TransUnion Strengthens Device Risk with ML, Boosts Fraud Capture by 50%
TransUnion has enhanced its Device Risk solution with advanced machine learning models that improve fraud capture by up to 50% and reduce rule-maintenance overhead. The platform leverages consortium-driven insights and thousands of device signals to detect non-human activity, helping businesses mitigate the $534 billion in global fraud losses.
1. Expanded Machine Learning Capabilities
TransUnion has integrated adaptive machine learning into its Device Risk solution, leveraging thousands of device signals to detect non-human activity and recognize returning devices. Pre-built models powered by a global fraud consortium improve detection accuracy by up to 50% and reduce manual rule maintenance.
2. Global Fraud Loss Trends
A survey of 1,200 business leaders reported $534 billion in suspected digital fraud losses, with organizations losing an average of 7.7% of annual revenue. TransUnion’s internal analysis also identified a 141% increase in digital account takeovers and a 26% rise in fraud during account creation over the last year.
3. Business Impact and Outlook
These enhancements aim to streamline digital customer experiences by reducing unnecessary friction, lowering operational overhead, and strengthening security across login, transaction and account creation processes. Businesses can expect more precise fraud protection and improved efficiency as privacy-driven technology changes challenge traditional device fingerprinting.