Leveraging natural language processing for automated regulatory compliance in financial reporting

Kothari, Sonali (2025) Leveraging natural language processing for automated regulatory compliance in financial reporting. Global Journal of Engineering and Technology Advances, 23 (3). 091-099. ISSN 2582-5003

Natural Language Processing (NLP) is revolutionizing regulatory compliance in the financial sector by automating the interpretation and implementation of complex regulatory frameworks. Financial institutions face mounting challenges in parsing extensive regulatory requirements amid continuously evolving Basel III, Dodd-Frank, and FASB guidelines. This article explores how financial institutions can leverage NLP technologies to transform traditional manual compliance processes into automated, efficient systems. Through advanced techniques including domain-specific language models, semantic analysis, and knowledge graphs, NLP systems process regulatory documents with substantially higher accuracy than conventional review methods. The implementation architecture integrates data acquisition, analytical processing, and business integration layers to create end-to-end compliance traceability. Real-world implementations demonstrate significant improvements in processing time, accuracy, and cost savings. Despite challenges including regulatory ambiguity and cross-jurisdictional variations, the strategic implementation of NLP solutions with human-in-the-loop frameworks and ethical considerations offers transformative potential for regulatory compliance, reducing operational risks while strengthening financial institutions' ability to meet global reporting obligations in an increasingly complex regulatory landscape.