用可解释的智能体系统自动化金融合规，提升透明度与信任。
Agentic AI for Financial Crime Compliance
- 设计可追踪的智能体架构，分角色执行合规任务
- 实现从开户到报告全流程自动化，支持审计日志
- 适合金融科技与监管科技从业者参考落地
金融犯罪合规（FCC）的成本与复杂性持续上升，但效果难以衡量。尽管人工智能具有潜力，现有方案大多缺乏透明度且不符合监管预期。本文通过与金融科技公司及监管方合作的行动设计研究（ADR），构建并部署了一种面向数字原生金融平台的智能体式AI系统。该系统自动完成开户、监控、调查和报告等环节，强调可解释性、可追溯性和合规即设计。采用以工件为中心的建模方法，为自主智能体分配明确职责，支持任务特定模型路由与审计日志。贡献包括一套参考架构、一个真实原型，以及在监管约束下重构合规流程的洞见。研究扩展了信息系统领域关于AI驱动合规的文献，证明当自动化嵌入可问责治理结构时，可在高风险受监管环境中促进透明度与制度信任。
原文摘要 · Abstract (English)
The cost and complexity of financial crime compliance (FCC) continue to rise, often without measurable improvements in effectiveness. While AI offers potential, most solutions remain opaque and poorly aligned with regulatory expectations. This paper presents the design and deployment of an agentic AI system for FCC in digitally native financial platforms. Developed through an Action Design Research (ADR) process with a fintech firm and regulatory stakeholders, the system automates onboarding, monitoring, investigation, and reporting, emphasizing explainability, traceability, and compliance-by-design. Using artifact-centric modeling, it assigns clearly bounded roles to autonomous agents and enables task-specific model routing and audit logging. The contribution includes a reference architecture, a real-world prototype, and insights into how Agentic AI can reconfigure FCC workflows under regulatory constraints. Our findings extend IS literature on AI-enabled compliance by demonstrating how automation, when embedded within accountable governance structures, can support transparency and institutional trust in high-stakes, regulated environments.
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