Financial Fraud Detector

A digital banking platform processing 50,000+ daily transactions uses rule-based fraud detection: hard thresholds, velocity checks, geographic restrictions. These catch obvious fraud but miss account takeover, synthetic identity, and coordinated rings operating below thresholds.

False positives plague the system: 200+ daily flags with only 15-20% actual fraud. Losses grow as attackers learn the rules. Feedzai processes over 8 trillion pounds annually — ML behavioral scoring dramatically outperforms rules. Featurespace detects coercion in real time. UK banks report 65% fraud detection accuracy improvement with these approaches.

Mid-market fintechs need these capabilities without dedicated data science teams.

The agent sits in the authorization path scoring every transaction in under 100ms. Gradient-boosted models trained on your history establish per-account behavioral baselines: typical amounts, merchants, geography, time patterns, device fingerprints.

Context-aware scoring: is this amount unusual for this customer but normal for this merchant? Is this location new but consistent with travel? Different device but same biometric auth? This reduces false positives compared to threshold rules.

Every score includes feature-level explanations for regulatory compliance. Network analysis detects coordinated fraud: shared fingerprints, fund flow patterns, synthetic identity clusters.