Real-time graph intelligence that catches fraud rings before they disappear.

Financial fraud hides in networks, not single transactions. FraudNet·AI makes those patterns visible the moment they happen — streaming live transactions into a 3D force-directed graph, running five parallel detection algorithms, and letting IBM Granite 3 AI explain every finding to investigators in plain language.

Built in 36 hours at GenAI Genesis 2026 — Canada's largest AI hackathon.

Live Demo → · DevPost →

Note on IBM integrations: Both IBM watsonx.ai and IBM Db2 are fully implemented in the codebase (backend/watsonx_client.py, backend/db2_client.py). They are currently disabled because the IBM Cloud trial tokens were exhausted and the Db2 trial instance expired. The app runs entirely on pre-cached Granite 3 responses and SQLite — no functionality is lost for the demo.

Transactions stream over WebSocket into a live 3D WebGL graph. Every 30 edges, the fraud detection engine re-runs and emits alerts as patterns emerge. Click any alert to get a full intelligence report — classification, evidence, regulatory flags, and recommended actions — powered by pre-cached IBM Granite 3 responses (watsonx.ai link broken due to insufficient tokens on IBM Cloud trial).

Composite risk score: 30% cycle involvement + 25% community isolation + 20% PageRank anomaly + 15% temporal burst + 10% neighbour propagation (2 passes)

Risk levels: clean (0–20) · watch (21–50) · suspicious (51–75) · fraud (76–100)

Backend

cd backend
python3 -m venv venv && source venv/bin/activate
pip install -r requirements.txt
python3 -m uvicorn main:app --reload --port 8000

Frontend

cd frontend/fraudnet-ai
npm install
npm run dev

Open http://localhost:3000. No data generation step needed — the simulation is configured in the browser.

All optional. The app runs fully without them.

# IBM Db2
DB2_HOSTNAME=xxx.databases.appdomain.cloud
DB2_PORT=50001
DB2_DATABASE=BLUDB
DB2_USERNAME=xxx
DB2_PASSWORD=xxx
DB2_SSL=true

# IBM watsonx.ai
WATSONX_API_KEY=your_ibm_cloud_api_key
WATSONX_PROJECT_ID=your_watsonx_project_id
WATSONX_URL=https://ca-tor.ml.cloud.ibm.com

NEXT_PUBLIC_API_URL=http://localhost:8000

Fallbacks:

• No watsonx.ai credentials → AI explanations use pre-cached Granite responses in watsonx_cache.json
• No Db2 credentials or expired instance → falls back to SQLite (data-gen/transactions.db) silently

watsonx.ai setup:

1. Create a project at dataplatform.cloud.ibm.com/wx/home
2. Associate a Watson Machine Learning service: project → Manage → Services & integrations
3. Copy the Project ID from: project → Manage → General

backend/db2_client.py connects to IBM Cloud Db2 with automatic SQLite fallback — no code changes needed when switching between them.

Schema:

fraudnet_accounts      (account_id, account_name, account_type, created_at)
fraudnet_transactions  (tx_id, from_account, to_account, amount, tx_timestamp, tx_type, is_fraud)

Load flow:

1. On startup, attempt Db2 connection using .env credentials
2. If connected: create schema, migrate SQLite data on first run, serve all transactions from Db2
3. If unavailable: fall back to SQLite with a log message — no errors, no config changes

Runtime API:

• POST /db2/load — reload data and broadcast demo_reset to all WebSocket clients
• GET /db2/status — connection state and row counts (shown via the IBM Db2 button in the header)

The trial Db2 instance used during development has expired. The SQLite fallback (data-gen/transactions.db, included in the repo) handles everything automatically. During development, Db2 stored 5,000 transactions and 500 accounts on IBM Cloud (ca-tor region).

Click Reconfigure in the dashboard to open the simulation configurator:

Presets: Quick · Default · Heavy · Chaos

Normal transactions compress into ~5 seconds. Fraud transactions replay at 50ms each so detection emerges dramatically at the end.

GET  /graph            — full graph (nodes + edges + risk scores)
GET  /alerts           — all detected fraud alerts
GET  /alerts/{id}      — single alert with embedded subgraph
POST /analyze          — AI fraud explanation via watsonx.ai
POST /demo/start       — reset engine with new in-memory simulation
POST /db2/load         — reload from Db2/SQLite, broadcast reset to all clients
GET  /db2/status       — Db2 connection status and row counts
GET  /stats            — dashboard stats (transactions, accounts, fraud rings, uptime)
WS   /ws/stream        — real-time transaction stream

Configured via nixpacks.toml and railway.toml.

• Build: Python 3.11 via Nix, pip install -r backend/requirements.txt
• Start: python -m uvicorn backend.main:app --host 0.0.0.0 --port $PORT
• Health check: GET /stats · Restart policy: on_failure

Add IBM credentials in the Railway dashboard to connect to live Db2 or watsonx.ai.

Deploy the frontend/fraudnet-ai directory with framework preset: Next.js.

Set one environment variable:

NEXT_PUBLIC_API_URL=https://your-railway-backend.up.railway.app

fraudnet-ai/
├── backend/
│   ├── main.py              # FastAPI app — REST + WebSocket
│   ├── graph_engine.py      # NetworkX fraud detection + risk scoring
│   ├── db2_client.py        # IBM Db2 client with SQLite fallback
│   ├── demo_generator.py    # In-memory synthetic fraud data generator
│   ├── watsonx_client.py    # IBM Granite 3 AI explanations
│   ├── watsonx_cache.json   # Pre-cached AI responses (fallback)
│   └── requirements.txt
├── frontend/fraudnet-ai/
│   ├── app/
│   │   ├── page.tsx                  # Main dashboard
│   │   └── components/
│   │       ├── GraphView.tsx          # 3D WebGL force graph
│   │       ├── AlertFeed.tsx          # Real-time alert list
│   │       ├── AIExplanation.tsx      # Granite AI analysis panel
│   │       ├── StatsBar.tsx           # Header + Db2 status
│   │       ├── LaunchScreen.tsx       # Simulation configurator
│   │       ├── DemoModal.tsx          # Reconfigure modal
│   │       ├── Db2StatusButton.tsx    # Db2 connection indicator
│   │       └── useWebSocket.ts        # WebSocket state + reconnect
├── data-gen/
│   ├── transactions.db      # SQLite fallback (included)
│   ├── transactions.json    # JSON export for simulator
│   └── generate.py          # Offline data generation script
├── nixpacks.toml            # Railway build config
└── railway.toml             # Railway deploy config

MIT License — Copyright (c) 2025 Ajaypartap Singh Maan

See LICENSE for the full text.