Graph database

A graph database is made up of three core components: nodes (representing entities such as people or accounts), edges (the connections or relationships between nodes), and properties (attributes that describe nodes or edges). These parts allow for fast, complex querying of relationship-based data without the overhead of joins in relational databases.

Graph databases are widely used in applications where understanding the relationships between data is crucial. This includes fraud detection, where linked entities need to be mapped and analyzed in real time, as well as supply chain visibility, social media analytics, identity resolution, and recommendation systems. Their ability to surface patterns in relationships makes them powerful tools for decision-making.

In fraud prevention, graph databases can model networks of users, transactions, and devices to detect suspicious patterns. For example, if multiple users share the same IP address, payment method, or device ID, a graph can instantly highlight these connections. This enables companies to identify fraud rings, detect synthetic identities, or prevent account takeovers by flagging unusual relationships and behaviors that may go unnoticed in traditional systems.