Strengthening public confidence
Jump to ↓
The evolution of SNAP |
Blending ID verification strategies successfully |
Building in flexibility |
Digital advancements in identity verification can improve the experience of clients while protecting institutions, and both the mission of the institution and the interest of its clients are safeguarded by adding advanced digital security and verification measures.
The key features of effective and secure digital authentication are early and automated detection of fraud attempts and a comprehensive identity verification strategy, including the use of multi-factor authentication. However, government agencies expanding digital platforms must balance the risk of excluding legitimate users even as they install barriers to fraudulent ones.
Our recent white paper, Leveraging identity verification for faster benefits delivery, provides a detailed account of how agencies can approach this secure process by using automated tools and thoughtful deployment of existing practices.
The evolution of SNAP: Value of early detection
Identity verification by government agencies traditionally relied upon manual processes to complete a case file for each applicant. The detection of fraud was much more painstaking and could often not be initiated until a later stage of processing. Not surprisingly, access for legitimate applicants was cumbersome. All these instances led to security measures being more flawed in nature.
Today, digital platforms for submission tend to expand the number of applicants, increasing uptake but also growing fraud attempts. For example, the nation’s Supplemental Nutrition Assistance Program (SNAP), now has US states delivering benefits electronically, with a card or a phone app. Intake, however, is a different matter, and states and localities have different methods of screening applicants.
There are three typical methods of submitting a SNAP application: i) fully on a digital platform; ii) through a paper application; or iii) by a mixed process with an initial application online and then identity documents presented later in person. Each of these methods presents opportunities and challenges.
For instance, new account fraud is now very common and often driven by bots, although an auto-detection tool can deflect many of these. A newer form of fraud, synthetic identities, are built from stolen points of identification; but many machine-learning tools can be helpful in thwarting this by matching submissions to existing public and private databases.
Rapid flagging of failures-to-match can benefit both the agency and the legitimate applicant. Fraud schemers will be deterred and move on to other targets if denied at this early screening stage, while real applicants can be immediately notified about discrepancies — often simply errors — and fix them. Thus, government agencies can speed up the processing of screened applications while setting aside only flagged applications for review, increasing the security of the process. Even those agencies that need to maintain paper or mixed-method applications can still benefit from newer digital tools that handle search queries across several databases at once.
![]() |
Blending new and old ID verification strategies successfully
Today, comprehensive identity verification strategies includes multi-factor authentication. Indeed, many institutions have discarded single-solution cybersecurity (such as passwords) in favor of combining different types of attributes to improve security.
The type of key factors used in multi-factor authentication today include:
- Knowledge — This remains the bedrock of any client application and involves something the applicant knows like names, addresses, or social security numbers.
- Possession — What is the means by which the applicant is approaching the agency? Is the applicant using a phone or computer to apply on the platform? If so, that digital device can be screened against lists of previously flagged devices.
- Inherence — The third secure element of comprehensive identity authentication is inherence, or something that is unique to the individual. This would include things like fingerprint scanning, retinal scans, voice recognition, and facial comparison/age verification.
- Location — Geolocation can determine an applicant’s location and IP address for verification.
- Behavior — The newest type of digital verification, this includes how fast we type, whether we cut and paste, and other characteristics of users. It can also detect bot traffic and fraudster patterns.
Of course, the ethics of collecting all this data should be carefully examined before implementation. Any government agency should ask itself: once we collect it, can we safeguard it?
Building in flexibility
Secure tools that provide agencies with enhanced screening and verification should be flexible enough to grow as oversight or new opportunities develop. In our SNAP example, the government provides updated guidelines for new screening tools that SNAP agencies can utilize.
Those agencies that transitioned early to a digital-platform application, like New York City in 2015, now are seeing more than 90% of applications done by electronic submission. Even as the number of applicants increased, the timeliness of decisions (defined as less than 30 days from submission) greatly improved — at least until the pandemic hit.
Today, that timeliness is rebounding; yet even agencies that are ahead of the pack in adopting electronic intake can still run up against real-world conditions that temporarily or permanently change the game.
Changes in caseloads, the evolution of new fraud techniques and regulations, and even the success that comes with more user-friendly processes will require agency leaders to choose the secure digital tools that can best help their staff keep up with these dynamic conditions.
For more on innovative ID verification techniques, read our white paper, Maximizing trust and customer experience: Leveraging identity verification for faster benefits delivery.
![]() |
Thomson Reuters is not a consumer reporting agency and none of its services or the data contained therein constitute a ‘consumer report’ as such term is defined in the Federal Fair Credit Reporting Act (FCRA), 15 U.S.C. sec. 1681 et seq. The data provided to you may not be used as a factor in consumer debt collection decisioning, establishing a consumer’s eligibility for credit, insurance, employment, government benefits, or housing, or for any other purpose authorized under the FCRA. By accessing one of our services, you agree not to use the service or data for any purpose authorized under the FCRA or in relation to taking an adverse action relating to a consumer application.