Business Impact
- $18M annualized loss avoidance.
- 99.7% precision / 96% recall on high‑risk segment.
- Sub‑120ms p95 scoring latency.
Challenge
Batch oriented fraud rules produced high false positives and lagged novel attack vectors. Manual review teams were overloaded and fraud rings exploited cross‑merchant blind spots.
Solution
- Unified streaming ingestion of transactions, device telemetry and behavioral events.
- Graph feature generation (entity linkage, community risk factors).
- Hybrid model ensemble (gradient boosting + graph embeddings + rule layer).
- Adaptive learning pipeline retraining on confirmed fraud / legitimate feedback.
- Analyst console with explainability (feature contributions, graph context).
Architecture Highlights
- Feature store with TTL & versioned transformations.
- Event streaming backbone + schema registry.
- Shadow deployment & canary promotion for new models.
- Model explainability service returning SHAP values.
Outcomes
Reduced false positive review queue by 44% while improving fraud capture; continuous deployment pipeline cut model iteration time from weeks to days.