Communications and Digital Media

Synthetic ID Fraud Checklist

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Copyright © 2018, Equifax Inc., Atlanta, Georgia. All rights reserved. Equifax and EFX are registered trademarks of Equifax Inc. 18-101178 CONTACT US For more information: cdm_inquiries@equifax.com equifax.com/cdm Stop synthetic ID fraud before it starts. Equifax can help. From unique, differentiated third-party data and advanced analytics to intelligent keying and linking technology, we have the proprietary solutions and deep expertise you need to better protect your service provider business — and your bottom line — against synthetic ID fraud. Pay attention to authorized users. An authorized user is granted access to another person's credit card account. They receive full access to the account's credit line, but they're not legally responsible for paying the balance or associated fees resulting from their use of the account. The practice of adding authorized users may not be illegal, but it can lead to authorized user abuse. Sometimes called credit boosting or piggybacking, authorized user abuse occurs when low-risk primary account owners "rent" their tradelines with extensive credit histories, high credit limits and solid repayment profiles to others — most times, knowingly, to fraudsters. The synthetic scheme looks very similar to the appropriate use — having analytics in place to detect it is critical. Automatically spot SSN red flags with robust proprietary algorithms. By comparing an SSN to a consumer's unique identification information, a sophisticated real-time algorithm can determine how well a consumer's SSN matches its identity. The most useful tools return both positive confirmations of an SSN match and several negative alerts that can signal the creation of a synthetic identity at the time of account activation, before any damage is done. Look for these warning signs: ■ SSN can't be matched to the specific consumer based on comparison algorithms ■ SSN matches to a different consumer, while no credit file is available for the requested applicant ■ SSN matches to a different consumer, and a credit file is available for the name and address provided; however, the SSN on that file is different from the SSN provided on the inquiry Discover suspicious patterns with advanced analytics. Analytics-based solutions can detect linkages and suspicious patterns, which help determine if the applicant is a real person. These models leverage advanced keying logic to validate components of an applicant's identity beyond an SSN. They can also provide information to help reveal inconsistencies with the applicant's behavior across a consortium of data or if the application has high-risk variables that are known to be predictive of fraud. 4 5 6

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