面向受监管金融的合规 AI 框架：分层多智能体与 DLT 审计链路
Compliant AI Infrastructure for Regulated Finance: A tiered multi-agent framework with DLT audit trails for financial operations in DACH
给做金融 AI 的人看的论文：教你怎么把监管要求直接嵌进智能体执行层，审计员可以重放验证每一步，DACH 和欧盟都能落地。
arXiv 论文提出一套合规优先的金融 AI 架构，将监管视为导向层而非确定性规则集。系统用监管意图与暴露度的矩阵做分类，再由策略编译器映射为具体禁止项、义务和运行时预算。证据、决策和原因码绑定到带确定性时间戳的许可 DAG 上，支持审计重放、溯源检查和失败归因。条款级法律索引配合能力路由，保证框架在 DACH 及更广泛的欧盟范围内可移植。
Compliant AI Infrastructure for Regulated Finance: A tiered multi-agent framework with DLT audit trails for financial operations in DACH
We present a compliance-first architecture for AI in regulated finance that treats regulation as an orientation layer rather than a deterministic ruleset. A matrix of regulatory intent and exposure provides a compact classification handle, which a governed policy compiler then maps into concrete prohibitions, obligations and runtime budgets. Prohibitions constrain feasibility and block externalisation, while obligations extend tasks with artefacts that must meet explicit admissibility criteria. Committee activation remains policy-driven and proportionate, preserving efficiency while ensuring supervisory oversight. Evidence, decisions and reason codes are bound to a permissioned DAG with deterministic timestamping, enabling replay, provenance checks and clear attribution of failure. Clause-level legal indexing with effective dates and capability-based agent routing ensure portability across DACH and the wider EU. The result is assurance by construction: compliance is embedded in execution and verifiable by auditors without sacrificing proportionality or transparency.
