Compliance Digital Twins for Autonomous Financial Agents: Reliability-Aware Scenario Assurance via Calibrated LLM Evaluation

Abstract

Autonomous AI agents are increasingly deployed in financial operations such as invoice processing, vendor onboarding, and payment authorization, outpacing the governance frameworks designed to oversee them. Their probabilistic, multiagent behavior challenges traditional validation methods, which assume deterministic and bounded system operation and fail to capture compliance risks arising from coordinated agent interactions under realistic conditions.

This paper introduces the Compliance Digital Twin (CDT), a framework that constructs a scenario-driven replica of enterprise financial workflows, control policies, and identity management structures. Within this environment, agents are exercised under routine, rare, and adversarial conditions to evaluate their behavior against regulatory and internal control requirements prior to production deployment. The CDT incorporates a reliability-aware control layer that models compliance risk as a runtime observable, dynamically modulating agent autonomy and escalating high-risk actions to human oversight. It further synthesizes segregation-of-duties conflict scenarios, including toxic entitlement combinations, to verify adherence to authorization constraints.

Scenario outcomes are evaluated using a calibrated LLM-as-Judge module that assesses execution trajectories against compliance rubrics, mitigates the overconfidence that uncalibrated evaluators routinely exhibit, and produces statistically interpretable reliability scores with uncertainty quantification.

Simulation-based evaluation on a synthetic accounts payable workflow demonstrated a pre-deployment compliance detection rate of 89% compared to 43% for conventional testing, segregation-of-duties enforcement efficacy of 96%, and well-calibrated evaluation performance (ECE = 0.041 relative to a 0.05 target). These results demonstrate the effectiveness of the CDT for continuous, scenario-driven assurance of autonomous financial agents in regulated environments.