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

Digital-first neo banks operate at massive transaction scale with thin fraud operations margins — every undetected event carries both financial loss and regulatory exposure. This European bank was ingesting over 2 TB of application and transaction data spanning millions of interactions across devices, IP addresses, merchants, and cardholders. The core constraint was architectural: traditional relational databases could not traverse the multi-hop entity relationships that modern fraud rings exploit, and commercial graph solutions failed to meet sub-second detection requirements at this data volume. The existing system produced delayed fraud scores with no explainability, leaving compliance teams unable to audit decisions or intervene before cardholders were harmed.

Crunch-IS built a four-layer 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 invisible to tabular data models. 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 natively with the bank's existing Java Spring Boot 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 in the fraud lifecycle before cardholder losses accumulate. 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, exposing organized schemes the relational system had missed entirely.

• 38% improvement in fraud detection accuracy
• <250ms end-to-end pipeline latency (5x faster)
• 3,200+ hidden fraud rings uncovered 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.
• Combining graph-derived structural features with gradient boosting models (XGBoost + Vertex AI) yields stronger fraud signal than either approach alone.
• 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 surfaces pre-existing fraud exposure immediately, delivering ROI before any new fraud occurs.