AI‑Powered Compliance Monitoring System

This project is an AI‑powered, software‑only Compliance Monitoring System designed to automatically analyze compliance policies and monitor structured databases to detect policy violations in an accurate, explainable, and scalable manner.

In many organizations, compliance requirements and business rules are stored as unstructured PDF documents, while operational data exists in structured databases. Manually interpreting policies and validating data is slow, error‑prone, and does not scale as data volumes grow. This project addresses that gap by bridging unstructured policy documents and structured enterprise data through automated rule extraction and continuous monitoring.

The system ingests free‑text PDF policy documents such as AML, GDPR, or internal business policies. Using NLP or LLM‑based techniques, it extracts actionable compliance rules and converts them into structured, machine‑readable formats. These rules are stored and applied to transactional or organizational data stored in a database or large CSV datasets. A rule engine scans the data efficiently, detects compliance violations, and generates explainable reports that clearly show which rule was violated, why it was violated, and which record triggered it.

The platform includes an interactive dashboard that allows users to upload policy documents, trigger compliance scans, and visualize detected violations through tables and summary metrics. The architecture is modular and scalable, supporting efficient querying, batch processing, and optimized scans, making it suitable for datasets with millions of records.

This prototype focuses on core compliance capabilities such as policy analysis, data analysis, and database monitoring, while remaining lightweight and feasible for hackathon and undergraduate project development. The system is designed to be extensible, allowing future enhancements such as real‑time monitoring, remediation recommendations, audit‑ready reports, and trend analysis over time.

Key Features

• PDF policy ingestion and automated rule extraction

• Machine‑readable compliance rule generation

• Database scanning and compliance violation detection

• Explainable and transparent violation reporting

• Dashboard for compliance monitoring and insights

• Scalable, modular backend architecture

Tech Stack

• Frontend: React

• Backend: FastAPI

• Rule Extraction: NLP / LLM‑based parsing

• Data Processing: Pandas / SQL

• Database: SQLite (prototype) / PostgreSQL (scalable)