European digital bank achieves 38% better fraud detection accuracy with TigerGraph graph analytics and Vertex AI

This European digital-first bank was ingesting over 2 TB of application and transaction data spanning devices, IP addresses, merchants, and cardholders. The core constraint was that traditional relational databases and commercial graph solutions could not process this volume fast enough to meet sub-second detection requirements. The existing system produced delayed fraud scores with no explainability.

Crunch-IS built a graph-based fraud detection ecosystem on Google Cloud Platform. A TigerGraph graph schema connected users, cards, IP addresses, devices, physical addresses, and merchants, enabling multi-hop traversal queries that surface indirect relationships. Graph-derived structural features — including shared connection counts and abnormal link patterns — were fed into an XGBoost gradient boosting model trained and continuously retrained on Vertex AI, optimizing for high precision to minimize false positives. The solution integrated with the bank's existing Java-based services and Kafka transaction streams: new transactions triggered live graph updates, real-time feature extraction, and risk scoring within a 250ms end-to-end pipeline. Detection results persisted in PostgreSQL and drove automated card holds, fraud team alerts, and a React-based compliance dashboard with graph visualizations for auditable case investigation. Infrastructure was provisioned via Terraform on Cloud Run.

Detection accuracy improved by 38%, enabling the bank to intervene earlier. Graph traversal time dropped from minutes to milliseconds, compressing the total fraud check pipeline to under 250ms — a 5x improvement over the prior system. Applied retrospectively to historical data, the system identified over 3,200 previously undetected fraud ring patterns.

• 38% improvement in fraud detection accuracy
• <250ms end-to-end pipeline latency (5x faster)
• 3,200+ previously undetected fraud ring patterns identified in historical data
• Near real-time card blocking with traceable graph paths for compliance reporting

• Graph databases become necessary when fraud relies on multi-hop entity connections — relational systems cannot traverse these relationships at production speed or scale.
• Graph-derived structural features can be combined with gradient boosting models (XGBoost + Vertex AI) for fraud scoring.
• In regulated banking, explainability is not optional: compliance teams require auditable graph paths, not just risk scores.
• Integrating with existing Kafka and Java infrastructure reduces deployment friction and enables rollout without a full system replacement.
• Applying the model retrospectively to historical data can surface pre-existing fraud exposure.