NLP FOR LEGAL TEXT MINING: AUTOMATING COMPLIANCE AND POLICY ANALYSIS
Keywords:
Legal NLP; compliance automation; Regulatory text , text mining; machine learning; deep learning; transformers; legal Bert; Burt the Finn; Court prediction; Withdrawal of commitment; political analysis; Retching. Explainable artificial intelligence.Abstract
The rapid expansion of legal and regulatory texts has increased the burden on legal and compliance professionals, making manual review increasingly ineffective and inconsistent. Advances in natural language processing (NLP), machine learning (ML), and deep learning (DL) have made it possible to automate legal text mining, court prediction, and regulatory policy analysis. Guess , Guess what? A synthesis of recent literature shows that traditional machine learning methods such as Support Vector Machines, K-Nearest Neighbor's, and Random Forests are still widely used, while transformer-based deep learning models such as BERT, Legal-BERT, and Fin BERT have significantly improved performance on tasks including classification, summarization, liability extraction, and question answering. A systematic design of research between 2015 and 2022 highlights increasing methodological diversity, increasing availability of domain-specific models, and expanding compliance automation applications.
Empirical studies show that domain-aligned transformers achieve high accuracy, often exceeding 90%, in interpreting regulatory requirements, extracting obligations, and mapping cross-border rules across large legal bodies. Pilot deployments at financial institutions indicate significant reductions in manual workloads and increased detection of compliance risks, supported by integration with governance, risk and compliance (GRC) systems and explainable AI technologies.
