How biometric authentication prevents fraud before it happens

The financial stakes are rising. U.S. consumers reported more than $12.5 billion in fraud losses in 2024, a 25% increase from the previous year, according to the Federal Trade Commission. At the same time, Liminal research found that 79% of organizations prefer unified platforms that merge authentication with fraud prevention. The takeaway is clear: authentication should not be treated as a compliance checkbox or a login control. It should be a proactive fraud prevention strategy that stops imposters before they can open, access, or take over an account.

Strong biometric authentication can distinguish real users from attackers at the point of access. That shifts fraud protection away from downstream damage control and toward prevention, helping organizations block fraud before a transaction, account takeover, or synthetic identity scheme can take hold.

Why fraud starts before the transaction

Fraudulent transactions are the final step in a process that likely began weeks or even months earlier, through identity compromise or fabrication. Data from the Identity Theft Resource Center illustrates this evolution; their latest Trends in Identity Report indicates that 52% of people who contacted the organization were dealing with identity misuse, versus only 35% reporting simple compromise. There’s a clear shift in fraud techniques, with the active use of stolen or fabricated identities to access accounts, open new ones, and execute fraud over an extended period versus a one-time compromise.

Account creation abuse

Many organizations have vulnerabilities in their identity assurance processes for new account creation, making it a primary attack vector for fraudsters.  Fraudsters often create accounts using stolen personal information or digitally manipulated documents. If an institution relies only on basic identity checks designed to meet compliance requirements rather than prevent fraud, these controls can be easily bypassed.

Once the fraudulent account is established, this gives attackers a platform for many types of fraud. This might include receiving funds from scam victims, obtaining credit or loans they never intend to repay, or laundering money. They might also simply let the account sit dormant so that it ages and looks more legitimate for larger schemes later on.

Traditional fraud detection can struggle with detecting fraudulent account creation because there’s no unusual transaction or pattern of transactions to detect at first, and most fraudsters will ensure the initial activity looks normal as just the use of the identity itself was the illegitimate action. Without strong identity verification at account creation that verifies the person submitting the application is live and present, organizations must wait to catch fraudulent activity that could have been blocked before the account was ever allowed to exist.

Synthetic identity fraud

Synthetic identities are created using combinations of real and fabricated data. They might include a real Social Security number (often one that belongs to a child, older person, or recently deceased person) but fictional personal information. Fraudsters often invest a significant amount of time cultivating these synthetic identities, building them credit histories and sometimes even a legitimate internet footprint. Eventually, the synthetic identity will rapidly  max out its credit lines and “bust out”, with no intention of repayment. At this point transaction monitoring will finally flag the behavior, but by then, the losses are already booked.

Better transaction monitoring won’t catch synthetic identity fraud. Identity assurance is needed to detect when the person behind the account application isn’t a real person with real history.

Why AI-generated attacks make authentication harder

AI-generated fraud is changing what organizations must defend against at the point of authentication. Attackers can now use deepfake videos, AI-altered selfies, face swaps, and injected digital media to imitate a legitimate customer during remote onboarding or account recovery.

These attacks are designed to defeat controls that only confirm a document was submitted or that a face appears in front of a camera. A convincing image alone is no longer enough to prove that a real person is present and authorized to access an account.

This is where biometric authentication and liveness detection become essential. Effective liveness detection helps determine whether the person completing a verification or authentication step is physically present, rather than a replayed video, printed image, synthetic face, or manipulated camera feed. It adds protection against presentation attacks and injection attacks that can otherwise make fraudulent identities appear legitimate.

For organizations, the goal is not simply to add another authentication step. It is to establish confidence that the person behind a transaction, account opening, recovery request, or high-risk account change is both real and the rightful account holder. As AI-generated attacks become more convincing, high-assurance identity verification and biometric re-authentication become critical controls for preventing fraud at the source.

The role of authentication in stopping fraud early

Strong authentication establishes high confidence that the person presenting credentials truly is who they claim to be. To contrast the approach taken by credential-based versus strong authentication, credential-based authentication simply asks the user: “Do you know the password?” High-assurance identity authentication instead asks: “Are you really the person who opened this account and has the legitimate right to access it?” The first question can be answered by anyone who has purchased or phished those credentials, or even by a credential-stuffing bot. The second question requires actually being the account holder. The security standard is elevated from something you know, such as a password, to something you are, with biometrics.

A layered strategy can combine biometrics with other user authentication methods so that the level of assurance matches the risk of the interaction.

Verifying real users from first interaction

The most effective fraud prevention starts with the very first interaction. During account creation, comprehensive identity verification should combine document inspection, biometric capture, and liveness detection to establish a high-confidence baseline. This creates a biometric anchor: a trusted biometric reference that can be used to re-verify identity whenever needed. With this anchor, future authentication can reference the biometric template rather than relying on credentials that might be shared or stolen. This foundation also supports continuous identity assurance, which helps organizations maintain confidence in a user’s identity throughout the customer lifecycle. An attacker might have the credentials and even something like a printed photo of the user, but won’t be able to pass the biometric check and liveness detection.

Enterprise-grade biometrics in the cloud also ensures that this identity assurance is available on any device. Traditional on-device biometrics like Face ID only prove that someone authorized by the device is present, not necessarily which person. Many times these on-device biometrics fail back to a PIN, essentially allowing biometrics to be bypassed. Enterprise biometrics in the cloud instead maintain identity consistency by using the same biometric template across all devices and channels, eliminating gaps attackers can exploit.

The result is the ability to recognize legitimate users with high confidence no matter what channel or device they’re using, while creating escalating barriers for fraudsters. Each touchpoint becomes another opportunity for the institution to detect inconsistencies and strengthen identity assurance, rather than serving as a potential breach point.

Authentication vs downstream fraud detection

Institutions and organizations have invested heavily in fraud detection systems. These powerful systems, often bolstered with cutting-edge AI and machine learning capabilities, excel at analyzing transactions for suspicious patterns and flagging anomalies. But by definition, they are detecting fraud that is already in progress, rather than preventing it from happening in the first place.

These systems have an enormous strategic role for every organization. For maximum effectiveness and maximizing ROI on your fraud prevention efforts, they should be layered with robust tools that also protect accounts upstream, within the authentication layer.

Cost and risk differences

Every dollar that you spend preventing a fraudster from gaining access to your systems can deliver significant return on investment, saving you multiples of that dollar in downstream costs. Those downstream costs include the losses from the fraudulent transaction itself, plus the investigation expenses, customer remediation, regulatory reporting, reputational damage, and other operational challenges of unwinding the activity. When fraudulent activity is blocked before it happens, none of these subsequent expenses are incurred.

Consider an account takeover scenario: when an attacker successfully authenticates to a legitimate user’s account, fraud detection must then distinguish whether or not their activity is legitimate and determine whether or not to block a transaction. False positives can frustrate the customer, while false negatives result in losses. The detection task could have been avoided if the attacker had been blocked at the identity verification layer.

Now, consider the same scenario but with continuous identity assurance in place. While the attacker might’ve logged in with stolen credentials, if biometrics authentication is in place their [MJ1] biometric attributes won’t match the legitimate user. This enables the system to challenge suspicious activity before the transaction is conducted and with much higher precision than transaction-based heuristics.

Building a fraud-first authentication strategy

To evolve authentication from an IT security function into a strategic fraud prevention strategy requires deliberate design that selects authentication approaches based on their overall fraud prevention efficacy, not just their compliance checkbox coverage.

Layered and adaptive approaches

An effective fraud-first authentication approach utilizes layers, with each adding assurance while remaining proportional to risk. The foundation of this approach is a high-assurance biometric anchor established during initial verification. This anchor enables subsequent authentication attempts to reference the anchor, rather than relying on credentials.

Atop that foundation, risk-based authentication should be used to apply different levels of context-based verification. Low-risk actions on a known device can proceed with no or minimal friction while higher-risk requests like adding new payees, making account changes or transferring large sums of money can trigger  biometric re-verification. With an adaptive approach, friction is kept proportional to risk. Organizations can use continuous authentication for ongoing identity confidence and step-up authentication when a customer attempts a high-risk action, such as changing account details, adding a payee, or transferring a large sum of money.

For legitimate customers, having the additional biometric verification before engaging in a higher risk can bolster your organization as one that is serious about protecting their account and defending against fraud.

Continuous monitoring of user activity provides the ongoing layer of context that catches suspicious activity point-in-time authentication would have missed.

Through this layered approach, the lifecycle of fraud attacks can be directly addressed. The approach is designed to catch fraudulent account creation before the accounts can become established, block account takeovers even when credentials have been compromised, detect mid-session anomalies and session hijacking, and ensure that channels like recovery and support, which are often the weakest link in identity security, require high-assurance verification to be kept as secure as other sensitive operations.

Every authentication enhancement should be evaluated for its impact on fraud outcomes that give it additional value beyond its (equally critical) role in ensuring compliance. When IT, fraud, and product teams can align around their shared objectives, authentication becomes a strategic growth driver with measurable ROI, rather than a cost center.