Multi-Sectoral Risk Management: A Comprehensive Analysis of Algorithmic Governance, Predictive Analytics, And Operational Resilience. Global Multidisciplinary Journal, 5(02), 68-75. https://www.grpublishing.org/journals/index.php/gmj/article/view/360

Most read articles by the same author(s)

<< < 2 3 4 5 6 7 8 9 10 11 > >> 

Similar Articles

1-10 of 104

You may also start an advanced similarity search for this article.