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Real-Time AI Fraud Shield

Intelligent Payment Router

Overview

Runs a lightweight ML model before calling auth, analyzing profile data, amount, device fingerprint, AVS/CVV, and historical inquire results to score fraud and success probability. Routes transactions to card, ACH, or wallet and can apply dynamic surcharge or hold. Layers on top of CardPointe's basics as vertical-specific ML.

Key Metric
10-20%
Reduction in payment declines
Market Angle

Sell to high-volume merchants as 'AI Risk-as-a-Service' — reduces declines by 10–20% and cuts fraud losses. Real-time, low-latency via sandbox-tested flows.

Revenue Model

Per-transaction fee + monthly platform access

CardConnect APIs Used

POST
/auth

Authorization with fraud scoring pre-check

GET
/profile

Retrieve customer history for risk assessment

GET
/inquire

Historical transaction data for ML training

POST
/capture

Capture after fraud clearance

POST
/void

Auto-void flagged transactions

Interactive Demo

Step-by-Step API Simulation

Step 1: Ingest Transaction

Payment request arrives with device fingerprint, geolocation, and card details.

API Request
POST /levuka/ai/risk-score { "amount": "1249.99", "device": "iPhone-15-Pro", "geo": "AU", "card_bin": "411111", "velocity": 3 }
Architecture Note

This demo simulates the API flow with mock data. In production, LevukaLabs' AI layer intercepts and enriches requests before forwarding to CardConnect's Gateway at https://<site>.cardconnect.com/cardconnect/rest/

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