From Probabilistic Policy to Deterministic Invariants: A Structural Framework for Eliminating AI-Washing in Financial Service Orchestration

As of 2026, financial regulators have shifted decisively from rulemaking to enforcement. In the United States and peer jurisdictions, unverifiable claims about artificial intelligence (AI) safety, fairness, and control are increasingly treated as potential material misstatements under existing securities, consumer protection, and financial integrity laws. A central enforcement target is “AI-washing”: the practice of asserting compliance properties that are not structurally guaranteed by system architecture. Most contemporary AI governance frameworks rely on probabilistic controls—policies, prompts, post-hoc monitoring, and human review—that reduce the likelihood of harm but cannot eliminate prohibited outcomes. This paper introduces the Accountability and Risk-Constraint System (ARC-S), a deterministic enforcement architecture that replaces probabilistic policy with structural invariants enforced at the orchestration layer. ARC-S converts regulatory obligations and fiduciary constraints into mechanically enforced prohibitions that render certain actions impossible to execute. By decoupling compliance enforcement from model inference, ARC-S enables financial institutions to satisfy emerging standards of care for non-discrimination, solvency protection, and decision accountability. The result is a transition from compliance by declaration to compliance by construction, eliminating AI-washing by design.