High Risk Alerts

Insights-driven, high-risk rules powered by trended credit data to spot early indicators of fraud

Identify out-of-pattern behaviors and reduce fraud losses

The financial services industry is witnessing rapid digital growth and an increased focus on customer journeys designed to reduce friction and provide a superior experience. This has sparked the need to implement enhanced checks and balances that identify potentially risky applicants early in the onboarding process to drive efficiency.

TransUnion High Risk Alerts (HRA) leverages the power of trended credit data and superlative trust solutions to identify out-of-pattern behaviors indicative of fraud. By combining advanced analytics with robust datasets, our solution helps you strike the right balance between fighting fraud and delivering exceptional user experiences. As an early warning signals (EWS) mechanism, it better enables you to proactively determine fraud and delinquencies.


Identify potentially risky customers throughout the lifecycle

Effectively screen initial applications to identify anomalies

High Risk Alerts helps identify anomalies — such as changes in contactability, variations in identity information, identity duplication and proven fraudulent behavior — that can be used to identify potentially fraudulent customers. This allows you to spot customers who potentially pose risk while minimising friction for good customers and driving more profitability.

Identification of changes in risk profile

High Risk Alerts provide predictive insights that help identify good customers, which can lead to greater customer lifetime value. By understanding changes in risk profiles, you can better predict those customers who are likely to default, and devise effective strategies to address their unique risks.


Strengthens end-to-end fraud management across the lifecycle of account

Includes behavioral attributes designed to identify any out-of-patterns trends

Leverages CreditVision® for a trended view of key indicators of stability

Helps reduce fraud losses using early warning signals

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