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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, India
tajiir 2025
VOL. 7 / NO. 09 SEPTEMBER
FULL TEXT PP. 138-144
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VOLUME 7
ISSUE 09
YEAR 2025
PAGES 138-144
RESEARCH OVERVIEW

Abstract

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.

RESEARCH TOPICS

Keywords

AI-driven risk assessment financial forecasting hazardous-material transportation

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References

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