At the same time, DMVs are moving more services online to improve the constituent experience. Driver’s license renewals, vehicle registration, address changes, voter registration and mobile driver’s licenses (mDLs) all allow residents to interact with government more conveniently. Delivering these services securely and with minimal friction requires a new kind of trust infrastructure built on modern identity and risk intelligence.
“DMVs are the state’s official identity broker to some degree, and the demand for online convenience is increasing,” says Neal Gallucci, Head of Public Sector Solution Consulting at Socure. “However, AI has fundamentally broken trust in the digital world, and what worked a few years ago no longer works today.”
In a world of deepfakes, synthetic identities and AI-generated documents, agencies need identity infrastructure that continuously establishes trust, detects deception and adapts to emerging threats.
For DMVs, that means adopting modern identity verification capabilities that strengthen accuracy and integrity, establish trust for legitimate users, protect against fraud and deliver online experiences with the same level of assurance as an in-person visit.
Addressing New Vulnerabilities
Traditional DMV services benefit from built-in security controls that are difficult to replicate online, including physical presence, document inspection, staff interaction, facility security and surveillance. “When you are in-person, you’re basically going through a full-blown identity-proofing,” says Gallucci.
As more services move online, those safeguards no longer exist by default. This creates new opportunities for identity fraud at the very point where government establishes trusted credentials.
That challenge is especially significant because DMVs are often the starting point for identity fraud, not the end target. Fraudsters seek governmentissued credentials that can be used to open financial accounts, obtain benefits, create additional identities and carry out broader identity theft schemes.
Common Identity Challenges
DMVs need identity platforms that address evolving fraud risks while working within existing technology environments and delivering fast, accurate verification with minimal impact on legitimate users.
Many DMVs rely on complex legacy systems that make large-scale modernization difficult. Rather than replacing those systems, agencies need flexible, API-based identity verification solutions that integrate with existing technology and provide fraud teams with clear visibility into risk signals.
In addition, some commonly used verification methods have become obsolete. For example, knowledge-based authentication (KBA), which verifies identity by asking questions based on personal history and public records, is unreliable because personal information is widely available through data breaches, social media posts and other sources. KBA also creates poor user experiences by requiring constituents to recall mundane details, such as the model of their first car. Incorrect answers can create additional friction and delays.
Document verification, another common verification method, is even more cumbersome. To prove identity, constituents must present governmentissued credentials, but those documents may not always be readily available, even for legitimate individuals.
Modern identity infrastructure must help agencies manage the full user lifecycle beyond driver’s license issuance, including validating out-of-state licenses, issuing licenses to individuals exiting the criminal justice system, updating addresses, verifying individuals with lost licenses and more.
Risk-Based Verification
A risk-based approach to identity verification enables DMVs to accurately assess risk, segment transactions, reduce false positives and provide frictionless constituent experiences. Here’s how it works:
Uses identity intelligence to assess risk. Risk-based verification evaluates attributes that constituents naturally provide during online transactions, such as a name, email address, phone number, physical address and device details, to determine whether they correlate to a verified identity or a fabricated identity.
It also evaluates risk signals tied to each attribute, such as a phone number activated only days ago or an address that is a commercial mail drop. Together, these signals produce a risk score that decides, in real time, how much friction to apply to a given interaction.
Segments risk traffic for targeted intervention. Risk-based verification uses machine learning models trained on historical data to segment risk traffic. Low-risk constituents can move through online services without unnecessary KBA challenges or document scans, while high-risk constituents can be routed to document verification steps or in-person visits. This concentrates the agency’s resources and in-person appointments on the transactions that truly warrant them.
Reduces false positives. False positives are one of the biggest sources of friction for DMVs because they can incorrectly block legitimate constituents from accessing services, resulting in support calls, escalations, unnecessary office visits and user frustration. They also fall hardest on the residents least able to absorb them, such as those without a current credential or the time for an in-person visit.
Traditional checks force a trade-off between stopping fraud and admitting good users. Accurate identity intelligence improves both at once, applying rigorous verification behind the scenes while clearing legitimate applicants with little or no added friction.
Supports mDLs. As DMVs expand the use of mDLs, digital identity verification becomes even more important. Issuing an mDL is among the highest-stakes identity decisions a DMV makes: The agency is provisioning a portable, cryptographically signed credential to the holder of the corresponding physical credential. Whatever assurance exists at that moment is the assurance every downstream relying party (public or private) inherits. Get it right and trusted identity extends to the phone; get it wrong and a fraudster walks away with a government-backed credential the wider economy will accept without question.
This is exactly where the in-person safeguards disappear. Because mDLs are often provisioned remotely, the physical presence, document inspection and staff judgment that once protected issuance are absent at the very moment the stakes are highest. Risk-based verification restores that assurance, confirming the applicant is the true holder of the physical credential (not someone who has acquired its data) before the digital version is minted.
“When I use my mDL, I’m using a government-issued digital token to do something officially. How is that transaction scrutinized?” Gallucci says. “We need to be careful about how much we trust mDL issuance without any additional verification.”
Conclusion
As DMV services become digital, states need modern identity infrastructure to translate in-person identity verification and assurance to online service delivery.
With risk-based verification, DMVs can strengthen digital identities, improve verification accuracy, protect against fraud, reduce false positives and enable seamless next-generation government services. More than that, getting identity right across the full credential lifecycle (at issuance, on every change to attributes or credentials and at reissuance), positions DMVs as the trust anchor for a digital economy that increasingly depends on them, turning a modernization mandate into a position of strategic value for the state and for every public- and private-sector service that relies on their credentials.
This piece was written and produced by the Government Technology Content Studio, with information and input from Socure.
