PolicyGuard AI Policy-Driven Compliance Monitoring System

We are presenting PolicyGuard AI, a policy-driven compliance monitoring system designed for financial institutions.

Today, banks store AML and compliance policies in long PDF documents written in human language.
But transaction data exists in structured formats like CSV files and databases.

The core problem is this:

Policies are written for humans.
Monitoring systems require machine-executable logic.

Because of this gap, compliance monitoring becomes:

• Expensive

• Hardcoded

• Difficult to update

• And often produces high false positives

PolicyGuard AI aims to bridge that gap.

Our system is designed to:

• Ingest compliance policy documents

• Convert them into structured, machine-readable rules using LLMs

• Dynamically apply those rules to transaction datasets

• Detect violations using both rule-based logic and behavioral ML

• Provide explainable outputs for auditing

In this prototype phase, we demonstrate:

• Policy ingestion

• Dynamic rule engine

• Hybrid anomaly detection

• Explainable flagged transactions

• And a compliance dashboard with downloadable reports

With Google Cloud integration, this system will evolve into:

• OCR for scanned policies

• Gemini-powered rule extraction

• And fully automated compliance monitoring

Our vision is to build an adaptive, explainable RegTech engine that automatically converts regulatory documents into executable monitoring logic.