Unveiled Findings by TransUnion Showcase the Potency of Public Data in Exposing a $3.3 Billion Synthetic Identity Fraud Risk
In the ever-evolving world of financial crimes, synthetic identity fraud has become a significant concern for U.S. lenders. By 2024, this insidious form of fraud is projected to expose lenders to a potential loss of over $3.3 billion.
To combat this threat, TransUnion, a leading global information and insights company, has developed the TransUnion Synthetic Fraud Model. This innovative tool is designed to help lenders detect and prevent synthetic identity fraud, minimizing financial losses.
Synthetic identities, a blend of authentic and fabricated information, are constructed using stolen Social Security numbers, fictitious names, digital contact details, and behavioral patterns that mimic legitimate consumer activity. These identities are designed to bypass traditional identity verification systems, making them difficult to detect using conventional methods.
Steve Yin, senior vice president and global head of fraud at TransUnion, emphasizes that while living characteristics like vehicle ownership, voter registration, or familial connections are not definitive solutions for detecting synthetic identities, they are an important piece of the broader identity puzzle.
The TransUnion Synthetic Fraud Model helps lenders detect risk at every stage of the customer lifecycle, starting with account creation. By analyzing these signals early in the customer journey, the model enables organizations to take preventive action with greater confidence and precision.
Other top characteristics that raise red flags for synthetic identities include missing voter and vehicle registrations. Intriguingly, 30-50% of synthetic identities have no known relatives and no open bankruptcies, adding to their complexity.
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. However, by isolating and evaluating specific traits, behavioral patterns, and characteristics associated with synthetic identities, organizations can strengthen their ability to differentiate between real and synthetic identities with greater precision.
The TransUnion Synthetic Fraud Model proactively identifies a wide range of public data indicators and risk factors to help uncover synthetic identities. By leveraging advanced detection tools, organizations can stay ahead of evolving threats and enhance operational efficiency by reducing the need for manual reviews and minimizing customer friction.
As the battle against synthetic identity fraud escalates, artificial intelligence (AI) is playing a critical role. AI is improving fraud detection rates by up to 50% and reliably identifying such synthetic identities. Regulators like the Consumer Financial Protection Bureau (CFPB) are enforcing stricter standards on digital payment providers.
However, criminal tactics are evolving, including the use of AI for sophisticated phishing and deepfake attacks. This ongoing technological arms race necessitates the adoption of multi-factor authentication and behavioral biometrics for defense.
In conclusion, the TransUnion Synthetic Fraud Model is a significant step forward in the fight against synthetic identity fraud. By analysing and evaluating specific traits, behavioural patterns, and characteristics associated with synthetic identities, the model offers lenders a powerful tool to protect themselves and their customers from this growing threat.
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