Compliance DNA: The "Gap" Between Law and Code

The "Audit Panic" Scenario

In fintech, an audit isn't just a meeting—it's a crisis mode. Why? Because regulations, internal policies, and actual system code exist in three different universes.

1. Regulations live in 500-page PDFs on government websites.
2. Policies live in forgotten Word docs on SharePoint.
3. Code lives in Git.

Proving that function validate_transfer() (Code) correctly implements "Section 3.2 of the AML Act" (Regulation) usually involves weeks of manual cross-referencing.

Bridging the Gap with AI

We built the Compliance DNA Engine for the RuleZ x AI Hackathon to solve this "traceability gap" automatically. It's not just a document search; it's a semantic lineage system.

How It Works

The engine creates a Knowledge Graph that links these disparate worlds:

1. Extraction: We use LLMs (GPT-4o) to "read" regulations and extract atomic rules (Obligations, Prohibitions, Thresholds).
2. Vectorization: These rules are embedded into a vector space.
3. Graph Construction: Use Neo4j to build relationships: (Policy)-[:IMPLEMENTS]->(Regulation) and (SystemRule)-[:ENFORCES]->(Policy).

The Result: Real-Time Gap Detection

Instead of waiting for an audit to find out we're non-compliant, the system runs continuous "Gap Analysis":

• Missing Implementation: "Hey, this new Fed regulation has no matching internal policy."
• Threshold Mismatch: "Regulation says LTV limit is 75%, but your code config is set to 80%."
• Drift Detection: "The underlying law changed yesterday. Your code is now stale."

The Tech Stack

We prioritized speed and explainability:

• FastAPI: For the REST interface.
• Neo4j: To model the complex web of many-to-many relationships between laws and code.
• Streamlit: For a rapid, interactive dashboard that auditors can actually use.
• OpenAI: For the messy task of unstructured text extraction.

This moves compliance from "archaeology" (digging through the past) to "monitoring" (watching the present).

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