Tag: verifications

The digital acceleration of the mortgage and rental industries has transformed how we verify income and employment—but it has also elevated the risks. As fraud grows in sophistication, lenders and verification providers alike must re-examine how they source, validate, and secure consumer data.  In this new landscape, real-time trust requires real-time data. That’s why Experian Verify (EV) has embraced a transactional, on-demand approach—often referred to as the “Go Fetch” model—which we believe is fundamental to building a safer, more resilient verification infrastructure.   Why Legacy Models Leave Gaps  Many verification providers still rely on a “data-at-rest” model, where employment or income data is stored indefinitely in static databases. This approach creates a prime target for attackers and increases the risk of data becoming outdated, incomplete, or even manipulated by bad actors. Reducing the number of places where employment data is stored significantly strengthens security.  Traditional models that maintain large databases can also introduce confirmation bias. They often send both the individual’s personally identifiable information (PII) and employer name to data partners, which can open the door to synthetic identities or fraudulent employer match backs. In fact, synthetic ID fraud accounted for 27% of business fraud cases in 2023, and by 2024, more than 70% of U.S. businesses identified deepfakes and AI-generated fraud as top threats. (https://www.experianplc.com/newsroom/press-releases/2024/new-experian-report-reveals-generative-ai--deepfakes-and-cybercr)  Some legacy verification providers still transmit both PII and employer details when requesting information. At Experian, we take a different approach: we search based on the consumer rather than the employer, and we pin—that is, cross‑check—the submitted consumer data against Experian credit file information to verify authenticity from the start.  Experian Employer Services maintains a secure copy of payroll data provided by our clients and updates it regularly. We have live, ongoing connections with employers and refresh data every two weeks directly from the source when payroll information is received. We never use stale data; every search pulls fresh, verified information.   The “Go Fetch” Model: Built for a Modern Threat Environment  In contrast, Experian Verify uses a real-time “Go Fetch” model, requesting data directly from the sources of truth at the time of the inquiry. No stale databases. No guessing games. This method reduces the window for fraud and ensures accuracy by design.   For each Experian Verify transaction, the following ‘Go Fetch’ approach and controls are applied:  Employment and income data are sourced in real-time with APIs from employers via Experian Employer Services (EES) and vetted payroll partners.  The PII data from the inquiry and the PII data returned from each data provider each undergo a pinning process, which cross-references the multiple PII data elements with Experian credit data to validate the identity of the individual and confirm the correct individual's data is being returned by each data provider, for each employment record returned.  Any income/employment data for which the second pin (based on data from the data provider) does not match the original first pin (from the inquiry) is disregarded to mitigate any risk of fat fingers/human error resulting in an incorrect consumer’s data on a VOIE report.  This multi-stage pinning process is more robust than a hard match on SSN and results in fewer errors. This not only minimizes the risk of bad data—it blocks it before it enters the pipeline.  More Than Technology: Trust Through Governance  Trustworthy data isn’t just about speed—it’s about the quality and integrity of the source. Experian Verify only partners with enterprise payroll providers and employers who pass rigorous onboarding and credentialing requirements to connect to Experian systems. This ensures we’re sourcing data from legitimate entities, helping prevent “fake employer” vectors used in synthetic employment schemes.  On top of this, data reasonability checks are run on every response, flagging anomalies like:  End dates before start dates  Net income exceeding gross income  Illogical or invalid birthdates  Any inconsistencies prompt an internal investigation, and where necessary, Experian Verify works directly with the data provider to resolve discrepancies—further reducing the propagation of fraudulent data.  Further, minimum field checks are performed on every response, which ensures the minimum data necessary is returned before delivering to the client. This helps provide an additional safeguard on the data received from Data Providers, providing reasonable assurance that the data delivered to clients can be used in their decisioning flow.    Industry Recommendation: A Call for Real-Time Integrity  As more lending moves online and fraudsters grow more creative, the verification industry must evolve. Experian advocates for a new standard, built on these principles:  Fetch data in real-time from sources of truth—don’t store it at rest.  Avoid employer name matching, which can inadvertently validate fake entities.  Validate PII match using multiple data elements instead of any hard match logic.  Automated reasonability & minimum field checks, monitored and investigated by human oversight for flagged issues.   Final Thought: Secure Growth Requires Secure Data  In an era where risk moves fast, stale data is a liability. Real-time models like Experian Verify’s “Go Fetch” approach do more than deliver speed—they help lenders make decisions with greater confidence, mitigate exposure to fraud, and ultimately, protect both borrowers and the institutions that serve them.  If trust is the foundation of lending, then real-time integrity must be the framework we build it on.   

Manual employment and income verification remain a persistent challenge in today’s digital-first financial ecosystem. Despite advances in technology, many organizations still rely on processes that are slow, fragmented, and vulnerable to fraud. These inefficiencies not only strain operational resources but also create friction for consumers seeking timely financial decisions.  Why Manual Income and Employment Verification Falls Short  Traditional income and employment verification methods often involve back-and-forth communication with employer HR departments, unclear documentation requirements, and delays that can stretch from hours to days. Beyond inconvenience, these processes introduce risks such as:  Inaccurate or incomplete data  Exposure to fraud through forged documents  Coverage gaps for gig workers and the self-employed  Operational inefficiency that diverts attention from higher-value tasks  As the workforce evolves—particularly with the rise of the gig economy—these shortcomings become even more pronounced.  Emerging Solutions: From Consumer Permission Data (CPD) to AI  The industry is responding with innovations that prioritize speed, security, and inclusivity:  Consumer-Permissioned Data (CPD): This approach allows individuals to securely share payroll data directly from their provider, reducing manual follow-ups and improving trust through consent-driven access.  Secure Document Upload: For workers without digital payroll systems, document upload offers a practical alternative. Pay stubs, W-2s, and 1099s can be submitted through secure portals, enabling verification for freelancers and small business owners.  AI-Enhanced Verification: Artificial intelligence adds a critical layer of protection and efficiency. Automated scanning detects anomalies, while fraud indicators such as tampered entries are flagged in real time—accelerating review and strengthening accuracy.  Why This Matters  The gig economy is projected to reach $2.145 trillion by 2033, underscoring the need for verification models that accommodate diverse income streams. By integrating CPD, document upload, and AI document verification, organizations can move beyond the limitations of manual employment verification toward systems that are:  Faster and more scalable  Resilient against fraud  Inclusive of non-traditional employment types  Looking Ahead  Manual income and employment verification may still have a role for businesses using niche payroll platforms, but the trajectory is clear: the future of employment and income verification is intelligent, consumer-driven, and built to scale. For lenders and verification providers, embracing these tools isn’t just about efficiency—it’s about setting a new standard for transparency and trust.   

In today’s evolving labor market, the employment screening landscape is undergoing a significant transformation. The traditional methods of verifying income and employment are being reimagined to keep pace with economic shifts, digital expectations, and the growing complexity of workforce dynamics. As organizations contend with an influx of applications, resume discrepancies, and evolving workforce structures, the demand for accurate, secure, and efficient verifications has never been more pressing.  A Workforce in Transition  The current employment environment is marked by a distinct shift toward lower-wage industries, which now account for nearly 88% of job growth in 2024. White-collar job creation, in contrast, has declined. Industries such as retail, staffing, food services, education, and healthcare are driving employment gains, while sectors like technology and professional services experience stagnation or contraction. (Experian, 2024)  Geographically, unemployment remains concentrated in regions impacted by remote work trends and industry-specific slowdowns. These changes in job distribution and employment types underscore the need for more adaptive and inclusive verification processes that can accommodate a broader spectrum of worker experiences—from traditional W-2 employees to gig economy participants.  The Verification Bottleneck  At the core of employment screening lies a critical step: verification. While often overlooked, verification has a profound impact on hiring outcomes, onboarding timelines, and organizational risk. The risks of poor verification—from hiring the wrong candidate to facing compliance pitfalls—are high. Resume inconsistencies are increasingly common, making robust verification processes essential to mitigate liability and protect organizational integrity.  Recruiters are also grappling with scale. Many employers report receiving thousands of applications, often from automated tools, creating noise and reducing the signal necessary to identify truly qualified candidates. In high-volume hiring environments, the absence of efficient screening tools can quickly lead to operational inefficiencies and hiring errors.  Modernizing Research Verifications  The industry is at an inflection point. Legacy methods of verification—manual phone calls, faxed documents, and mailed records—are no longer viable at scale. As a result, the sector has shifted toward instant digital verifications sourced directly from employers and payroll providers. These methods, supplemented by consumer-permissioned workflows, offer a scalable and more accurate alternative.  However, not all employees can be verified through instant or consumer-permissioned methods, especially those in small businesses or with multiple jobs. This is where research verifications, long considered a fallback option, are being reengineered.  Today, a digital-first approach is transforming research verifications into a strategic asset. This evolution includes multi-channel support: call centers for live interactions, online smart forms for asynchronous data entry, and conversational AI that guides users through the process intuitively. Such flexibility ensures that verifications are accessible, efficient, and reflective of how people communicate in the digital age.  Consumer Engagement as a Verification Tool  A key innovation in the verification space is the rise of consumer-permissioned access. These workflows empower individuals to authorize access to their payroll or earnings data directly—often through secure, embedded interfaces or mobile prompts. This not only broadens the verification net to include gig workers and contractors but also strengthens data integrity by retrieving information from the source.  Interestingly, many hourly and gig workers are already familiar with this kind of access, given their reliance on apps for earnings and scheduling. As comfort with these tools grows, so too does the potential for consumer-permissioned verifications to become a mainstream standard.  Nevertheless, it's important to acknowledge that not every candidate is willing or able to engage with digital verification methods. That’s why the ongoing development of research verifications remains critical. Ensuring that all candidates—regardless of role, industry, or digital fluency—can be verified effectively is essential to creating an equitable hiring process.  Toward a Holistic Verification Ecosystem  Looking ahead, the employment screening industry is poised to adopt a more comprehensive approach. Income and employment verifications are no longer standalone processes—they are part of a broader ecosystem that includes identity verification, fraud prevention, and compliance validation. Integrating these components through automation and modern digital infrastructure enhances both security and decision-making.  Organizations now play dual roles in this ecosystem: as both verifiers (providing information about current and former employees) and consumers (seeking data for new hires). This dual perspective fosters greater alignment around the need for transparency, efficiency, and data integrity.  The vision for the future is clear. Verification processes must be fast, flexible, and fair—capable of handling the complexity of today’s labor market without compromising on accuracy or candidate experience. By reimagining research verifications through the lens of innovation and inclusivity, the industry is not only solving present-day problems but also laying the groundwork for a more agile and resilient workforce infrastructure.   Explore the Future of Employment Screening  Want to dive deeper into the trends and innovations shaping modern employment verification? Watch the full webinar, Reimagining Research Verifications for Employment Screening, featuring industry experts from Experian. 👉 Watch the webinar now  Troy Huff, Director of Product Management, Experian Employer Services, Reimagining Research Verifications for Employment Screening webinar, 2024. According to Hoff, in 2024, nearly 88% of new job growth occurred in lower-wage industries, highlighting a significant shift in workforce composition post-COVID.         

By Joy Mina, Director, Product Commercialization  As the verification landscape evolves amid rising fraud and increasing demand for digital efficiency, a strategic reassessment of how we ensure data accuracy is no longer optional—it's imperative. In this environment, trust must be built not only in consumer identities but also in the datasets lenders use to make critical decisions. At Experian, we believe a thoughtful, layered approach to identity verification and data validation is key to building that trust.   Rethinking Data Confidence: Why Pinning Matters More Than Ever  The rise in synthetic identity fraud and employer misrepresentation has challenged traditional income and employment verification models. In fact, recent fraud studies show that synthetic identity fraud accounted for 27% of all fraud reported by U.S. businesses in 2023, with expectations of a surge in 2024 due to AI-generated deepfakes and evolving scams1. The consequences are not only financial—they also erode lender confidence in verification outcomes.  To help lenders meet these challenges head-on, Experian Verify™ employs a multi-step, comprehensive PIN approach that leverages our vast credit and verification data ecosystems to validate both the who and the what of every piece of data associated with a Verification transaction.   The Mechanics of Dual Pinning: More Than Just Matching  1st Pin – Verifying Identity with Credit Bureau Rigor  When a verification inquiry is submitted, Experian Verify uses advanced PII (personally identifiable information) search algorithms to confirm the individual exists within Experian’s credit database. The "PIN" refers to a unique person identification number that is assigned to each consumer within Experian's Credit ecosystem. If the consumer cannot be "pinned," the verification transaction stops, and no data is returned. This not only protects the lender from fraudulent inquiries but also prevents invalid results from progressing through the pipeline.  2nd Pin – Verifying Data Belongs to the Same Individual  This is the stage where the industry often struggles. Other providers may stop after a single PII match—commonly a Social Security Number. But with increasing risks of misattributed or incomplete data and a growing number of state regulations requiring more than just SSN matches, that's no longer sufficient. Further, most Data Providers sourcing the data into the Verifications ecosystem have the flexibility to define their own consumer match logic or may even use “fuzzy” matching logic, which exposes both the client and the distributing partner to the risk of matching the wrong consumer without additional, redundant controls to confirm the identity of the consumer records returned.  Experian Verify not only pins the PII from the lender but also pins the PII data received back from each data source (employer or payroll provider) and employment record. For each data source, the PIN must match the original inquiry PIN for data from that source to make it onto an Experian Verify report. A mismatch may indicate that the PII from the data source may not be for the same consumer as the initial inquiry—ensuring the final report contains only information with a high confidence match.  This process mitigates risk and protects lenders from intentional or unintentional fraud. For example, if a consumer were to apply for a loan and accidentally enter an incorrect SSN (or other PII), the legacy method of hard matching on SSN would result in data from the wrong consumer being returned from the verifications provider.  Experian Verify avoids this by a redundant and secure design:  Multiple PII data elements are used to search and retrieve a PIN  The PII from the lender is pinned  The PII returned in the data payload from each data source is pinned  The consumer PIN from the lender must match that of a data source for data from that source to be used in a Verify report  This multi-step, comprehensive pin method provides an essential safeguard in an industry where even minor data discrepancies can have major implications.   Industry Comparison: Moving Beyond Minimal Match Models  According to Arizent Research, 95% of mortgage lenders say “completeness of data” and “speed to decision” are critical priorities, but many still rely on verification systems that use basic or single-element hard matching 2. That exposes both lenders and borrowers to greater risks of misidentification or fraudulent records.  Experian’s PIN Algorithm requires a minimum of three data elements (e.g., Name, SSN, and DOB), enhancing accuracy and reducing false positives—even when data entry errors occur. It's a foundational practice we believe should become standard across the industry.   Why This Matters in Today’s Mortgage Climate  With the Federal Housing Finance Agency (FHFA) approving new models like VantageScore 4.0 and FICO 10T, the industry is moving toward broader, more inclusive underwriting standards—many of which rely on data beyond traditional credit 3. That includes rental history, income trends, and even employment stability. But the promise of these expanded datasets can only be realized if the data itself is reliable.  Experian’s investment in redundant identity pinning and advanced search algorithms is part of a broader strategy to bring clarity, accuracy, and trust to the verification process—especially as digital lending ecosystems scale.   Looking Ahead: Recommendations for Industry Best Practice  To help move the industry forward, we propose three pillars of verification best practice:  Mandate Multi-Layer Identity Validation – A single hard match on PII data elements isn’t enough. Multi-factor validation should be the norm and ensure that all data on a VOIE report goes through the same level of validation.  Go beyond data provider identity validation – Many data providers will return income and employment data based on hard matches, often using only 1 or 2 data elements. While we like to trust, we always verify and ensure the data meets Experian’s standards.  Insist on Data Accountability – Only include verified, matched data in reports. Inaccurate data should be filtered out by design, not exception.  Adopt Scalable, Real-Time Tools – Instant verifications save time but must be paired with controls that preserve data integrity.   Conclusion: Building a Safer Verification Ecosystem  Verification is no longer just a checkbox on a loan application—it's a critical part of credit risk, borrower experience, and fraud prevention. As fraud methods become more sophisticated, verification providers must lead with transparency, data integrity, and advanced identity science.  Experian Verify’s pinning methodology is not just a competitive differentiator—it’s a blueprint for where the industry should go next.   Footnotes   Let me know if you’d like this formatted into a formal PDF or published as a blog with visuals.  Footnotes  Experian State of the U.S. Rental Housing Market Report 2025, pg. 15: Synthetic identity fraud accounted for 27% of all fraud types reported by U.S. businesses in 2023, with rising concern about AI-generated fraud in 2024. ↩  Arizent / National Mortgage News Whitepaper (2024): 95% of mortgage lenders rated “data completeness” and “speed to receive data” as critical or highly important when selecting a VOIE solution. ↩  Federal Housing Finance Agency (FHFA) News Release, Oct 24, 2022: FHFA validated and approved both VantageScore 4.0 and FICO 10T for use by Fannie Mae and Freddie Mac. Implementation date to be announced. ↩