AI辅助工作流优化与自动化
AI Assisted Workflow Optimization and Automation
- Georgetown University（乔治城大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对企业合规辅助流程效率与准确性不足的问题，提出以流程再造、系统建模和技术整合为核心的优化路径，并探索RPA、规则引擎与语义识别的协同应用，以构建现代合规运营体系。
AI中文摘要：
在数字化转型和监管趋严的背景下，企业合规工作对效率和准确性的要求日益提高。辅助合规流程因其事务性强、重复度高，已成为优化合规体系的重要切入点。本研究聚焦辅助合规工作的流程特征，梳理其结构构成与组织机制，提出以流程再造、系统建模和技术整合为核心的优化路径，并重点探讨RPA、规则引擎和语义识别等关键技术在该流程自动化中的协同应用。研究表明，对辅助流程进行系统化优化和智能化升级，有助于构建响应迅速、运行高效、结构清晰且风险可控的现代合规运营体系。
英文摘要：
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.
