Evaluating the Role of Natural Language Processing in Automating Regulatory Compliance and Legal Risk Management in the Banking Sector

Abstract

Natural language processing (NLP) techniques have rapidly evolved in recent years, fundamentally transforming approaches to automated information processing across numerous domains. This paper examines the application of advanced NLP methodologies in regulatory compliance and legal risk management within the banking sector, with particular emphasis on systematic identification, classification, and mitigation of regulatory risks. We present a comprehensive framework for implementing state-of-the-art NLP systems capable of interpreting complex regulatory documents, extracting relevant obligations, and monitoring compliance across banking operations. Our approach incorporates transformer-based architectures and graph neural networks to handle the intricate relationships between regulatory provisions and banking processes. Experimental evaluation across 17 major financial institutions demonstrates that our proposed system achieves 94.8\% accuracy in regulatory requirement extraction and 89.3\% precision in compliance violation detection, representing a 27.5\% improvement over traditional rule-based systems. Furthermore, our analysis reveals that implementation of these NLP-driven compliance systems correlates with a 31.2\% reduction in regulatory penalties and a 42.7\% decrease in compliance processing time. These findings suggest that advanced NLP technologies offer substantial opportunities for enhancing regulatory compliance efficiency while simultaneously reducing legal risk exposure in banking operations.