Synthetic Identity Fraud: Detection and Prevention
Synthetic identity theft, meaning an act in which criminals combine real and fabricated information to create a new identity for fraudulent purposes, is difficult to spot using traditional checks. This is because they also include real personal details.
According to the LexisNexis Risk Solutions Cybercrime Report published in 2026, which analyzed more than 116 billion online transactions during 2025, 11% of fraud cases involved a synthetic identity, an eight-fold increase on 2024. Synthetic identity theft has overtaken true identity theft, which fell to 6.4% of reported fraud. Hence, making it crucial for businesses to focus on stronger identity verification techniques.
Key Takeaways
- Synthetic identity fraud uses a mix of real and fake information to create a fraudulent identity.
- Detection requires analyzing multiple signals such as behavior, device, network, and transaction data.
- Businesses can strengthen fraud detection and reduce financial losses through layered verification.
- Diopter adds an extra identity assessment layer by connecting the person, request, payment details, and action being taken.
Synthetic Identity Fraud vs. Stolen or Traditional Identity Theft
The following table compares synthetic identity fraud against traditional identity theft.
| Aspect | Synthetic identity theft | Traditional identity theft |
|---|---|---|
| Meaning | Criminals combine real and fabricated information to create a new, fake identity. The identity may use a genuine Social Security number with a fake name, address, or date of birth. | Criminals steal and use a real person’s complete personal information to impersonate them. The information may come from phishing, data breaches, account takeovers, or stolen documents. |
| Real-life example | According to the U.S. Department of Justice, a fraud ring created synthetic identities and used them to open credit accounts with Synchrony Bank, Capital One, Discover and other financial institutions. | A fraud ring obtained personal information belonging to real Wells Fargo customers, including dates of birth, account numbers, driver’s license numbers and Social Security numbers. The criminals then used fake IDs to impersonate those customers and withdraw money at Wells Fargo branches. |
| Key difference | It creates a new identity that does not belong to a real person. This is why synthetic identity fraud detection can be tougher. | It involves impersonating an existing person, so unusual activity can often be linked to the genuine victim. |
| Statistics | At the end of 2024, TransUnion estimated $3.3 billion in potential US lender losses from synthetic identities. | Javelin Strategy & Research’s 2025 Identity Fraud Study reported $27.2 billion in overall US identity fraud losses in 2024. |
How Does A Synthetic ID Fraud Unfold?
Synthetic identity theft develops in stages. Criminals build a believable identity over time and then gain access to financial data. The final step is to use the identity to commit fraud. Here is how a synthetic ID fraud unfolds in different steps:
Obtain Real Information
The first step involves criminals acquiring legitimate personal information about individuals. The common methods they use for this include data breaches and phishing.
Add Fabricated Details
A synthetic identity is created by mixing real information with fake names, addresses, phone numbers, or other details.
Build Credibility
The identity is gradually introduced into financial and digital systems, with normal-looking activity used to establish a history and gain trust.
Gain Financial Access
Once the identity appears credible, criminals use it to obtain credit, loans, accounts, or other financial services.
Identity and Payment Attack
The fraudster eventually uses the established identity to carry out high-value transactions, max out credit, or move funds before abandoning the identity.
A synthetic identity can become particularly dangerous when it is used to support a high-stakes request during a call.
What Signals Can Reveal Synthetic Identity Theft?
Synthetic identity fraud often appears as a combination of small warning signs across identity, behavior, device, and transaction data. There is no single signal that reveals synthetic ID fraud. Businesses must look for clusters of warning signs.
- Identity information: If names, addresses, phone numbers, or dates of birth do not match, it can be a sign of fraud. Credit histories that are new or thin can also be a cause for suspicion.
- Behavior: Unusual typing speed, repeated corrections, heavy copy-pasting, or automated-looking form activity can indicate fraudulent onboarding. In video-based verification, unusual facial movements, lip-sync issues, or signs of AI-generated or manipulated video can be additional signals.
- Network and device: If the same device, network, or IP address is used to send several applications, it can be fraud.
- Inconsistencies: In video or voice interactions, inconsistencies between a person’s appearance, voice, identity information, or other verification signals may warrant further review. AI-generated or manipulated content makes these inconsistencies harder to spot without additional analysis.
- Type of transactions: Accounts may become highly active, or show sudden spending, increased credit card use, and multiple loan applications. These patterns can indicate potential financial exploitation.
What Happens When Synthetic Identities Meet AI-Generated Media?
Synthetic identities become even harder to detect when combined with AI-generated media. Criminals can create realistic faces, clone voices, or manipulate videos to give a fabricated identity a convincing human presence.
For example, a synthetic identity may use genuine personal information along with an AI-generated face that does not belong to a real person. This makes the profile appear more authentic. AI-generated or altered video can also be used during remote verification or calls.
So a government ID, selfie, or static document may appear genuine, but the person presenting it may not. This is one of the drawbacks of traditional identity checks.
As a result, in high-risk interactions such as account opening, payment approvals, or account recovery, businesses must use more than a single verification step. They should combine document checks with liveness detection, behavioral signals and transaction monitoring to detect synthetic identity deception.
How Does Diopter Approach Synthetic Identity Deception?
Diopter takes an action-focused approach to identity fraud. Instead of checking whether a face or voice looks genuine at one point in time, it continuously analyzes the call to assess potential signs of impersonation and fraud.
Verifies the person. Diopter compares the caller with trusted identity signals, then considers the context and channel through which the request was made.
Assesses the conversation. The tool looks beyond individual video or audio clips. It studies the conversation in detail to see if there are authority claims, urgency, or attempts to bypass normal processes.
Validates payment requests. When a payment request is made, Diopter validates the beneficiary’s banking information using proprietary data systems it has access to. The beneficiary account itself is checked, rather than relying only on information already held in the customer’s records.
Checks the approval process. Diopter checks the request against the approval path your policy already requires. If a request skips dual approval or a required callback, Diopter surfaces it as out of policy and routes that verdict to the approver. It advises rather than blocks, so it does not sit inline in payment rails and will not stop a transfer on its own.
Turns detection into action. Instead of simply reporting that something looks suspicious, Diopter provides a verdict such as “Hold the instruction,” giving the team a clear next step before money or access moves.
Diopter’s goal is to detect identity fraud so that a convincing synthetic identity does not become a successful attack. By linking identity verification to the specific action being requested, businesses can make better decisions before a fraudulent payment, access grant, or account change is completed.
Where This Matters for Financial Services and Other Businesses
The scale of synthetic ID theft helps explain why synthetic identity fraud detection and protection matters across the financial ecosystem.
According to Javelin Strategy and Research’s 2025 Identity Fraud Study, consumers in the US lost $27.2 billion to identity fraud in 2024, up 19% from the previous year. The study also highlights how data breaches, deepfakes, AI, and new digital payment methods are creating new opportunities for fraudsters.
Banks, fintechs, payment companies, and other businesses handling high-value transactions face several consequences and must deploy synthetic and AI fraud detection techniques to safeguard their customers, transactions, and operations.
- Criminals may use fabricated identities to perform fraudulent transactions. They may open accounts, obtain loans or credit, or abuse payment systems. The resulting losses can include unpaid credit and chargebacks.
- Businesses may also face higher investigation and manual-review costs as teams try to identify suspicious accounts.
- Synthetic ID fraud can do more harm than financial loss. When fraudulent activity reaches the accounts of legitimate customers, it can increase customer friction and weaken trust.
- Teams may face a higher volume of alerts and cases, putting additional pressure on existing resources.
Financial institutions can combine traditional security checks with real-time monitoring for identity verification. This aligns with Diopter’s approach: the tool links identity verification to the specific action being requested, checking who is making the request and what is being changed.
Steps to Prevent Synthetic Identity Fraud
Preventing synthetic identity fraud requires ongoing monitoring and verification. Businesses should keep monitoring accounts after onboarding to spot unusual activity early. Here are some simple steps to reduce the risk:
- Monitor accounts over time: Look for unusual changes in spending, account activity, behavior, or credit use. A synthetic identity may appear normal at first but become risky later.
- Use multiple fraud checks: Combine identity checks with device, behavior, network, and transaction monitoring. Diopter takes a broader approach by connecting identity verification with the specific action or payment being requested.
- Review unusual activity: Add extra checks or human review before approving high-risk actions, such as large credit requests, unusual transfers, or changes to account details.
- Connect different signals: Look for links between identities, devices, contact details, and transactions. Activity that looks normal on its own may look suspicious when these signals are viewed together.
What Should Businesses Do About Synthetic Identity Fraud?
Businesses can reduce synthetic identity fraud risk by continually reviewing behavior, checking identities carefully, and reviewing transactions. Ongoing monitoring helps identify suspicious activity as a synthetic identity builds credibility or is used for a high-risk transaction.
Diopter extends this approach to live interactions, analyzing video and voice during calls and connecting identity verification with the actions or payment requests made during the conversation.
Frequently Asked Questions
Can voice or video verification help detect synthetic identity fraud?
Yes. Voice and video checks can help detect AI-generated faces, cloned voices, and manipulated media. However, you must use a combination of liveness detection, behavioral signals, identity checks, and device intelligence.
Can Diopter help businesses detect suspicious identity or payment activity in real time?
Yes. Diopter connects identity and payment verification to the requested action. It can assess identity signals, payment details, conversation context, and business controls to help flag risky activity before a transaction or access change is completed.
Can a real person be involved in a synthetic ID theft attempt?
Yes. A real person may knowingly or unknowingly participate in creating or using a synthetic identity. Criminals can also use genuine personal information without the person’s knowledge. It is vital to verify the identity and context behind each transaction.
Can Diopter replace traditional KYC and synthetic identity fraud detection tools?
Diopter is not a replacement for traditional KYC or fraud detection tools. Instead, it adds another layer of identity assessment by examining the person, request, payment context, and interaction. It helps businesses identify risks that standard checks may miss.
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