AI Fraud Detection & Prevention

Overview

An e-commerce marketplace processing $20M/month was getting hit from both sides: fraudsters exploiting the platform for $300K/month in losses, while the crude rule-based fraud filter was blocking 12% of legitimate orders ($2.4M/month in lost revenue). We replaced the rule engine with an AI fraud detection system that evaluates 200+ signals per transaction in real-time, dramatically reducing both fraud losses and false declines.

The Challenge

Fraud evolves constantly — as soon as you block one pattern, fraudsters adapt. The system needed to detect novel fraud patterns without being explicitly programmed for them. It also needed to operate in real-time (decisions in under 200ms to not delay checkout), handle the extreme class imbalance problem (99%+ of transactions are legitimate), and minimize false positives that cost more in lost revenue than the fraud they prevent.

Our Approach

We built a multi-model system. A real-time scoring model evaluates every transaction using device fingerprinting, behavioral biometrics, velocity patterns, graph analysis of connected entities, and transaction characteristics. Transactions are scored 0-100; low-risk approves instantly, high-risk declines, and a middle band routes to manual review. A separate model continuously learns from fraud confirmations, adapting to new patterns within hours. A/B testing ensures model updates don't increase false declines.

Key Features

• Real-time fraud scoring in <200ms
• 200+ signal evaluation per transaction
• Device fingerprinting and behavioral biometrics
• Graph-based entity linking for organized fraud
• Self-learning from confirmed fraud cases
• Manual review queue for borderline cases
• False decline monitoring and optimization

Results

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