Cross-Domain Assurance for Financial, Environmental, and Autonomous Risk

Keywords

artificial intelligence
environment

Abstract

This review examines cross-domain assurance for financial, environmental, and autonomous risk through an evidence-centered design lens. The analysis asks which assurance principles transfer and which remain domain-specific. It treats the relevant unit as a complete pathway from data or physical observations to representation, model output, human interpretation, and accountable action. The cited literature is synthesized without inventing experiments or unreported performance values. Particular attention is given to superficial analogy across evidence regimes. The review argues that credible translation requires explicit evidence boundaries, uncertainty-aware evaluation, author-visible traceability, and a documented route for intervention. The resulting framework supports portfolio-level technology review while distinguishing component promise from system readiness.

References

Beard, R. W., & McLain, T. W. (2012). Small unmanned aircraft: Theory and practice. Princeton University Press.

Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009). Curriculum learning. In Proceedings of the 26th International Conference on Machine Learning (pp. 41-48).

Deng, H., Luo, H., Zhu, Y., Li, L., Chen, Z., Zhao, X., Li, M., Zhang, J., Wang, M., Cao, Y., & Kang, Y. (2026). IIB-LPO: Latent policy optimization via iterative information bottleneck. arXiv preprint arXiv:2601.05870.

Lian, X., Wang, Y., Guo, J., Wan, X., Ye, X., Zhou, J., Han, R., Yu, H., Huang, S., & Li, J. (2024). The short-term effects of individual and mixed ambient air pollutants on suicide mortality: A case-crossover study. Journal of Hazardous Materials, 472, 134505.

Maclure, M. (1991). The case-crossover design: A method for studying transient effects on the risk of acute events. American Journal of Epidemiology, 133(2), 144-153. https://doi.org/10.1093/oxfordjournals.aje.a115853

Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50(3), 559-569. https://doi.org/10.1016/j.dss.2010.08.006

Qiu, M., Li, R., Cheng, Q., Xu, J., & Zheng, J. (2024, May). Construction of Financial Fraud Risk Assessment Model Assisted by Artificial Intelligence. In International Conference on Artificial Intelligence for Society (pp. 606-613). Cham: Springer Nature Switzerland.

Tao, J., Lyu, R., & Cao, X. (2026). A Deep Learning-Based Automated Content Moderation Framework for Online Platforms. Future-Adaptive Intelligence and Lifelong Systems, 1(1).

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.

Xu, M. (2026). Energy-Efficient UAV Path Planning for Maize Disease Monitoring via Generative Data Augmentation and Uncertainty-Guided Navigation. Smart Agricultural Technology, 102278.