For over a decade, a crucial part of fraud detection has been assigning an identity to every laptop, tablet, and mobile device that accesses a website or app. Such a fingerprint is a representation of hundreds of different device-specific values taken from an end user's device. Like in the real world, a device fingerprint aids in identification and tracking of bad actors.
Fraudsters' devices often share patterns in their set of signals. With the help of machine learning, device signal datasets render a fraud score that tells a story about the device and the user behind it.
Download this whitepaper to understand:
- The current state of device fingerprinting;
- Where it is headed with more effective and advanced techniques;
- How advanced fingerprinting technologies recognize pattern shifts and detect fraud while so the fraud model automatically adjusts.