Automated Interpretation of Financial Regulations Using NLP: A Compliance-Centric Analysis of Legal Texts and Policy Adherence Frameworks

The increasing complexity and volume of financial regulations pose significant challenges for institutional compliance, traditionally reliant on manual review and static rule-based systems. This study investigates the application of Natural Language Processing (NLP) to automate the interpretation of financial regulatory texts and organizational compliance policies. By leveraging domain-adapted transformer architectures such as Legal-BERT and FinBERT, the proposed framework enables accurate classification, obligation extraction, and jurisdictional mapping across heterogeneous legal corpora.

A multi-jurisdictional dataset, comprising regulatory documents from the U.S., EU, and India, underpins the model development and evaluation. The system demonstrates high performance across key metrics—precision, recall, F1-score, and compliance accuracy—exceeding 90% in several use cases. Pilot implementations in financial institutions show significant reductions in manual workload and improved early detection of compliance risks. The architecture integrates seamlessly with Governance, Risk, and Compliance (GRC) systems via RESTful APIs, offering real-time analytics and interpretability through intuitive dashboards and explainable AI techniques.

The study addresses challenges related to data privacy, model transparency, and regulatory dynamism, proposing solutions such as continual learning and modular design. This research contributes to the RegTech domain by providing a scalable, adaptable, and legally defensible approach to compliance automation, with potential for cross-sectoral application in similarly regulated industries.