发表机构
Royal Bank of Canada（加拿大皇家银行）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对KYC开户流程耗时问题，提出Spectra平台，结合规则引擎与LLM智能体，实现文档分类、提取和验证自动化，达到100%分类准确率、89.4%提取准确率，人工负担降低96%。
AI 中文摘要
资本市场中的“了解你的客户”（KYC）开户流程要求分析师手动对文档进行分类、从异构来源提取结构化数据，并依据复杂的监管政策进行合规性验证。由于顺序交接，每个客户的分析师时间占用显著，端到端开户往往拖延至数周。在本工作中，我们分析了一个开户流程，发现其包含适合AI自动化的可重复组件。因此，我们提出了一种适合自动化的重构工作流：将传统的四方流程整合为两方，共享大部分工作并可以一起自动化，消除了加剧延迟的中间交接。为了自动化剩余步骤，我们引入了Spectra，一个AI辅助的文档处理平台，它结合了结构化规则引擎与基于LLM的分类、提取和验证智能体。规则引擎将合规政策编码为可查询的数据库，实现聚焦的上下文注入，从而减少令牌使用并提高提取精度。系统不是采用单一的庞大提示，而是将文档处理分解为隔离的、可审计的阶段，每个阶段独立优化并可追溯到特定的政策条款。在真实KYC文档的评估中，Spectra实现了100%的分类准确率和89.4%的提取准确率。人工审查负担下降了96%。
英文摘要
Know Your Client (KYC) onboarding in capital markets requires analysts to manually classify documents, extract structured data from heterogeneous sources, and validate compliance against complex regulatory policies. This process requires significant analyst time per client, with end-to-end onboarding often stretching to multiple weeks due to sequential handoffs. In this work, we analyze an on-boarding process and find that it comprises repeatable components well-suited to AI automation. We therefore propose a restructured workflow to be amenable to automation: we consolidate the traditional four-party process into two parties that share most of the work and can be automated together, eliminating intermediate handoffs that compound delays. To automate the remaining steps, we introduce Spectra, an AI-assisted document processing platform that combines a structured rules engine with LLM-based classification, extraction, and validation agents. The rules engine encodes compliance policy as a queryable database, enabling focused context injection that reduces token usage while improving extraction precision. Rather than a single monolithic prompt, the system decomposes document processing into isolated, auditable stages, each optimized independently and traceable to specific policy clauses. In evaluation on real KYC documents, Spectra achieves 100% classification accuracy and 89.4% extraction accuracy. Human review burden dropped by 96%.
