Fraud doesn’t happen in isolation. Users, devices, IP addresses, and physical locations are connected in ways that can reveal suspicious behavior and coordinated activity. But traditional fraud signals often miss these connections. With billions of precise location observations and a proprietary Location Intelligence Graph, Radar can uncover patterns and relationships that individual signals can’t reveal, adding a new dimension of intelligence to existing fraud stacks.
Join Radar CEO, Nick Patrick for an introduction to Radar’s Location Intelligence Graph, which connects users, devices, IP addresses, and physical locations to reveal fraud patterns traditional tools can miss. See how this connected intelligence helps identify coordinated fraud, uncover suspicious concentrations of activity, and power RF-1, Radar’s new machine-learning risk model that uses graph-powered signals to deliver explainable risk scores for more confident fraud decisions.
We’ll also explore how Radar helps teams identify and investigate fraud hotspots, uncovering coordinated activity across seemingly unrelated accounts and devices and demonstrating how precise location intelligence can strengthen existing fraud detection strategies.
What you’ll learn:
- How Radar’s Location Intelligence Graph uses billions of precise location observations to create network effects that strengthen fraud detection and risk assessment
- How connecting users, devices, IP addresses, and physical locations reveals suspicious relationships and coordinated activity
- How Radar identifies fraud hotspots to uncover suspicious clusters, device farms, and multi-account abuse
- How RF-1 builds on the Location Intelligence Graph to deliver explainable, machine-learning risk scores that help teams make more confident fraud decisions
