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Verifiably Autonomous: Formal Verification of Autonomous AI Agent Decision Chains in Zero Trust Financial Risk and Fraud Environments
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
Abstract
As autonomous artificial intelligence (AI) agents transition from passive credit scoring and fraud classification to executing multi-step decision chains in safety-critical financial environments—such as high-frequency algorithmic market making, automated anti-money laundering (AML) asset freezing, cross-border settlement, and dynamic liquidity management—ensuring high precision alongside absolute risk compliance becomes paramount. Traditional statistical validation, backtesting, and empirical stress-testing fall short when confronting the inherent opacity, non-determinism, and infinite state spaces of generative models and deep neural controllers. A single hallucinated transaction parameter set, unauthorized logic branch, or out-of-distribution execution can trigger systemic market instability, severe regulatory non-compliance, or irreversible capital destruction. This paper presents a novel Financial Hybrid Verification Architecture (Fin-HVA) designed to mathematically certify the correctness, policy compliance, and risk bounds of autonomous agent decision sequences prior to market execution. We introduce a neuro-symbolic semantic parser that converts complex natural language reasoning and tool-use parameter arrays into formal Linear Temporal Logic (LTL) formulas and Satisfiability Modulo Theories (SMT) queries. By coupling static SMT reachability solvers with a real-time Runtime Financial Safety Shield (RFSS) and an autonomous evaluator-optimizer repair loop, Fin-HVA guarantees that agent decisions remain strictly bounded within predefined risk envelopes (e.g., maximum drawdown thresholds, capital adequacy ratios, and regulatory compliance constraints). We evaluate Fin-HVA across three simulated high-risk financial domains: automated high-frequency algorithmic liquidity management, dynamic credit line adjustment, and real-time AML transaction interdiction. Our experimental results demonstrate that Fin-HVA completely eliminates safety and compliance violations (0.0% Violation Rate) while maintaining high operational task success rates (>94%) with negligible step latency (<1.3s). This research establishes a rigorous mathematical foundation for deploying fully autonomous AI agents within highly regulated financial ecosystems.
Keywords
Formal Verification; Autonomous AI Agents; Financial Risk Management; Fraud Detection; Anti-Money Laundering (AML); Algorithmic Trading; Neuro-Symbolic AI; Runtime Safety Shield
References
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How to cite this paper
@article{1724674,
author = {Olakunle Olaniyi},
title = {Verifiably Autonomous: Formal Verification of Autonomous AI Agent Decision Chains in Zero Trust Financial Risk and Fraud Environments},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {5},
pages = {816-824},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1724674.pdf},
abstract = {As autonomous artificial intelligence (AI) agents transition from passive credit scoring and fraud classification to executing multi-step decision chains in safety-critical financial environments—such as high-frequency algorithmic market making, automated anti-money laundering (AML) asset freezing, cross-border settlement, and dynamic liquidity management—ensuring high precision alongside absolute risk compliance becomes paramount. Traditional statistical validation, backtesting, and empirical stress-testing fall short when confronting the inherent opacity, non-determinism, and infinite state spaces of generative models and deep neural controllers. A single hallucinated transaction parameter set, unauthorized logic branch, or out-of-distribution execution can trigger systemic market instability, severe regulatory non-compliance, or irreversible capital destruction. This paper presents a novel Financial Hybrid Verification Architecture (Fin-HVA) designed to mathematically certify the correctness, policy compliance, and risk bounds of autonomous agent decision sequences prior to market execution. We introduce a neuro-symbolic semantic parser that converts complex natural language reasoning and tool-use parameter arrays into formal Linear Temporal Logic (LTL) formulas and Satisfiability Modulo Theories (SMT) queries. By coupling static SMT reachability solvers with a real-time Runtime Financial Safety Shield (RFSS) and an autonomous evaluator-optimizer repair loop, Fin-HVA guarantees that agent decisions remain strictly bounded within predefined risk envelopes (e.g., maximum drawdown thresholds, capital adequacy ratios, and regulatory compliance constraints). We evaluate Fin-HVA across three simulated high-risk financial domains: automated high-frequency algorithmic liquidity management, dynamic credit line adjustment, and real-time AML transaction interdiction. Our experimental results demonstrate that Fin-HVA completely eliminates safety and compliance violations (0.0% Violation Rate) while maintaining high operational task success rates (>94%) with negligible step latency (<1.3s). This research establishes a rigorous mathematical foundation for deploying fully autonomous AI agents within highly regulated financial ecosystems.},
keywords = {Formal Verification; Autonomous AI Agents; Financial Risk Management; Fraud Detection; Anti-Money Laundering (AML); Algorithmic Trading; Neuro-Symbolic AI; Runtime Safety Shield},
month = {November},
}