What is Synthetic Identity Fraud?

Synthetic identity fraud is one of the most costly and elusive threats facing financial institutions today. Fraudsters combine real and fabricated information, a valid Social Security number, a made-up name, a false date of birth, or a borrowed address, to create identities that don’t exist but behave like they do.

What makes synthetic fraud so difficult to stop is the no-victim problem. Unlike traditional identity theft, a synthetic identity often does not belong to a real person, so no consumer is there to sound the alarm. Fraudsters spend months or years building a believable credit history before they strike, and by the time the pattern is recognized, losses are already locked in.

Socure’s solution, Sigma Synthetic Fraud, proactively detects synthetic identity fraud before it causes financial losses or reputational harm. Sigma Synthetic Fraud combines 400+ third-party data sources and uses the industry’s most predictive machine learning algorithms to uncover complex patterns and connect synthetic-specific behavioral elements. This empowers your organization to capture fraud at the door — making way for more good customers.

How to Protect Against Synthetic Identity Fraud

No single control stops synthetic fraud on its own. The most effective defenses layer identity intelligence, behavioral signals, and real-time detection, and they start at the door, not after the bust-out.

• Go beyond basic digital identity verification by using biometric data, document verification, and authentication protocols to confirm the person behind an application actually exists.
• Regularly monitor and update customer information to catch irregularities that signal a synthetic identity building credibility over time.
• Add multi-factor authentication methods, such as one-time passwords, biometric verification, or device authentication, to strengthen access controls and hinder unauthorized access attempts.
• Use predictive fraud detection technology, including machine learning and AI, to detect anomalies that rules-based systems and manual review often miss.
• Fraud teams, frontline staff, and customers need to know what synthetic fraud looks like in practice, including slow credit-building, inconsistent identity elements, and suspicious account behavior. The faster they can flag it, the smaller the loss.
• Share intelligence with industry partners. Consortium-level data, shared across banks, fintechs, and platforms, surfaces patterns no single organization can see on its own.

Socure’s Sigma Synthetic Fraud solution reduces false positives by 25% and increases synthetic fraud capture to 71% in the riskiest 3% of consumers. With the industry’s largest volume of consortium feedback, human-in-the-loop label cleansing, and generative AI techniques for email tumbling risk detection, Socure expertly stops synthetic fraud while improving the consumer experience for your trusted customers.

Synthetic Identity Fraud vs. Traditional Identity Fraud

Synthetic identity fraud involves the creation of a manipulated or fabricated identity, while traditional identity fraud exploits a complete and authentic identity of an individual.

Both traditional identity theft and synthetic identity fraud can result in significant financial losses for individuals and financial institutions. However, there are key differences between synthetic and traditional identity fraud that require unique approaches to detection and prevention.

Differences Between Synthetic Identity Fraud and Traditional Identity Fraud

How Does a Synthetic Identity Get Created?

Synthetic identities are created in a few distinct ways. In practice, fraud teams usually see three methods:

• Identity compilation: A real SSN is paired with invented personally identifiable information, such as a false name, date of birth, or address.
• Identity manipulation: Real identity data is altered just enough to create a new profile.
• Identity fabrication: Entirely bogus personally identifiable information is used to build an identity from scratch.

SSN randomization, adopted by the Social Security Administration in 2011, made this problem harder to detect. Because SSNs no longer follow older geographic and issuance patterns, detection systems have less context for separating legitimate numbers from invented or misused ones.

Once the profile exists, the credit-building lifecycle begins. Fraudsters start with low-risk activity, may piggyback on legitimate accounts to gain credit history, and then move toward a bust-out, maxing out available credit before disappearing.

Children, the elderly, and homeless individuals are common SSN-theft targets because they are less likely to monitor their credit closely, giving synthetic identities more time to mature unnoticed.

What is a Synthetic Identity Used For?

Synthetic identities are used for more than credit card bust-outs. A Federal Reserve-convened focus group of fraud experts identified four primary categories of use:

• Credit repair: Hiding from negative credit history under a clean synthetic profile.
• Fraud for living: Using a synthetic identity to access employment, housing, utilities, or bank accounts, even when there is no immediate plan to default.
• Payment default schemes: Building credit over months or years, then busting out across multiple institutions.
• Other criminal activity: Using synthetic identities to support money laundering, human trafficking, narcotics distribution, terrorist financing, or evading law enforcement.

Synthetic identity creation is a strategic, long-term effort that lets criminals operate covertly while gradually building trust within financial systems. This poses a persistent and evolving challenge for fraud and compliance teams at financial institutions, fintechs, and digital lenders, and it requires identity intelligence that goes beyond what any single organization can see on its own.

Synthetic identity fraud isn’t new, but it has accelerated sharply alongside digital onboarding, data breach proliferation, and the rise of AI-enabled fraud tools. The threat is no longer theoretical. For financial institutions and fintechs, the question is how many synthetic identities are already in the portfolio, and how long they’ve been there.