100% Open Source · MIT License · 180 Tests Passing

Detect Fraud in <10ms

Open-source fraud detection platform for fintech, e-commerce, and payments. 3-layer ensemble: Rule Engine + XGBoost + Graph Analytics. Self-host 100% free or use our Cloud API.

Get Free API Key Try Live Demo ⭐ GitHub

<10ms

Detection Latency

99.9%

API Uptime

ML Features

Fraud Rules

Everything you need to stop fraud

Production-grade from day one. No PhD required.

Real-Time Detection

Score transactions in under 10ms using our ML ensemble. Never block legitimate customers.

ML Ensemble

XGBoost + IsolationForest + 9 rule engine working together. SHAP explanations for every decision.

Graph Analytics

Detect fraud rings using NetworkX graph engine. Catch shared device and IP patterns instantly.

Pathway Streaming

Built on Pathway (Rust engine). Handles 100k+ transactions/second in real-time.

Security First

PII hashing, API key auth, rate limiting, SQL injection prevention. GDPR-aware design.

Analyst Dashboard

Built-in review queue, fraud ring visualization, and analytics. No extra tools needed.

Try it live

Real API call. Real ML model. See results in milliseconds.

Choose a scenario:

Select a scenario and click Analyze

Integrate in 5 minutes

One API call. JSON in. Decision out.

Get your API key

Send a transaction

POST transaction data to our API. Takes 2 minutes to integrate.

Get instant decision

ALLOW, REVIEW, or BLOCK with explanation. In under 10ms.

Monitor & improve

Use our dashboard to review alerts and track fraud patterns.

example.py

import requests

response = requests.post(
  "https://fraudshield-api.onrender.com/api/v2/transactions/analyze",
  headers={"X-API-Key": "your-api-key"},
  json={
    "user_id": "USER_001",
    "amount": 299.99,
    "currency": "USD",
    "merchant_id": "SHOP_001",
    "merchant_category": "electronics",
    "location": "US",
    "device_id": "DEVICE_001",
    "ip_address": "192.168.1.1",
    "channel": "online"
  }
)

result = response.json()
print(result["decision"])     # ALLOW / REVIEW / BLOCK
print(result["score"])        # 0.0 - 1.0
print(result["reasons"])      # Why flagged
print(result["latency_ms"])   # < 10ms