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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.  New Content!  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. ↩