AI-Driven Compliance Audits: Enhancing Regulatory Adherence in Financial and |Legal Sectors

Abstract

AI-based compliance auditing leverages machine learning, natural language processing (NLP), and automation to identify regulatory breaches, pull out evidentiary matter, and deliver audit findings faster and more consistently. This paper suggests a hybrid compliance-audit model that integrates transformer-based NLP for contract and regulation interpreting, supervised anomaly detection with the transaction and reporting stream, and explainability layer mapping model outputs to regulation clauses and audit trails. The framework was applied to a corpus of synthesized and de-identified real financial transaction logs, regulatory filings, and contracts (N ≈ 1.2M records; 12K contract sections). Approaches for this included fine tuning pretrained legal transformers, gradient boosted anomaly detectors on engineered features to perform transaction monitoring and a rule based mapping module that transformed model signals into audit evidence. Performance was tested on three audit tasks: Contract-clause compliance identification, Anomalous transaction detection for regulatory reporting, and Evidence extraction for the audit trails. Results observed average task-level F1 scores of 0.88 (A), 0.84 (B), and 0.81 (C); precision/recall tradeoffs could be engineered to reflect organisational risk appetites. Traceability to regulation clauses measurable increased human auditor validation rate by up to 42% in a controlled study. The false positive rates were decreased by 31% in transaction detection under the same sensitivity level than baseline heuristics. The research shows that hybrid AI + rules approaches can make a significant improvement in the efficiency and regulation aliment of auditing while offering audit trails necessary for governance. Drawbacks are the representativeness of the dataset, human-in-the-loop validation of high-risk decisions.