Financial crime rarely happens in isolation. Fraud rings, laundering networks, mule accounts, and hidden ownership structures are designed to appear legitimate at the individual transaction level, while remaining invisible at the network level. When detection systems evaluate payments one at a time, coordinated risk stays hidden — leading to delayed detection, higher losses, and missed investigative signals.

A Transaction Graph addresses these challenges by adding relationship-aware intelligence to your existing fraud detection stack and connecting accounts, customers, devices, and entities in real time. As a result, organizations can uncover hidden patterns, detect coordinated fraud earlier, and accelerate investigations. In this 30-minute webinar, we’ll show you how to integrate the Neo4j Graph Intelligence Platform into modern financial crime architectures to deliver measurable impact — without a rip-and-replace transformation.

We’ll cover:

• Why evaluating transactions in isolation misses coordinated fraud and increases the risk of false positives
• How graph intelligence enhances rules engines and machine learning models
• How a Transaction Graph enables investigators to trace fund flows and uncover Ultimate Beneficial Ownership (UBO)