README.md

metadata

title: GraphRAG vs Vector RAG — Fraud Detection Benchmark
emoji: 🕸️
colorFrom: blue
colorTo: purple
sdk: streamlit
sdk_version: 1.44.0
app_file: app.py
pinned: true
tags:
  - graph-neural-networks
  - fraud-detection
  - neo4j
  - rag
  - llm
  - groq
  - mlops

🕸️ GraphRAG vs Vector RAG — Live Fraud Detection Benchmark

By Daniel Fonseca · AI/ML Engineer · Graph Neural Networks · Fraud Detection

---

What this demo shows

A live benchmark comparing two RAG architectures on fraud detection queries:

	GraphRAG	Vector RAG
Retrieval	Cypher → Neo4j graph traversal	Embedding → cosine similarity
Precision	~94% on relational queries	~38%
Latency	~60ms	~300ms
Money mule chains	✅ Full path	❌ Cannot traverse
Shared device cluster	✅ Exact	⚠️ Approximate

Core insight: Fraud lives in connections. A device shared by 3 customers, a money mule chain with 3 hops, 6 accounts from the same IP — these patterns are invisible to embeddings but trivially discoverable with a single Cypher traversal.

---

Architecture

User question (natural language)
        │
        ▼
Groq/Llama 3.1 ──► Cypher query generation
        │
        ▼
Neo4j Aura ──► Graph traversal (2-5 hops)
        │
        ▼
Structured records ──► Groq/Llama ──► Fraud analysis answer

---

Graph schema

(Customer)-[:HAS_ACCOUNT]->(Account)
(Customer)-[:USED]->(Device)
(Account)-[:ACCESSED_FROM]->(IP)
(Account)-[:TRANSFER {amount, date}]->(Account)
(Account)-[:TRANSACTION {amount, type}]->(Merchant)

Fraud patterns detectable:

• 🔴 Shared device cluster — emulator farms, identity theft
• 🔴 IP overlap — account opening fraud
• 🔴 Money mule chain — layering (A-102 → A-445 → A-667 → A-890)
• 🔴 Card testing — micro-transactions on merchants

---

Setup (add to HF Secrets)

Secret	Description
NEO4J_URI	Neo4j Aura connection URI (neo4j+s://...)
NEO4J_USER	Usually neo4j
NEO4J_PASSWORD	Your Aura password
GROQ_API_KEY	Free at console.groq.com

After adding secrets: click "Seed fraud graph" in the sidebar to populate Neo4j.

Without credentials the app runs in demo mode with realistic simulated responses.

---

Related projects

• IBM Safer Payments — AUC-ROC 0.9591
• HetGNN Fraud Graph Explorer
• Agentic RAG Pipeline on Kubernetes

---

Built with Neo4j Aura · Groq · Llama 3.1 · Streamlit · PyVis · Plotly