Product

ID Fraud Score

More accurately identify fraud risks on new and existing accounts

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Rely on precision analytics to open and grow consumer relationships with confidence

Fraud is shifting—from transaction fraud to account-level fraud, and spreading to organizations that haven’t historically experienced significant fraud threats. Without substantial investments in new processes or systems, organizations can use TransUnion identity models and analytics to more accurately identify fraud risks at the time of acquisition and in existing customer portfolios.

Synthetic fraud model

Synthetic fraud—using fabricated identities for the purpose of fraud—is one of the fastest-growing and costliest threats, impacting everything from cards to auto lending to wireless carriers. Through analysis of a consumer’s behavior and credit file, this model can identify new and existing accounts that may not be exactly what you think they are.

Fraudulent Default Model

For years, card issuers have struggled to identify cards opened with the intent of running up high balances and charging off. Now, by bringing together powerful data, card issuers have access to a model that identifies accounts at risk of charging off from this type of fraudulent behavior.

Account opening fraud score

With unprecedented amounts of stolen data available, organizations need better tools to detect stolen identities being used to open accounts. The TransUnion account opening fraud score detects applications showing signs of an identity that is being used fraudulently to open an account.

Identity Alerts

As identity fraud becomes increasingly sophisticated, new insights are needed to detect fraud. Identity Alerts analyze the behavior and use of an identity across industries to alert organizations when applications show signs of identity fraud.

PRODUCT HIGHLIGHTS
  • Delivered real time or in batch to support a variety of uses and operating environments

  • Designed for quick action and precision identification of specific fraud threats

  • Complements many existing decision systems, to provide protection as fraud changes without the expense of costly system changes

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