金融领域机构准备状态的AI治理
AI Governance for Institutional Readiness in Finance
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中文总结 AI 辅助
针对金融领域智能体AI治理滞后于部署的问题，本文提出四层可计算AI治理框架，通过实证研究验证其有效性并给出90天实施序列，明确跨主体风险控制的可转移措施。
中文摘要 AI 辅助
智能体AI（Agentic AI）在资产管理中正获得认可，但治理措施未能同步跟进：接受调查的金融专业人士中88%报告，尽管普遍知晓智能体AI的部署，却无针对该技术的运营治理框架；在75家提交Form ADV文件披露AI使用情况的美国大型资管机构中，仅24家报告具备正式治理政策。本文认为该差距是架构性的，而非文化性的：为确定性系统构建的治理假设静态验证，而持续再训练的智能体策略本质上违反静态治理要求。本文提出四层框架（政策层、工程层、组合层、系统层），具备可计算的实例化方案：仅通过观测数据即可检测策略漂移的遗憾协方差统计量，以及校准后的拥挤模型，该模型显示当机构集中于相关敞口时，联合回撤概率从39.2%升至79.3%。本文通过对已部署的LLM嵌入交易策略及同期主动管理基金爆仓的研究验证该框架，明确哪些控制措施可在智能体与人类主导的风险承担间转移，还为机构提供了90天的框架实施序列。
英文摘要
Agentic AI is gaining acceptance in asset management, but governance has not kept pace: 88\% of surveyed finance professionals report no operational governance framework for agentic AI, and only 24 of 75 large U.S. money managers disclosing AI use in Form ADV filings report a formal governance policy. We argue this gap is architectural: governance built for static validation does not survive continuously retrained agentic policies. We propose a four-layer framework (Policy, Engineering, Composition, Systemic) grounded in two distinct kinds of evidence, kept explicitly separate: two calibrated synthetic illustrations (a regret-covariance drift monitor; a crowding simulation showing joint drawdown risk rising from 39.2\% to 79.3\%), and three real, documented cases (a deployed LLM-embedding trading strategy, a \$45 billion discretionary fund's forced-deleveraging blowup, and a tribunal ruling holding an airline liable for its chatbot). The synthetic examples demonstrate computability from observable data; the cases demonstrate that the failure modes are not hypothetical. We provide a 90-day implementation sequence spanning trading and payments/customer-facing systems.
