arXiv:2607. 04103v1 Announce Type: cross Abstract: The release of SR 26-2 marks a significant modernization of U.
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arXiv:2607. 04103v3 Announce Type: replace-cross Abstract: Generative artificial intelligence is moving from general-purpose experimentation toward specialized applications across banking, capital markets, insurance, payments, and wealth management.
arXiv:2606. 19887v1 Announce Type: cross Abstract: Existing safety benchmarks target general adversarial scenarios but miss finance-specific risks.
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.
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.
Financial institutions are beginning to deploy agentic workflows in credit, fraud, collections, compliance, and operational control. Governance remains largely component-centric: each model or agent i...
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.