Ensuring consistency, fairness, and transparency in cross-regulatory compliance has become a critical national priority as enterprises increasingly file interdependent reports to agencies such as the IRS, SEC, and DOL. However, fragmented regulatory ecosystems often lead to inconsistent filings, elevated fraud risk, and inefficient allocation of audit resources. To address these challenges, this paper proposes a unified audit-intelligence framework that integrates neuro-symbolic reasoning, blockchain-based trust management, and deep reinforcement learning within a multi-agent system. First, a neuro-symbolic consistency engine combines graph neural networks with first-order logical rules to detect subtle, cross-form discrepancies that may indicate misreporting or fraud. Second, all generated evidence trails and verification outcomes are anchored on a permissioned blockchain to ensure tamper-proof traceability and transparent regulatory collaboration. Third, a multi-agent deep reinforcement learning module dynamically allocates IRS audit resources by jointly optimizing long-term tax recovery and fairness objectives, mitigating disproportionate enforcement on disadvantaged groups or small businesses. Experimental simulations using synthetic and semi-real regulatory datasets demonstrate that the proposed system significantly improves cross-agency report consistency detection (+21.7%), enhances audit transparency, and reduces allocation bias by up to 34%. This research provides a technologically grounded pathway for modernizing regulatory intelligence, safeguarding tax bases, and supporting equitable and sustainable compliance enforcement in the United States.
Tiantian Zhang (2025) studied this question.