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The paper discusses how financial institutions are increasingly using AI agents in areas such as credit, fraud, and compliance, yet current governance focuses only on individual components. It introduces ARIA, a finance‑specific reference architecture that adds six capabilities—policy specification, population‑level monitoring, bounded authority, runtime containment, adaptive policy change, and preserved human oversight—to address the gap of constitutional non‑compositionality. Two simulations demonstrate how local controls can miss collective bias and how observed‑versus‑expected monitoring can provide earlier warnings of drift.
The paper introduces a compliance-first AI architecture for regulated finance, treating regulation as an orientation layer rather than a rigid rule set. It uses a regulatory intent matrix and a governed policy compiler to translate regulatory requirements into concrete prohibitions, obligations, and runtime budgets, while maintaining proportional committee activation and supervisory oversight. Evidence and decisions are recorded on a permissioned DAG with deterministic timestamps, enabling replay, provenance checks, and clear attribution of failures, and clause-level legal indexing ensures portability across the DACH region and the EU.
arXiv:2608. 11344v1 Announce Type: cross Abstract: Financial institutions are delegating consequential decisions to agentic AI systems that decompose goals, coordinate models and tools, and act with little oversight.
arXiv:2608. 19278v1 Announce Type: cross Abstract: The most capable general-purpose AI (GPAI) models are mostly built in two jurisdictions, the United States and China, but the risks they carry land globally.
The paper introduces Governance-as-Code (GaC), a framework that translates the EU AI Act’s technical requirements into 43 machine‑checkable acceptance criteria across six compliance modules. GaC runs within a CI/CD pipeline, producing Article‑indexed audit evidence and providing actual Rego policy code. The authors validate GaC on two enterprise deployments, showing it reproduces manual audit findings—including three penalty‑triggering violations—while reducing audit labor by about 75%.
arXiv:2608. 14562v1 Announce Type: new Abstract: AI governance is shifting from voluntary ethics to enforceable, risk-based regulation, yet cross-jurisdictional divergence creates compliance uncertainty for operators of high-stakes AI.