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.

Key Takeaways

Synthetic profiles can operate undetected for long periods, as they mimic legitimate user behavior, build credit histories and often pass standard KYC checks.
Fraudsters follow a clear process of maxing out credit lines and disappearing without a trace.
Synthetic identity fraud differs from traditional identity theft, mainly because it has no direct individual victim and is much harder to trace.
Detecting synthetic IDs requires advanced tools, including digital footprint analysis, device intelligence, velocity checks and machine learning models.

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 identity fraud is a staged process that unfolds over months or even years. Fraudsters build identities slowly, allowing them to blend in with legitimate customers.

Data collection: Fraudsters gather fragments of personally identifiable information (PII), including Social Security numbers (SSN), dates of birth and addresses. These details can come from data breaches, the dark web or purchased identity kits.
Identity creation: Using the collected data, they build a new synthetic profile that uses both real and fabricated data. The identity may use a real SSN with a fake name or a fully constructed identity with manipulated data.
Establishing credit history: The fraudster starts applying for credit. Initial applications may be denied, but they still generate a credit file. Over time, small lines of credit, timely payments and normal account activity create a legitimate-looking credit history.
“Bust-out” fraud: Once the synthetic profile appears trustworthy, the fraudster takes out larger loans or credit lines then maxes them out and disappears. Financial institutions absorb the losses because the victim never existed.

Fraudsters may also boost synthetic IDs by creating a fake digital footprint, adding authorized users with good credit, or cycling repayments before maxing accounts out.

Differences Between Synthetic Identity Fraud and Traditional Identity Theft

Although synthetic identity fraud and traditional identity theft are often grouped together, they operate in fundamentally different ways. One exploits real people, while the other fabricates entirely new personas. Recognizing these distinctions is crucial for choosing the right detection and prevention strategy.

	Synthetic identity fraud	Traditional identity theft
How it works	Combines real and fabricated data to form a new identity.	Uses a real person’s full identity without alteration.
Victim	No direct individual victim; identity is partly fictional.	A real person whose identity is stolen and misused.
Goal	Build credit and accounts under a fake profile, then “bust out.”	Access or misuse an existing person’s accounts.
Detection	Harder to detect due to realistic behavior and gradual credit building.	Easier to trace because the activity ties back to a real individual.

Importance of Synthetic ID Fraud Detection

Synthetic identities are hard to catch because they often look like real customers. They can pass standard KYC checks, build believable credit histories, and behave “normally” for months—sometimes years—before the fraud shows up.

That’s why early detection matters. The longer a synthetic profile stays active, the more opportunities it has to open new accounts, increase limits, and scale losses. The key is spotting what synthetic profiles usually lack: real-world continuity across identity signals over time (consistent history, stable linkages, and credible patterns), not just a set of matching credentials.

“What’s much harder to fabricate is continuity — the rich, multi-dimensional footprint of a real person who has existed and interacted naturally online over time.”
— Mira Sidhu, Director of Growth, Compliance Solutions (IDV)

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.

Synthetic Identity Fraud Prevention Strategies

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.

Implement enhanced identity verification: Use dynamic data sources and multiple verification layers to validate identity elements that are often manipulated in synthetic profiles.
Adopt machine-learning-based risk scoring: Models can analyze behavioral, device and identity signals to detect anomalies that manual reviews may miss.
Conduct continuous monitoring: Track user behavior over time to spot unusual activity patterns, sudden spending spikes or inconsistencies that appear after onboarding.
Use alternative data for verification: Incorporate digital footprint analysis and device intelligence to validate whether the identity has a consistent online presence.
Collaborate and share data with industry partners: Cross-industry intelligence helps catch repeat offenders and mule networks by identifying shared fraud patterns.

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.

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.

Learn more about:

Browser Fingerprinting | Device Fingerprinting | Fraud Scoring | Fraud Detection with Machine Learning & AI

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