Synthetic identity fraud involves the piecing together of genuine personal information and fake details to form a brand new identity, which fraudsters then use to access a business’ private information or financial accounts.
AI plays a huge role in preventing synthetic identity fraud, as do image history searches, data matching and information monitoring. Platforms like Trustpair work to prevent businesses from falling victim to payment fraudsters by detecting discrepancies in real time, and monitoring changes to the data that could indicate higher risk.
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Key Takeaways
- Synthetic identity fraud combines real stolen data (like Social Security Numbers) with fake details to create a new, entirely fictional identity.
- It is the fastest-growing financial crime in the US: estimated losses reach $20–$40 billion globally per year, with US lenders alone facing $3.3 billion in exposure in H1 2025 (BIIA, 2026).
- 95% of synthetic identities pass standard verification checks, making them nearly invisible to legacy fraud detection systems (Thomson Reuters).
- In a B2B context, synthetic identities are used to infiltrate procurement cycles, submit fake invoices, and divert payments — often going undetected for months.
- The best defence is automated, continuous account validation that cross-references vendor and supplier details against external databases before every payment.
- Trustpair has blocked 100% of payment fraud attempts for its customers since deployment.
What is synthetic identity fraud?
Synthetic identity fraud is a sophisticated financial crime in which fraudsters blend legitimate stolen data — such as Social Security Numbers (SSNs) or Tax IDs — with fabricated information to construct entirely new, functional personas. This allows them to bypass traditional verification protocols and infiltrate corporate systems under the guise of legitimate entities.
For businesses, this poses a critical risk in the form of invoice fraud, where fraudulent suppliers intercept the payment chain or submit billing for unrendered services. These fabricated identities are systematically cultivated to build credibility — sometimes over months — before being used to exploit procurement vulnerabilities and then abandoned.
What makes synthetic identity fraud so dangerous is its invisibility: 95% of synthetic identities pass standard verification checks (Thomson Reuters), and since no single real individual is fully victimised, traditional red-flag systems rarely trigger.
How are synthetic identities created?
Here’s the step-by-step of synthetic identity creation:
- The fraudster obtains one piece of real information — usually a Social Security Number — typically purchased on the dark web
- They combine the real SSN with fabricated data: a false name, date of birth, and address
- They build a credit profile for the identity over time, opening accounts with smaller lines of credit and making legitimate payments to build trust with financial institutions. The longer the fraudster “incubates” the identity, the more credible it appears
- Once a strong credit history is established, the fraudster maxes out all lines of credit in one go and immediately abandons the identity
- The process is repeated — often across multiple synthetic identities simultaneously
There are two types of synthetic identities:
Manipulated
- A genuine identity where only one or two items of information have been altered
- Example: a consumer commits first-person fraud by falsifying their poor credit history to access better loan terms
Manufactured
- A completely fake identity where none of the data corresponds to a real individual
- Example: a fraudster opens a bank account using a fake identity with the purpose of being approved for a loan
What are the key indicators of synthetic identity fraud?
One of the most challenging aspects of synthetic identity fraud is that attackers often incubate their identities and wait until the right moment to strike. In 2025, synthetic identities were implicated in 21% of first-party frauds detected across financial institutions (BIIA, 2026).
Key indicators to watch for:
- Digital identity mismatch — physical details look clean but IP location, device fingerprint, or login behaviour don’t align
- Complaints from real customers or suppliers — a manipulated identity based on a real person may trigger alerts from the genuine victim
- Lack of credit history — a very thin or unusually short credit profile for a supposedly established entity
- Multiple accounts sharing the same contact details — same phone number or email address across different identities
- High-risk IP metadata — access requests originating from flagged or mismatched geographies
Digital identity mismatch
While the physical data may not be suspicious, always use additional verification methods to validate the identity of customers and suppliers.
By applying detective controls such as assessing digital identity factors like IP address, you can determine whether an individual is contacting you from where their physical address indicates. Two-factor authentication adds a further layer: sending a text message to the associated phone number verifies both that a real person exists and that the number belongs to the right identity.
Where these factors don’t align with the details provided or verified through an external database, the case warrants escalation to your fraud team.
Complaints from real customers or suppliers
Cyber fraudsters often spend weeks or months building a synthetic identity’s credit history before striking — but this creates one key vulnerability for manipulated identities. Since these are based on real people, there is a chance the genuine victim notices unusual activity and files a report, which can trigger an investigation and allow the synthetic identity to be identified and blocked.
How can your business detect and prevent synthetic identity fraud?
Preventing identity fraud requires you to answer two primary questions:
- Does this entity really exist?
- Do all the details match verified, publicly available records?
To answer these questions reliably, many companies rely on technology like Trustpair’s vendor database monitoring, which:
- Collects the data that vendors provide and compares it against trusted external sources
- Grades each vendor as favourable or unfavourable based on real-time risk signals
- Spots duplicate entries, errors, or missing data and flags these for review
- Automatically blocks payments to fraudulent vendors using customisable AI rules
Given that 67% of financial institutions saw fraud rates climb in 2025 (BIIA, 2026), manual vendor vetting processes are no longer sufficient. Automated, continuous validation is now the operational standard.
How is AI used to detect and prevent synthetic identity fraud?
AI is almost always used by fraudsters to build convincing credit profiles at scale — often managing multiple synthetic identities simultaneously. But AI is also the most effective counter-measure available.
Generative AI can combine biological and behavioural biometrics to combat synthetic identity fraud, checking how many applications have been submitted per device, for example. It can cross-reference ID photos against known duplicate identities in real time. AI can also conduct Know Your Customer (KYC) and Know Your Business (KYB) checks on an ongoing basis — not just at onboarding.
As AI-generated fraud becomes more scalable and harder to detect manually, automated account validation is no longer optional for finance teams. It is the only reliable way to catch synthetic identities before payments leave the business.
To conclude
Synthetic identity fraud occurs when criminals combine real and fabricated information to impersonate customers or vendors and infiltrate payment workflows. US lenders faced $3.3 billion in exposure in H1 2025 alone. Protect your business by checking for digital identity mismatches, verifying data against external databases, and using automated tools like Trustpair’s vendor monitoring platform for continuous oversight.
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