TruValidate Device Risk

Device intelligence that continuously adapts to reduce fraud risk, stay ahead of changing fraud tactics and enable more seamless customer experiences.

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Distinguish between suspicious transactions and trusted digital interactions using resilient device recognition, advanced anomaly detection and predictive AI-driven risk scoring that learns and adjusts as fraud evolves.

Reduce fraud in real time

Identify devices with risky attributes, associations or histories before they engage with your organization.

Quickly build trusted connections

Reduce friction for legitimate customers and safeguard against fraud with trusted device to account linkages.

Adapt to evolving fraud threats

Detect and mitigate sophisticated evasion techniques, device anomalies and new risky behavior using predictive machine learning.

See Device Risk in Action

Stay ahead of evolving threats by evaluating the trust of every digital interaction across the customer lifecycle. Device Risk uses advanced device intelligence, billions of signals, millions of fraud reports and predictive machine learning to determine if the device behind the interaction is being manipulated. The result? Fewer false positives, less time spent on manual reviews, sharper accuracy and smoother experiences for customers.

Product Capabilities

Extensive device fingerprinting technology

Analyses of thousands of permutations of device attributes help to identify billions of devices and assess risk in real time.

Sophisticated device intelligence consortium

Over 185 million global multi-sector confirmed fraud evidence reports help identify broader fraud patterns about known and unknown devices.

Actionable device-account linkages

Harness hundreds of signals from dozens of sources to authenticate the connection between devices and users.

Powerful, predictive risk decisioning

Leverage adaptive AI and machine learning to recognize complex patterns and non-human behavior, helping detect bots, automated attacks and fraud rings faster while improving decisioning accuracy.