AI Assisted Workflow Optimization and Automation

Abstract:Against the backdrop of digital transformation and stricter regulation, enterprise compliance work demands higher efficiency and accuracy. The auxiliary compliance process has become an important entry point for optimizing the compliance system due to its strong transactional nature and high degree of repetition. This study focuses on the process characteristics of auxiliary compliance work, sorts out its structural composition and organizational mechanism, proposes an optimization path with process reengineering, system modeling, and technology integration as the core, and focuses on exploring the collaborative application of key technologies such as RPA, rule engine, and semantic recognition in process automation. Research suggests that the systematic optimization and intelligent upgrading of auxiliary processes will help build a modern compliance operation system that is responsive, efficient, structurally clear, and risk controllable.

5 pages, 1 figure. Published in International Journal of Advanced Computer Science and Applications (IJACSA), Vol. 16, No. 10, 2025

Software Engineering (cs.SE); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG); Risk Management (q-fin.RM)

arXiv:2609.14323 [cs.SE]

(or arXiv:2609.14323v1 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2609.14323 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

International Journal of Advanced Computer Science and Applications (IJACSA), Vol. 16, No. 10, 2025

https://doi.org/10.14569/IJACSA.2025.0161001 Focus to learn more DOI(s) linking to related resources

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