TransUnion Research Highlights Power of Public Data, Uncovering $3.3B Synthetic Identity Threat

​With synthetic identities now linked to a record number of newly opened accounts, U.S. lenders faced more than $3.3 billion in exposure for the year ending 2024. This alarming trend underscores the urgent need for financial institutions such as auto lenders, mortgage lenders and credit unions to harness all available data to detect and prevent synthetic identity fraud at the point of account creation. New research from TransUnion (NYSE: TRU) reveals that key traits and behavioral characteristics found in public data can play a critical role in identifying these deceptive identities before they pose a risk.
 
Synthetic identities are carefully constructed using a blend of authentic and fabricated information—often incorporating stolen Social Security numbers, fictitious names, digital contact details and behavioral patterns that mimic legitimate consumer activity. These identities are engineered to appear credible and frequently bypass traditional identity verification systems, making them particularly difficult to detect using conventional methods.
 
There is no single blueprint for how criminals perpetrate synthetic identity fraud, which adds to its complexity. Increasingly, organizations face the challenge of distinguishing genuine customers from synthetic ones, especially when these false identities exhibit consistent, low-risk behavior that closely mimics that of real individuals. To stay ahead of evolving threats, organizations must leverage advanced detection tools capable of isolating and analyzing specific traits, behavioral patterns and characteristics that are frequently associated with synthetic identities.
 
“While the presence of living characteristics such as vehicle ownership, voter registration or familial connections is not a definitive solution to detecting synthetic identities, it represents an important piece of the broader identity puzzle,” said Steve Yin, senior vice president and global head of fraud at TransUnion. “These attributes alone cannot confirm authenticity, but when combined with credit header data, they offer valuable context that contributes to forming a clear picture of identity. By isolating and evaluating these elements, organizations can strengthen their ability to differentiate between real and synthetic identities with greater precision.”
 
There are a number of living characteristics that, when present, indicate an identity to be significantly more likely to be synthetic. For example, no known relatives and no motor vehicle registrations occur in 30-50% of all synthetic identities and increase the likelihood of being synthetic by up to 7x vs. legitimate identities. Other top characteristics that raise red flags include missing voter and vehicle registrations or having no record of property ownership on file. Notably, every synthetic identity analyzed showed no open bankruptcies, making it a universal trait among them.
 
TransUnion’s Synthetic Fraud Model is designed to proactively identify a wide range of public data indicators, along with numerous other risk factors, to help uncover synthetic identities before they can cause financial harm. By analyzing these signals early in the customer journey, the model enables organizations to take preventive action with greater confidence and precision.
 
At the same time, the model enhances operational efficiency by reducing the need for manual reviews and minimizing customer friction. This allows lenders to streamline their processes while improving fraud detection rates—catching more fraudulent activity with greater accuracy and speed, and ultimately protecting both their customers and their bottom line.
 
Yin added, “Just as fraudsters relentlessly exploit every tactic available to pursue their deceptive financial objectives, lenders must be equally vigilant and proactive in their defense. Solutions like TransUnion’s Synthetic Fraud Model empower lenders to detect risk at every stage of the customer lifecycle—starting with account creation—by identifying the absence of real-life attributes, helping to prevent fraud and minimize financial losses.”
 
To learn more about strategies to protect from digital risk with a clear picture of identity, click here. To learn more about how TransUnion’s TruValidate Synthetic Fraud Model can help lenders detect synthetic identity fraud while increasing approval rates for legitimate customers throughout the customer lifecycle, click here.