AI-Powered Regulatory Compliance Checker for Contracts

Abstract

The rapid growth of digital contracts across
industries such as healthcare, finance, information technology,
and government sectors has increased the complexity of
regulatory compliance management. Traditional manual
compliance verification methods are time-consuming,
expensive, and prone to human errors. This research paper
presents an AIpowered Regulatory Compliance Checker for
Contracts that leverages Natural Language Processing (NLP),
Machine Learning (ML), and Large Language Models (LLMs)
to automate the analysis of legal agreements and identify
compliance risks. The proposed system extracts contractual
clauses, maps them with regulatory frameworks such as
GDPR, HIPAA, PCI-DSS, and organizational policies, and
detects missing or non-compliant clauses. The architecture
integrates document ingestion, clause classification, semantic
analysis, retrieval-augmented generation (RAG), and risk
scoring mechanisms. Experimental evaluation demonstrates
improved efficiency, accuracy, scalability, and consistency in
contract analysis compared to traditional manual review
systems. The research highlights the potential of AI-driven
legal technology to transform regulatory compliance
management while reducing operational costs and legal risks.