Identify instances of fraud before they impact earnings

TransUnion’s customer was facing significant losses from consumers who had charged off on loans or credit card accounts. Despite passing standard fraud and identity verification processes, the organization suspected fraudulent activity—including a potential fraud ring—and reached out to TransUnion. To help uncover any underlying issues, TransUnion conducted a thorough investigation using its TruValidate suite, particularly the Synthetic Fraud Model, which analyzes consumer behaviors by discovering anomalies or suspect patterns, and TLOxp^® which consolidates 10,000+ data sources into a simple, powerful investigative tool.

TransUnion was provided a customer sample to investigate for synthetic identities—fraudulent identities comprised of fabricated data elements or a compilation of multiple, real identity elements. The Synthetic Fraud Model discovered 80% of the examples returned a high fraud risk score. 

Further examination with tools like TLOxp found flagged examples had other characteristics of synthetic fraud, such as:

• Some identities were shown to be linked to addresses associated with hundreds of additional unique identities
• One identity had an authorized user who also had an exceedingly high number of other seemingly unrelated authorized users
• Multiple consumers utilizing the same name and DOB but had different SSNs
• SSN used in one identity could not be verified and may possibly be issued to a minor.

As a result of this analysis, the customer implemented TransUnion TruValidate Synthetic Fraud Model into its fraud and identity strategies to help proactively detect synthetic identities at new account opening.

TransUnion’s customer implemented the TruValidate Synthetic Fraud Model on all its credit reports as an additional layer of protection. Consequently, the organization was immediately able to identify synthetic fraud during the application process, rather than waiting until after the account was booked and losses piled up.

When synthetic fraud is identified during the origination process, the customer requests additional information from consumers including “proof of life,” such as multiple utility bills or a SSN card. This helps drive synthetic fraudsters away from the application process because they’re unable to provide the requested information. By identifying fraud cases with precision, the organization can maintain a positive experience for their real customers. They can also protect themselves by introducing additional friction only to the cases where it’s needed most.

Today, TransUnion conducts additional analyses to identify fraud within the customer’s existing portfolio. This will help isolate likely synthetic identities and enable appropriate actions, such as removing these customers from cross-sell or upgrade offers, to limit losses.

TransUnion TruValidate Synthetic Fraud Model provides deep insights to help:

• Detect synthetic identities at the point of origination–before approval decisions are made
• Monitor your existing portfolio to isolate accounts with synthetic identities and act before a fraudster “cashes out”
• Ensure compliance to protect yourself from future risks by truly knowing your customer
• Maximize return on investments by preventing wasted marketing budget on synthetic identities and allocating those funds to viable prospects