Detect Fraud Faster With a Transaction Graph

Modern financial crime, characterized by sophisticated fraud rings and synthetic identities, is increasingly challenging to detect due to its evasive nature and the limitations of traditional detection tools, which often evaluate transactions in isolation. Relational databases, typically used in fraud detection, fragment data and fail to capture the interconnectedness of transactions across accounts, devices, and merchants, resulting in high false positive rates and inefficiencies. A graph database approach, exemplified by iuvity's implementation of a transaction graph, offers a solution by storing relationships between entities, providing a connected view of transactions, and enabling faster, more accurate detection of coordinated fraud. This method improves detection by surfacing hidden contexts and relationships in real-time, doubling detection rates without increasing false positives while keeping legitimate user friction low. The flexibility of a graph model allows for seamless adaptation and scaling, ensuring fraud detection systems can evolve with emerging threats and maintain robust performance across various financial crime scenarios.