Synthetic Identity Fraud: What Is It & How to Detect it

Synthetic identity fraud has become one of the most prevalent and damaging types of financial crime, accounting for nearly 30% of all identity fraud cases in 2024. Losses have surpassed $3.2 billion globally in just the first half of last year, driven by fraudsters using AI and publicly available data to create convincing fake personas.

These identities often go undetected for months or even years, allowing criminals to build credit histories, exploit financial systems and vanish without a trace. As synthetic fraud becomes more sophisticated, businesses must adopt smarter, real-time detection tools to identify suspicious behavior before it causes lasting damage.

What Is Synthetic Identity Fraud?

Synthetic identity fraud is a form of fraud where criminals create a fake identity by blending real and fabricated personal information. This fictitious identity is then used to defraud businesses, bypass KYC checks, exploit promotions, secure loans, make purchases with stolen cards, and launder money. Because synthetic identities often mix authentic and fake data, they appear legitimate, making detection challenging and posing a significant threat to financial institutions and other industries reliant on precise identity verification.

Fraudsters employ various techniques to create synthetic identities:

Manipulated identities: Combining authentic documents with made-up information.
Blended identities: Merging real data from multiple sources.
Manufactured identities: Fabricating entirely new details, such as randomized social security numbers that fit within valid ranges.

How Are Synthetic IDs Created?

Synthetic ID creation typically follows these 4 steps:

Identity creation: The fraudster blends stolen, manipulated, or entirely fabricated data to create a synthetic identity. For instance, they might use a real but inactive Social Security number stolen from a child.
Credit application: Initially, the fraudster applies for credit to establish a credit history for the synthetic identity. This often requires multiple attempts as lenders are cautious with new, unproven identities.
Building credit: Once approved for a small line of credit, the fraudster meticulously builds a positive credit history by making regular payments. This improves the synthetic identity’s credit score over time.
Exploiting credit: With an enhanced credit score, the fraudster applies for larger lines of credit, eventually withdrawing funds and disappearing. This process can span months or even years, but it offers significant financial gain for the criminal.

Variations of synthetic identity fraud include paying individuals with good credit to link their accounts to the synthetic identity, creating fake digital footprints on social media, and using fake checks to temporarily repay credit lines before maxing them out again.

Importance of Synthetic ID Fraud Detection

Detecting synthetic identity fraud early is essential to prevent more complex fraud schemes. Businesses must implement advanced detection measures to identify fraudulent patterns, as these synthetic profiles can cause extensive financial and reputational damage over time. Robust synthetic identity fraud detection strategies help mitigate risks and protect organizations from evolving fraud tactics.

Uncover Intent Before Onboarding Begins

Use digital footprint signals to understand who’s behind the screen and stop synthetic identities early.

How To Detect Synthetic Identity Fraud

Synthetic identity fraud is engineered to bypass traditional verification methods, making it difficult to catch with standard KYC checks alone. To stay ahead, businesses need a multi-layered approach powered by dynamic data and real-time signals.

Analyze Digital Footprints

Digital footprint analysis goes beyond static identity data. By analyzing email, phone, and social media signals, you can validate a user’s online presence or flag those who leave no trace. Disposable email addresses, virtual SIMs, and suspicious domain usage can all indicate high-risk behavior before onboarding begins.

Spot Inconsistencies with IP and BIN Lookups

Cross-referencing IP geolocation and card BIN data helps surface mismatches that often reveal synthetic patterns, like a card issued in one country and an IP in another. Combining alternative data points builds a clearer picture of user intent.

Leverage Device Intelligence

Fraudsters rely on the same setups to scale attacks. By analyzing intelligence gathered via device and browser fingerprinting, you can detect emulators, virtual machines and repeated configurations, allowing you to link seemingly unrelated profiles and block repeat offenders.

Apply Velocity Rules

Velocity checks track how users behave — how fast they input data, how often certain patterns appear or how quickly they move through onboarding. These subtle signals help surface synthetic IDs operating at scale or supported by money mule networks.

Leverage Machine Learning

A transparent whitebox machine learning model can uncover connections and anomalies that manual reviews miss. By analyzing behavioral, device and digital signals in real time, you can adapt to emerging fraud patterns and stop threats before they escalate.

How to Protect Yourself Against Synthetic Identity Theft

The most effective defense against synthetic identity theft is a layered one. Start with foundational methods like ID document verification, biometric checks and public records analysis to confirm that an identity exists and aligns with official data. Then, strengthen your protection by combining these with real-time, dynamic signals, such as behavioral patterns, device intelligence and alternative data.

This multifaceted approach ensures you don’t rely on a single signal or tool. Instead, you’re building a comprehensive identity profile that filters out synthetic identities while minimizing friction for legitimate users.

How SEON Detects & Prevents Synthetic ID Fraud

Synthetic IDs are built to slip past static checks, but SEON is built to catch what others miss. By combining real-time digital footprint analysis, device intelligence, velocity rules, and AI-driven insights, SEON creates dynamic risk profiles that go far beyond traditional KYC.

You get a smarter way to spot fraud without drowning in manual reviews. From email and phone analysis to IP, BIN and behavioral signals, every user touchpoint becomes an opportunity to detect fake identities and stop fraud before it happens.

Stop Synthetic Fraud Before It Starts

Detect and block synthetic identities early with SEON’s real-time signals. Cut KYC costs, reduce manual reviews, and onboard only trusted users.

Frequently Asked Questions

What are the warning signs of synthetic identity fraud?

If you notice strange payments on your statement or start receiving suspicious emails, it’s possible some of your ID documents have been stolen and used for synthetic IDs.

How do people create synthetic identities?

To create a synthetic identity, you need some kind of real document to begin with. It could be a name, address or social security number. The fraudster then modifies or tweaks the information for their need.

Why do fraudsters use synthetic IDs?

Synthetic IDs are harder to detect than made-up, completely fake IDs because they contain an element of truth (the person’s ID documents). This is why fraudsters use them to bypass KYC checks or for fraudulent transactions, among others.

What kind of stolen data is used in synthetic ID fraud?

Fraudsters rely on any identifiable information they can find, including tax-related information, medical records, social security numbers, and even children’s identity records.

How do you prevent synthetic identity fraud?

Preventing synthetic identity fraud involves using unique, complex passwords with tools like password managers, staying cautious during online interactions to avoid social engineering, and leveraging cybersecurity measures such as antivirus software and two-factor authentication. Regularly monitor credit reports to catch suspicious activity early, and be careful when sharing personally identifiable information (PII) like Social Security numbers or ID copies. Additionally, consider freezing children’s credit reports to prevent fraudsters from exploiting their identities.

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