TransUnion has introduced a major advancement to its Device Risk solution, delivering next-level capabilities that redefine how businesses combat fraud. This breakthrough empowers organizations to identify risky devices with unprecedented precision, uncover hidden anomalies, and dynamically optimize fraud strategies in real time--hel helping them stay ahead of increasingly sophisticated threats. The enhancements come as fraud continues to escalate at an alarming pace, costing businesses an average of 7.7% of annual revenue--totaling $534 billion.
As fraudsters deploy increasingly sophisticated tactics, businesses need advanced tools to protect their customers and revenue. Devices with risky attributes, suspicious histories or questionable associations often drive fraud losses. Financial institutions including lenders, retail banks, fintechs, and others, struggle to identify new or familiar devices that match known fraud patterns, making early detection a persistent challenge.
Key enhancements to Device Risk include: Cross-session device identification: Recognize and track devices across multiple sessions and platforms without relying on cookies, ensuring consistent identification even as privacy regulations evolve. This approach reduces dependence on cookies while maintaining strong compliance with privacy standards and delivering reliable device recognition for fraud prevention. Adaptive Machine Learning (ML): Leverage advanced ML models and dynamic rule strategies that deliver significant performance improvements, boosting fraud detection rates by up to 50% compared to static device recognition alone.
These models continuously adapt to evolving fraud patterns and incorporate feedback from confirmed fraud cases, ensuring defenses remain agile and effective over time. Advanced Anomaly and Evasion Detection: Detect and flag virtual environments, remote access tools, and automated bot activity while strengthening resistance to user manipulation techniques. By making it harder for fraudsters to bypass detection, this capability helps organizations proactively block suspicious behaviors and maintain trust in digital interactions.
Device Risk analyzes thousands of device attributes and behavioral signals in real time to generate a unique device fingerprint. It evaluates key risk indicators, including device integrity, behavioral patterns and environmental context. By combining these insights with adaptive machine learning, Device Risk continuously refines risk scoring and fraud detection strategies.
Businesses can integrate the solution into existing workflows via APIs, enabling instant decisions and seamless customer experiences. When used in conjunction with TransUnion's IP Intelligence, the authoritative source of IP decisioning data on 99.99% of IP addresses worldwide, customers can even further reduce potential risk for each transaction.
