Finance-Grade Assurance for Agentic AI: Verifiable Governance, Systemic Risk Mitigation, and Sustainability/Compute Accounting Architecture for banks, insurers, and major financial services providers

This paper proposes the Finance-Grade Verifiable Governance Architecture (FG-VGA), an implementable layered framework for deploying agentic AI systems in high-stakes financial environments. Agentic AI—capable of autonomous goal-directed planning, tool-use, and execution—poses unique risks in workflows like portfolio management, market-making, credit decisions, fraud detection, and insurance underwriting/claims, where correlated failures can amplify systemic instability. Current standards (e.g., SR 11-7, ISO 42001, DORA) lack machine-verifiable controls for multi-agent autonomy, such as policy gating, evidence binding, and monoculture metrics. FG-VGA decomposes assurance into four auditable "currencies" (probabilistic, energy/compute, epistemic, and social/environmental safety) grounded in first-principles (energy/compute, information/entropy, capital/liquidity, agency/authority). It enforces controls via policy-as-code (fail-close gates), immutable evidence packets, remote attestation, systemic coupling proxies (spectral radius ρ(W)), and sustainability accounting aligned to PCAF/GHG protocols. A novel Heterogeneity Score (H_S) operationalizes model diversification as a gate, with mathematical lemmas proving bounds on loss amplification under monoculture. Game-theoretic alignment ensures Nash-stable compliance. The architecture draws analogies from geometric safety (Swiss e-scooter kinetic bounding), network neuroscience (modularity/interconnectivity), ecological resilience (generalist/specialist dynamics), and decision theory (speed-accuracy trade-offs). It includes blueprints, phased rollouts, resource estimates, checklists, a synthetic case study, and red-team scenarios. An optional functional/variational upgrade generalizes scalars to computable integrals over probe measures, enabling sensitivity analysis without empirical over-reliance. This work bridges supervisory expectations (ESRB/FSB/FINMA) with AI-specific needs, providing banks, insurers, and providers a pathway to resilient agentic deployment. Residual risk is bounded and governable, converting uncertainty into board-accountable operation.