Curve, a UK-based financial super-app, faces challenges with high-volume financial crime. Traditional fraud detection struggles with organized fraud rings that exhibit complex interconnections. To address this, Curve partnered with Google Cloud to implement BigQuery Graph for deep network analysis. Identifying fraud requires understanding multi-hop relationships, which are computationally expensive and difficult to scale with standard SQL. Curve transitioned its network analysis to BigQuery Graph, leveraging its native Graph Query Language (GQL) support. This allowed them to keep data within their existing BigQuery environment, saving time and costs. By modeling their payment ecosystem as a property graph, Curve simplified architecture and uses intuitive GQL for pattern matching. They can now traverse billions of connections efficiently and unify data analysis with standard SQL and machine learning workflows. This integration has led to significant financial impact, with estimated savings of $12 million in transaction losses for 2025. Graph-powered queries achieve approximately 72% accuracy in identifying fraudulent users, allowing fraud agents to focus on high-certainty cases. The adoption of GQL has streamlined fraud rules and enabled faster traversal for machine learning models. Curve is now exploring real-time detection loops and native graph visualization to further enhance its fraud mitigation strategy.