Intelligent Device Fingerprint Technology
THAT LETS YOU STOP FRAUD AT ITS ROOT
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- Identify unique devices–desktop or mobile–by their fingerprints.
- Use device risk scores–computed using proprietary algorithms–to determine fraud probability for any device, even one that has never been blacklisted before.
- Determine whether two different device fingerprints–highlighted by clustering algorithms–are likely to belong to the same fraudster.
- Get complete access to the device attributes for use in your custom fraud models.
HOW IT WORKS
Device Recon analyzes hundreds of mobile and desktop device characteristics and behaviors–including browsers, language, location, operating system, even mobile emulation, and battery level–to fingerprint devices. Fraudsters can mask identifying properties like their username, email, and IP address, but with Device Recon you can determine if a device is associated with fraud. If a fraudster returns to your site or application and makes minor changes to device characteristics and behaviors, traditional fingerprinting technology will not detect it’s the same device, and the fraudster will slip through your blacklist defense. But Device Recon uses machine learning models that incorporate risk scoring and clustering to see through the fraudsters’ techniques–giving you triple protection against their attacks.
Our powerful machine-learning models determine the riskiness of every device, even if it has never appeared on the network before or is not on a blacklist.
To calculate the probability that a particular device is being used by a fraudster, we deploy our proprietary algorithms against an ever-growing knowledge base of external data points, such as IP belonging to a Tor network or likelihood of a browser version being used in a particular country.
A device fingerprint captures the state of a device at a given point in time. But device characteristics–even those of legitimate users–naturally change over time, such as when a plugin is installed or operating system is upgraded.
So Device Recon deploys a second machine learning model that uses statistical clustering to distinguish between major and minor device changes, which enables it to determine whether two device fingerprints actually belong to the same device.