TransUnion Strengthens Device Security with New Machine Learning Capabilities

Suspected digital fraud continues to impact businesses worldwide. In a recent TransUnion (NYSE: TRU) survey of 1,200 business leaders , respondents reported fraud losses totaling $534 billion. To help companies combat this growing threat, TransUnion today announced expanded machine learning (ML) capabilities within its Device Risk solution.

The enhancements are designed to help organizations detect and combat increasingly sophisticated attacks, while maintaining a streamlined and trusted customer experience. Today's announcement comes at the Merchant Risk Council's MRC 2026 conference in Las Vegas, where TransUnion will be exhibiting its fraud solutions at Booth 422.

To help businesses stay ahead of emerging threats, TransUnion Device Risk has been further powered to enable:

· Stronger recognition of returning devices across customers

· More robust detection of non-human activity (including behavior patterns associated with virtual machines, residential proxies and remote desktops)

· Deeper consortium-driven insights that illuminate evolving fraud trends

These updates enhance fraud-detection accuracy and streamline digital customer experiences by reducing unnecessary friction. The new capabilities introduce advanced machine learning that extends Device Risk intelligence far beyond traditional, static rule-based decisioning.

Pre-built adaptive ML models learn from thousands of device signals and fraud feedback sourced from TransUnion's long-standing global fraud consortium. This enables proactive detection of anomalies and evasion attempts. ML has demonstrated the ability to improve fraud capture by up to 50%, while also reducing the volume and complexity of manually maintained rules, lowering operational overhead, and improving overall precision.