RAG-Based AI Compliance Monitoring and Report Generation System
TL;DR
CompVault, an Enhanced Retrieval-Augmented Generation (ERAG)-based Artificial Intelligence Compliance Monitoring and Report Generation System for intelligent regulatory compliance assessment, and results indicate that the ERAG-based framework can be used as an efficient, scalable, and explainable solution for regulatory compliance monitoring and automated report generation.
Abstract
Compliance with regulatory rules becomes an increasingly complex task because of the constantly changing legal regulations and policy regulations. Currently, the existing approach to compliance verification is mostly based on the manual analysis of documents and is characterized by high time consumption, inconsistency, and high levels of operational risks. To solve the problem, this paper introduces CompVault, an Enhanced Retrieval-Augmented Generation (ERAG)-based Artificial Intelligence Compliance Monitoring and Report Generation System for intelligent regulatory compliance assessment. The introduced approach combines the semantic document retrieval, vector-based knowledge representation with ChromaDB, and contextual reasoning by means of the large language model to check compliance of organizational policies with regulatory requirements. It also determines the compliance gaps and compliance risk level and generates structured reports with recommendations to decision-makers. Web-based implementation was implemented using FastAPI, all-MiniLM-L6-v2 embeddings, and LexGLUE legal benchmark as a knowledge source about regulations. As a result, the following metrics were achieved: 97.42% accuracy, 96.88% precision, 97.15% recall, 97.01% F1-score, and 98.80% AUC-ROC. Therefore, the achieved results indicate that the ERAG-based framework can be used as an efficient, scalable, and explainable solution for regulatory compliance monitoring and automated report generation.