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Fraud Signal Extraction

Detect fraud signals in real-time from transaction descriptions, user messages, and behavioral patterns. Semantic understanding catches patterns that rules miss.

Fits auth window — 18ms vs 100-500ms for enterprise fraud platforms. Fits inside the 50ms authorization budget
Catches what rules miss — Semantic patterns like gift card splitting and P2P velocity that regex can't express
Minutes not vendor cycles — New fraud vector? LEAP fine-tune in 30 minutes, not a vendor upgrade cycle

The Problem

Rule engines maintain thousands of regex patterns. Enterprise platforms (Featurespace, Feedzai) cost $500K-2M/yr and add 100-500ms. Both miss novel attack vectors requiring semantic understanding.

How LFM Compares

Rule engines miss novel attack vectors. Enterprise fraud platforms add 100-500ms of latency. LFM extracts semantic fraud signals within the 50ms auth window — catching patterns rules cannot.

What LFM Unlocks

Semantic fraud signal extraction within the 50ms auth window. Catches gift card splitting, P2P velocity anomalies, synthetic identity indicators.

Fraud Signal Extraction

Semantic fraud detection within the payment authorization window

Payment Authorization Pipeline

Real fraud detection uses enriched context: account history, device fingerprints, and transaction velocity — not just the merchant descriptor. Each example below includes the full signal context a payment processor would have at authorization time. LFM classifies the combined context in under 50ms.

Select a transaction to analyze:

This demo is fine-tuned on sample data. Results improve with your data.

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