AI Fraud Detection & Prevention

Overview

A real-time fraud detection layer for payments, e-commerce, fintech, marketplace platforms, and any business processing meaningful transaction volume. The system scores every transaction (or auth, login, account creation, withdrawal) within milliseconds, combining 18+ signal types — device fingerprint, IP reputation, behavioral patterns, velocity rules, geo mismatches, BIN intelligence, graph signals (mule account ring detection), and historical patterns specific to your business. Confirmed fraud and false positives both feed back into the model continuously, so detection accuracy improves over time without manual rule updates.

The Challenge

Real fraud is adversarial — attackers adapt every time a rule changes. A static rules engine ages out in months. A model that's not explainable creates compliance headaches and CX nightmares ('why was my legitimate transaction blocked? I need a reason.'). False positives have to stay low (every blocked good transaction is a CX hit and lost revenue). And the system has to run at production latency — single-digit milliseconds per scoring decision — at peak volume without dropping requests.

Our Approach

We build a layered detection stack: a fast feature-extraction layer computes device, velocity, geo, and behavioral features per event. A gradient-boosted model (or ensemble) produces the primary score. A separate graph model identifies coordinated patterns (card-testing rings, mule networks, device-reuse clusters) that single-event models miss. Every block ships with the top contributing signals, in plain language, so CX can explain it and compliance can audit it. Decision latency is sub-50ms p99 at the volumes we've shipped. Confirmed-fraud and false-positive feedback continuously retrains the model. Integration is via REST API or Kafka consumer.

Key Features

• Sub-50ms scoring at production volumes (p99)
• 18+ signal types: device, velocity, geo, behavior, BIN, graph
• Graph-based detection of coordinated patterns (rings, mules)
• Plain-language explanation per block for CX and compliance
• Continuous learning from confirmed-fraud and false-positive feedback
• Configurable risk thresholds per transaction type
• Real-time alerts on detector pattern shifts
• Integration via REST API or streaming (Kafka, Kinesis)

Results

Try It Yourself