Suspicions of digital fraud continue to impact businesses around the world. In a recent TransUnion survey of 1,200 business leaders, respondents reported total fraud losses of $534 billion. To help businesses combat this growing threat, TransUnion today announced expansions to machine learning (ML) capabilities within its Device Risk solution.
This enhancement is designed to help organizations detect and respond to increasingly sophisticated attacks while maintaining a streamlined and reliable customer experience. Today’s announcement is being made at the Merchant Risk Council’s MRC 2026 conference in Las Vegas, where TransUnion will be exhibiting its fraud solutions in booth 422.
To help businesses stay ahead of emerging threats, TransUnion Device Risk has been further enhanced to:
· Increased awareness of device returns across customers
· More robust detection of non-human activity (including behavioral patterns related to virtual machines, residential proxies, and remote desktops)
· Deeper consortium-driven insights to uncover evolving fraud trends
These updates improve the accuracy of fraud detection and streamline the digital customer experience by reducing unnecessary friction. New capabilities introduce advanced machine learning that extends device risk intelligence far beyond traditional static, rules-based decision-making.
Pre-built adaptive ML models learn from thousands of device signals and fraud feedback from TransUnion’s long-standing global fraud consortium. This allows for proactive detection of anomalies and evasion attempts. ML has demonstrated the ability to improve fraud capture by up to 50%, while reducing the amount and complexity of manually maintained rules, reducing operational overhead, and improving overall accuracy.
