Verifiably Autonomous: Formal Verification of Autonomous AI Agent Decision Chains in Zero Trust Financial Risk and Fraud Environments (Iconic Research and Engineering Journals)

Summary

• Fin-HVA mathematically certifies autonomous agent decisions, eliminating safety violations (0.0% Violation Rate) with task success rates above >94% and step latency below <1.3s.p.1
• Layer 2 SMT solvers Z3 and CVC5 perform reachability verifications and extract counterexample traces if checks time out after 500 ms.p.6
• The Layer 3 Runtime Financial Safety Shield acts as a real-time firewall operating with low latency (<5ms) to execute deterministic fallback policies.p.6
• In real-time AML interdiction tests, Fin-HVA achieved a 0.0% Safety Violation Rate, 98.5% Task Success Rate, 0.82 s mean latency, and 93.1% self-healing convergence.p.8
• In enterprise credit adjustment benchmarks, Fin-HVA reached a 0.0% Safety Violation Rate, 96.1% Task Success Rate, 1.05 s mean latency, and 88.9% self-healing convergence.p.8
• Disabling the Runtime Financial Safety Shield component allowed a 1.6% violation rate during rare solver execution timeouts (>500ms).p.8
• Removing the counterexample feedback loop caused the Task Success Rate to decrease from 94.2% to 61.8%.p.8

Key insights

1. Traditional empirical validation methods such as backtesting and stress testing cannot guarantee safety across the non-deterministic state spaces of generative AI models.p.2
2. Multi-step neural reachability approximations can suffer from compounding errors over time, producing overly conservative bounds.p.4
3. Guardrail frameworks based on natural language regex matching or LLM prompts lack formal mathematical safety guarantees.p.4
4. Fin-HVA separates generative reasoning from deterministic risk enforcement across three synchronized architectural layers.p.5
5. The Runtime Financial Safety Shield operates inline at ultra-low latency (<5ms) to intercept unauthorized transactions before execution.p.6
6. Unconstrained generative LLM baselines produced safety violation rates of 19.2% in AML interdiction and 22.8% in credit adjustment tasks.p.8
7. Ablation trials demonstrate that removing counterexample feedback drops task success to 61.8% because agents fail to resolve unhandled fallbacks.p.8