Articles
| Open Access | Cross‑Domain AI‑Driven Risk Assessment: Integrating Financial Forecasting And Hazardous‑Material Transportation Risk Paradigms
Arjun R. Mehta , Department of Systems Engineering, Global Institute of Technology, IndiaAbstract
The rising complexity of modern socio‑economic systems demands sophisticated risk assessment frameworks capable of anticipating adverse outcomes across heterogeneous domains. This paper proposes a novel, cross‑domain conceptual framework for AI-driven risk assessment that synthesizes insights from two apparently disparate fields: financial market risk forecasting and hazardous-material (hazmat) transportation risk analysis. Drawing on the latest developments in augmented analytics and predictive modeling within finance (Hassan, 2025; Pala, 2023; Oko‑Odion, 2025; Kesarpu & Dasari, 2025) and on extensive literature on risk modeling for hazardous-material transport (Liu et al., 2021; Dong et al., 2020; Li et al., 2020; Ditta et al., 2019; Guo & Luo, 2022; Huang et al., 2018; Liu et al., 2018; Erkut et al., 2007; Ma et al., 2020), the framework aims to bridge methodological gaps and deliver a unified paradigm. The proposed framework foregrounds privacy-preserving federated learning techniques (Kalejaiye, 2025) and real-time event sourcing architectures (Kesarpu & Dasari, 2025) as foundational components. We argue that many of the structural and methodological challenges in financial risk forecasting—such as non-stationarity, data sparsity, and regulatory constraints—mirror those in hazmat transportation risk modeling, such as accident unpredictability, environmental variability, and inter-jurisdictional data silos. Through this convergence, the paper outlines theoretical implications, exposes limitations, and sets a research agenda for future empirical validation. The integration of cross-domain methods promises enhanced predictive accuracy, improved resilience, and holistic risk oversight in industries where both financial and physical risks are significant.
Keywords
AI-driven risk assessment, financial forecasting, hazardous-material transportation
References
Hassan T. Augmented Analytics in Financial Forecasting: Enhancing Risk Management and Investment Strategies through AI‑Driven Insights. 2025 Mar 14.
Kalejaiye AN. Federated learning in cybersecurity: privacy‑preserving collaborative models for threat intelligence across geopolitically sensitive organizational boundaries. Int J Adv Res Publ Rev. 2025;2(07):227–50.
Pala SK. Role and importance of predictive analytics in financial market risk assessment. International Journal of Enhanced Research in Management & Computer Applications. 2023;1:2319‑7463.
Kesarpu S., Dasari H. P. Kafka Event Sourcing for Real-Time Risk Analysis. International Journal of Computational and Experimental Science and Engineering. 2025;11(3).
Oluwagbade E. Bridging the healthcare gap: the role of AI‑driven telemedicine in emerging economies. Int J Res Publ Rev. 2025 Jan;6(1):3732–43.
Oko‑Odion C. AI‑Driven Risk Assessment Models for Financial Markets: Enhancing Predictive Accuracy and Fraud Detection. International Journal of Computer Applications Technology and Research. 2025;14(04):80–96.
Anietom Ifechukwu Chelsea. Evaluating machine learning-based polypharmacy risk prediction in multigenerational households under family medicine, internal and pediatric care interfaces. Int J Sci Res Arch. 2025;16(01):1288–1306.
Liu L., Wu Q., Li S., Li Y., Fan T. Risk assessment of hazmat road transportation considering environmental risk under time‑varying conditions. Int. J. Environ. Res. Public Health. 2021;18:9780.
Dong S., Zhou J., Ma C. Design of a network optimization platform for the multivehicle transportation of hazardous materials. Int. J. Environ. Res. Public Health. 2020;17:1104.
Li Y., Xu D., Shuai J. Real-time risk analysis of road tanker containing flammable liquid based on fuzzy Bayesian network. Process Saf. Environ. Prot. 2020;134:36–46.
Ditta A., Figueroa O., Galindo G., Yie‑Pinedo R. A review on research in transportation of hazardous materials. Socio‑Econ. Plan. Sci. 2019;68:100665.
Guo J., Luo C. Risk assessment of hazardous materials transportation: A review of research progress in the last thirty years. J. Traffic Transp. Eng. (Engl. Ed.) 2022;9:571–590.
Erkut E., Tjandra S., Verter V. Hazardous materials transportation. Handbook of Operations Research and Management Science 2007;14:539–621.
Huang X., Wang X., Pei J., Xu M., Huang X., Luo Y. Risk assessment of the areas along the highway due to hazardous material transportation accidents. Nat. Hazards. 2018;93:1181–1202.
Liu X., Turla T., Zhang Z. Accident‑cause‑specific risk analysis of rail transport of hazardous materials. Transportation Research Record: Journal of the Transportation Research Board. 2018;2672:176–187.
Ma C., Zhou J., Xu X., Pan F., Xu J. Fleet scheduling optimization of hazardous materials transportation: A literature review. J. Adv. Transp. 2020;2020:4079617.
Download and View Statistics
Copyright License
Copyright (c) 2025 Arjun R. Mehta
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain the copyright of their manuscripts, and all Open Access articles are disseminated under the terms of the Creative Commons Attribution License 4.0 (CC-BY), which licenses unrestricted use, distribution, and reproduction in any medium, provided that the original work is appropriately cited. The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations.