At A Glance
Synthetic identity fraud is when someone combines real and fabricated personal information to create a fake identity, often to open accounts or obtain credit. Read on to learn how you can detect and prevent it.A new account can look legitimate even when the identity behind it isn’t. Synthetic identity (ID) fraud combines real and fabricated personal information to create an identity that appears credible enough to pass routine checks. Because it may not belong to one identifiable person, it can be difficult for businesses to spot and investigate.
Understanding how synthetic ID fraud works can help organizations strengthen their identity checks, recognize suspicious patterns and reduce losses without adding unnecessary friction for legitimate customers.
What is synthetic identity fraud?
Synthetic identity fraud happens when someone combines real personal information with fabricated details to create a fictitious identity and use it to obtain a financial or other benefit. For example, an application might pair a real Social Security number with a made-up name, birth date or address.
The identity may look plausible when each data point is checked on its own. The warning signs can emerge when those details are evaluated together or when an organization examines how the identity behaves across accounts and over time.
Fraudsters may use synthetic identities to apply for credit, loans, services or benefits. Some seek value quickly; others spend time building an account history before attempting to take a larger amount of credit and stop paying. That delayed activity can make synthetic ID fraud harder to detect than fraud that appears at account opening.
Synthetic ID fraud vs. traditional identity theft
Both types of fraud can involve stolen personal information, but the identity being used is different.
| Synthetic identity fraud | Traditional identity theft | |
|---|---|---|
| Identity used | A fabricated identity assembled from real and false details | The identity of a real person, used without their permission |
| Who may notice first | A business may discover it through account behavior, investigation or a loss | The affected person may notice unfamiliar accounts or transactions |
| Typical challenge | There may be no single person who can report the identity as stolen | The real identity owner may report the misuse |
| Potential account pattern | Accounts may be developed over time before a loss occurs | The fraudster may use existing accounts or open new ones in the victim’s name |
The distinction matters for detection and investigation. In traditional identity theft, a real person may recognize and report suspicious activity. With synthetic ID fraud, the identity can persist without a clear consumer complaint, and losses may initially look like ordinary credit defaults.
Synthetic identities can still harm real people. If a fraudster uses someone’s genuine personal information as part of the fabricated identity, that individual may face problems connected to the misuse even if they never opened the account.
How are synthetic identities created?
Synthetic ID fraud can involve different tactics, but the process often follows a series of broad stages:
- Personal information is obtained: A fraudster may get real identity details through data exposure, scams or other criminal sources. In some cases, they use fabricated information instead.
- Real and false details are combined: The fraudster pairs selected data points to create an identity that appears plausible, even though the combination as a whole does not accurately describe a single person.
- The identity is presented to an organization: An application or account request tests whether the identity can pass onboarding checks. A thin or incomplete history may make it harder to assess.
- Accounts and history may be developed: Some fraudsters use accounts over time to establish a record of ordinary activity. Others may pursue value more quickly.
- The fraudster attempts to extract value: This may involve taking out available credit, accessing services or otherwise exploiting the account, then abandoning it. When losses appear, they can be misclassified as routine delinquency if the identity concerns aren’t recognized.
This sequence is a general pattern, not a fixed playbook. The data used, account types targeted and time between account opening and loss can vary. That’s one reason organizations benefit from reviewing identity signals throughout the customer lifecycle, not only at the application stage.
Why synthetic ID fraud is difficult to detect
A synthetic identity may contain details that appear valid individually. A name, address, or identity number may not raise an immediate concern on its own. The inconsistency can appear in the relationships between details, the length and shape of an account history or behavior across channels.
A single signal is rarely enough to establish fraud. Businesses can look for patterns such as:
- Identity details that do not consistently resolve to the same person
- A credit or account history that does not fit the application
- Connections between accounts, devices or other identities that merit review
- Differences between information provided and information observed across interactions
- A pattern of account activity that changes as access to credit or services grows
Organizations should interpret signals in context and use appropriate review paths. The goal is to identify risk while allowing legitimate customers to proceed with as little unnecessary friction as possible.
How businesses can reduce synthetic ID fraud risk
A stronger approach combines identity verification with analysis of account relationships, behavior and risk over time. Businesses can consider:
- Checking identity at key moments: Evaluate risk at onboarding and, where appropriate, at first login, account changes or other higher-risk events.
- Using multiple data sources: Compare application details with relevant identity, credit and alternative data to build a more complete view.
- Reviewing linked activity:Look for relationships among identities, accounts and devices that may reveal patterns not visible in a single application.
- Applying risk-based decisions: Use scores and decision strategies to determine when to approve, request additional verification, or route a case for review.
- Monitoring portfolio outcomes: Review suspected fraud and charge-offs so teams can identify emerging patterns and refine their approach.
These measures can help detect synthetic ID fraud earlier, reduce avoidable losses and focus manual review where it can have the most value.
Why partner with us
We help businesses detect and manage synthetic ID fraud with identity insights, advanced analytics and data designed to support decision-making across the customer lifecycle.
Our capabilities can help organizations resolve identities and identify synthetic traits using credit, alternative and partner data. Custom scores and decision strategies can be combined with device intelligence and relationship history to give teams a broader view of risk. Organizations can apply checks at onboarding and at later interactions, with the aim of reducing false positives and unnecessary manual reviews while applying appropriate friction to higher-risk activity.
We also support integrated fraud prevention. Businesses can layer identity verification, device intelligence and behavioral analytics into decision workflows and connect our data with existing tools through Application Programming Interfaces (APIs).
