Zero Trust for Non-Technical Leaders: What You Need to Know
As government systems are increasingly accessed from homes, phones, and third-party vendors, Zero Trust serves as the foundation for their security.
Estimates suggest $100–$135 billion in pandemic unemployment benefits were lost to fraud. Most of that loss happens at the front door, during application intake, when agencies couldn't verify documents quickly enough to separate legitimate claims from fraudulent ones.
Automated document intake prevents fraud by verifying identity documents and supporting materials in real time, extracting structured data that can be cross-checked against authoritative sources, and flagging inconsistencies before claims are processed. When implemented correctly, it reduces fraud without adding friction for legitimate applicants.
The Fraud Problem in Benefits Applications: By the Numbers
The scale of benefits fraud during COVID-19 revealed systemic vulnerabilities. The Department of Labor estimated that unemployment fraud exceeded $87 billion. Medicaid improper payments reached $86 billion in 2022.
These numbers reflect a pattern: fraud concentrates at intake. Bad actors exploit the gap between when agencies receive documents and when they can verify them. In manual workflows, that gap can last days or weeks. During the pandemic, when states waived verification requirements to process claims faster, the gap became permanent.
The problem isn't just volume. It's traditional fraud detection that happens after acceptance. Claims are approved, payments are issued, and agencies attempt recovery later. Detection rates are low, and recovery rates are lower. Prevention requires moving verification earlier in the workflow, closer to the point of document submission.
How Manual Document Review Creates Fraud Vulnerabilities
As we explored in Why Document Intake Is the Weakest Link in Digital Services, paper-based and semi-digital intake processes create structural weaknesses that fraudsters exploit systematically.
Manual review relies on caseworker judgment to spot forged documents. That works for obvious fakes, such as misspellings, wrong fonts, and poor image quality. It can fail against professional forgeries: documents with correct security features, plausible biographical data, and supporting materials that appear consistent.
The time lag creates another vulnerability. When agencies accept documents and verify them later, they must process applications in good faith while verification is pending. That delay is enough for fraud groups to submit hundreds or thousands of applications using variations of the same forged documents.
The third vulnerability is data entry. When caseworkers manually transcribe information from documents, they can introduce errors that fraudsters exploit. A transposed digit in a Social Security number can prevent automated cross-checks against IRS or SSA databases. Fraud that would be caught instantly through API verification passes through because the data doesn't match. These aren't edge cases. They are core design flaws in workflows built for paper documents.
Five Fraud Patterns Automated Intake Prevents
1. Synthetic Identity Fraud
Fraudsters combine real and fake information to create identities that don't exist but pass basic checks. A real SSN from a deceased person, a fake name, a real address. Automated intake prevents this by verifying identity documents against issuing authority APIs in real time. If the name on a driver's license doesn't match the name associated with that license number in the DMV database, the application is flagged immediately.
2. Document Forgery
Professional forgeries replicate security features well enough to fool visual inspection. Automated systems extract embedded data (PDF metadata, digital signatures) that forgeries can't replicate. A legitimate California mobile driver's license, for example, contains cryptographically signed data from the state. From Paper to Structured Data: The Missing Link in Government Digital Services explains how this extraction works and why it matters for verification workflows.
3. Benefits Stacking
Applicants use the same documents to apply for multiple benefits across agencies that don't share data. Automated intake creates structured records that can be checked against shared indexes. If the same SSN and supporting documents were used to open a claim in another state last week, the system flags it.
4. Income Misrepresentation
Applicants submit altered pay stubs or bank statements showing a lower income than their actual. Optical character recognition extracts dollar amounts, employer names, and account numbers. Those values can be validated against third-party databases (The Work Number for employment, early warning systems for bank accounts) before approval. Inconsistencies that would take weeks to discover through manual investigation are surfaced in seconds.
5. Ineligibility Fraud
Applicants provide documents showing they meet residency, citizenship, or other program requirements when they don't. Automated verification confirms addresses against postal databases, validates immigration documents against systems, and cross-checks age and relationship claims against vital records where agreements exist.
Zero-Data-Retention Architecture: Verify Without Storing
While the fraud prevention benefits of automated intake are well understood, its privacy implications are less clear.
Traditional document management systems store uploaded files indefinitely. Those databases become targets. A breach exposes thousands of driver's licenses, Social Security cards, birth certificates, and financial records. Zero-data-retention workflows can verify documents without storing them. The system extracts data, validates it against authoritative sources, records the verification result, and discards the document. The agency gets proof of eligibility. The applicant's documents aren't held.
This is how privacy-preserving verification works in some production systems today. The verification confirms "this person is over 21" or "this person is a California resident" without retaining the ID that proved it. Reducing Fraud Without Slowing Down Services walks through some of the technical implementation and policy framework that makes this possible.
From a fraud prevention standpoint, zero retention actually strengthens security. There's no database to compromise. Fraudsters can't social-engineer access to stored documents or exploit insider threats. The verification record proves eligibility, but it doesn't contain the materials to commit identity fraud.
Implementation Path: Adding Fraud Detection to Existing Workflows
You don't need to rebuild your entire benefits system to add automated fraud detection. Most agencies already have document upload portals. The question is what happens after the upload. Start by identifying the documents you currently require and the most common fraud patterns. For unemployment, that's identity documents and wage records. For Medicaid, it’s identity, proof of income, and residency. For SNAP, add household composition documents.
Next, map which of those documents can be verified automatically. State-issued IDs can be checked against DMV APIs. SSNs can be validated with SSA. Wage records can be confirmed through The Work Number or state employment databases. Not everything can be automated, but enough can to catch the majority of fraud.
Implement verification as a pre-screening step, before the application reaches a caseworker. Applicants upload documents. The system extracts data, runs verifications, and returns a result: verified, flagged for manual review, or rejected. Clear passes go straight to eligibility determination. Flags get human review, obvious fraud is rejected with an explanation.
This doesn't eliminate caseworkers. It lets them focus on cases that need judgment (unusual circumstances, incomplete documentation, conflicting information) rather than manually checking every document.
The technology is already here, and the standards are well established. What most agencies lack isn’t capability, it’s the procurement and policy pathways to deploy verification tools that protect privacy and reduce fraud.
What This Means for Program Integrity
Preventing fraud starts at intake, not after the fact. Systems that verify documents in real time, validate structured data, and avoid retaining sensitive materials can stop fraudulent claims before they move forward - rather than trying to recover funds later.
The trade-off is not between speed and security. It is between manual processes that are both slow and vulnerable, and automated processes that are efficient and verifiable. Eligible applicants are approved more quickly, while fraudulent attempts are stopped before a claim is submitted.
SpruceID partners with agencies to design and deploy verification systems that reduce fraud while protecting user privacy. If you’re modernizing intake or eligibility processes, get in touch to learn more.
Building digital services that scale take the right foundation.
About SpruceID: SpruceID builds digital trust infrastructure for government. We help states and cities modernize identity, security, and service delivery — from digital wallets and SSO to fraud prevention and workflow optimization. Our standards-based technology and public-sector expertise ensure every project advances a more secure, interoperable, and citizen-centric digital future.
