Paper Title
NLP-Enabled Legal Compliance Monitoring: Automating GST Law Interpretation and Case-Tracking for Businesses

Abstract
This study investigates how Natural Language Processing (NLP) and machine learning (ML) can be embedded within legal compliance monitoring to automate GST law interpretation and case-tracking for businesses, strengthening regulatory governance and supporting sustainable industrial growth aligned with Sustainable Development Goal 9. Building on recent advances in transformer-based language models and document intelligence, we propose a hybrid compliance framework that combines rule-based GST validation logic with supervised and semi-supervised ML models to classify notices, predict compliance risk, and prioritize legal actions across high-volume indirect tax workflows. The approach integrates NLP pipelines for extracting entities (GSTIN, invoice IDs, sections, deadlines), mapping statutory references to a structured “GST knowledge graph,” and generating explainable compliance flags using SHAP/LIME-style attribution to ensure transparency and audit readiness. To align with enterprise governance and model-risk expectations, the architecture incorporates data lineage, monitoring, and validation controls inspired by BCBS 239 and SR 11-7, enabling traceable decision-making across filings, reconciliations, and dispute handling. Evaluation on a multiindustry corpus of GST circulars, case orders, notices, and internal compliance records demonstrates improved accuracy in issue classification and deadline tracking, alongside meaningful reduction in manual review time and missed-response risk through automated alerts and prioritization. The paper further discusses scalable deployment using cloud-based NLP services, challenges related to evolving legal language, multilingual documents, data privacy, and organizational adoption, and policy implications for building compliance-first digital infrastructure that reduces friction for businesses while improving enforcement effectiveness. Keywords - GST Compliance, Indirect Taxation, Legal Compliance Monitoring, Natural Language Processing (NLP), Transformer-Based Language Models, Document Intelligence, Case-Tracking Automation, Notice Classification, Litigation Analytics, GST Knowledge Graph, Entity Extraction (NER), Information Retrieval, Risk Scoring, Anomaly Detection, Explainable AI, SHAP, LIME, Regulatory Governance, Audit Readiness, Data Lineage, Model Risk Management, BCBS 239, SR 11-7, Cloud-Based AI Infrastructure, Real-Time Compliance Alerts, Sustainable Development Goal 9 (SDG 9), Digital Public Infrastructure