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  As a follow‑up to our January post on Freddie Mac’s Loan-Level Directed Collateral (LLDC) program and its use of new loan‑level data fields from Experian’s Mortgage Loan Performance (MLP) dataset, we’re highlighting another newly available field: current second lien balance.  What kind of data moves markets?  Before diving into the new second lien field, we’ll outline the criteria we use to determine whether a new data field has the potential to move MBS markets—and therefore warrants the time and effort required to prepare and deliver it to our institutional investor clients.  These criteria will apply to all new fields discussed in future posts.   Over the past decade, rapid technological innovation, combined with financial markets’ increasing focus on data and AI, has led to a steady stream of new market data and analytical products. Most of these releases don’t materially impact how MBS trade. As discussed in prior posts, two notable exceptions stand out:  The introduction of pool‑level data in the 1980s enabled the rise of specified (“spec”) pools.  The public release of agency MBS loan‑level data in 2013 ushered in a new era of advanced analytics and precision modeling.   So, what criteria must be met for new, incremental data to change how MBS trades? We believe three requirements must be met:  New: Provides information not available in existing datasets (i.e., orthogonal to currently available data).  Material: Impacts a sizeable portion of the MBS universe.  Significant: Differentiates collateral performance by a large enough margin to influence trading and risk management decisions.  With these criteria in mind, we turn to one of several new fields from Experian’s MLP that meet all three: current second lien balance.  Subsequent Second Liens: An ‘Invisible’ CPR Throttle  MLP contains several fields related to open second liens, with each loan linked to both the individual borrower and the specific property. This structure allows visibility into a borrower’s full set of open second lien loans, even across multiple properties. For the illustrative exercise below, we focus on one field: the total balance on open second‑mortgage trades reported in the past three months.  Does this field meet the first criteria—New? Yes, the current presence of junior liens is new information in agency MBS markets. In standard agency and Government National Mortgage Association (GNMA) disclosures, second‑lien information appears only at the time of first‑lien origination. Any subsequent second liens remain unreported, preventing accurate calculations of current combined LTV post-origination.  The material blind spot: Missing junior‑lien data   The absence of updated junior lien status represents a material blind spot for investors seeking to predict prepayment behavior of the associated first lien in agency MBS. Current combined LTV, inclusive of subsequently opened second liens and adjusted for home price appreciation (HPA), is one of the most important drivers of both prepayment and credit performance. Without supplementary data from MLP, information on newly originated second liens go unobserved. As a result, prepayment and credit forecasts become overly aggressive, and prepayment call protection is therefore mispriced.    In addition to information regarding the junior lien loan, Experian’s MLP dataset includes a monthly refreshed AVM value for each property, ensuring an accurate current CLTV value. Having established newness, is current junior lien data material?  Yes, particularly in the current environment of record-high home equity. Approximately 16% of active mortgages carry second liens, representing roughly $522 billion in outstanding balances—and growing (Source: Experian MLP dataset). In 2024 alone, second-lien originations exceeded $100 billion and continued to trend upward (Source: Experian MLP dataset).  Second liens added after primary‑mortgage origination, often layered onto low‑LTV agency MBS, aren’t captured in standard GSE data. Their impact is especially pronounced in periods of moderate or negative HPA. Borrowers who take on new second liens and then experience negative HPA may be unable to refinance due to re‑subordination limits, which materially affect prepayment behavior and call protection. Investors relying on standard agency disclosure have no visibility into post‑origination junior liens.  Is current junior‑lien data significant?  After having established newness and materiality, is the current junior lien data significant?     Yes—Figure 1 illustrates the impact of new second-lien balances on prepayments. This field is independent of other collateral characteristics available in standard GSE data, as the decision to take out a new second lien is made by the borrower after the primary mortgage has closed.  As shown in Figure 1, prepayments decline materially as new second-lien balances increase. On average, if approximately 20% of mortgages carry second liens and the CPR differential for in-the-money (ITM) mortgages with and without new second liens are 10 CPR, then new second liens account for roughly 2 CPR of prepayment impact on average (10 CPR × 20%).  This CPR-throttling effect is significantly more pronounced for mortgages with a current CLTV of around 80%. These loans may be effectively locked out of refinancing due to re-subordination constraints, yet they appear highly callable when evaluated using only standard GSE data, leading to materially overstated prepayment expectations.  Fig 1. Prepayments S-Curve: New Second Liens Balance Source: Experian Mortgage Loan Performance Dataset, hosted on the IVolatility MBS Data-Driven Portal  _____________________________________________________ Michael Pyatski advises MBS traders, portfolio managers, quants, risk managers, loan originators, and technology professionals on making informed, data-driven business decisions that drive revenue growth, enhance risk management, and reduce trading costs. With more than 15 years of experience as an Agency RMBS trader—including serving as Head of the Proprietary Trading Desk at BNP Paribas—Michael developed and successfully implemented relative-value, data-driven profitable trading strategies to capture market opportunities embedded in data but not fully priced by the market. His trading experience, combined with a Ph.D. in econometrics, led him to found the Data-Driven Portal (https://datadrivenportal.com/), a platform that provides advanced technology for MBS trading and risk management. The platform’s No-Model Data-Driven technology leverages big data, econometric analysis, and AI to help traders identify relative-value opportunities in RMBS markets and generate above-market, risk-adjusted returns.   _____________________________________________________  

  As vehicle prices and interest rates continue to evolve, both consumers and lenders are recalibrating their approaches to affordability and long-term sustainability. This shift has resulted in the subprime segment growing to its largest share of total finance market for subprime in the fourth quarter since 2021. According to Experian’s State of the Automotive Finance Market Report: Q4 2025, subprime borrowers accounted for 15.31% of total vehicle financing, an increase from 14.54% in Q4 2024. To understand why the subprime space is evolving, we took a deeper dive into the affordability picture and how changes in pricing and interest rates are influencing both consumer decisions and lender strategies. In Q4 2025, the average loan amount for a new vehicle increased $1,882 from the prior year to $43,582, and the average interest rate for a new vehicle went from 6.34% last year to 6.37% this quarter. As a result, the average monthly payment increased from $746 to $767 in the same time frame. On the used side, the average loan amount increased $872 year-over-year, reaching $27,528 in Q4 2025. However, despite the average interest rate declining from 11.63% to 11.26% during the same time, the average monthly payment grew $9 from last year to $537 this quarter. These changes are prompting thoughtful adjustments across the automotive ecosystem. Consumers are comparing financing options more carefully and adjusting loan terms when necessary to prioritize the cost of ownership. Lenders are also focusing more on payment flexibility and how long-term borrowers are performing as they leverage it for central pillars of strategies to stay ahead of the ever-evolving market. To learn more about automotive finance trends, view the full State of the Automotive Finance Market Report: Q4 2025 presentation on demand.

In May 2024, new guidelines were proposed by the Consumer Financial Protection Bureau (CFPB)  that would require Buy Now, Pay Later (BNPL) providers to share consumer payment information with credit reporting agencies (CRAs). Although data reporting about BNPL activity isn’t mandated through the Fair Credit Reporting Act, issuers have begun reporting payment information to CRAs.  While this proposed guidance from the CFPB focuses on BNPL activity, it signals a broader shift in how the credit landscape is evolving—particularly for lenders relying on a holistic view of consumer financial behavior.  As of June 2025, the CFPB has stated it isn’t enforcing its guidance on BNPL activity and it isn’t issuing new guidance.The direction is clear: credit evaluations are moving toward a more complete picture of how people manage their everyday spending and credit obligations. This includes recognizing consistent patterns in recurring, nontraditional payments—such as BNPL installments, rent, and utilities—and ensuring these behaviors can be factored into credit-related decisioning models to afford consumers appropriate recognition of their financial handling, as well as giving credit granters a comprehensive view.  BNPL reform mirrors the mortgage market’s credit overhaul  BNPL activity is increasingly being evaluated using the same reporting standards as other forms of consumer credit. This shift reflects a broader transformation in how lenders assess financial behavior.  Modern credit evaluations place greater emphasis on trended data that shows patterns in how consumers have used and managed credit over time. They also incorporate alternative payment history—such as rental and utility payments—creating a more complete view of a consumer’s financial habits. These approaches have demonstrated stronger predictive performance, particularly for individuals with limited traditional credit history.  Overall, the direction of credit evaluation is moving toward broader data inclusion, both historical and real-time, for a more holistic, consumer‑centric assessment.  Although the CFPB has delayed implementation of these models, the direction is clear. Credit scoring is moving toward broader data inclusion and more accurate, consumer-centric evaluation.  Why mortgage lenders should care about BNPL rules  Modern expectations for credit evaluations are shifting toward a more complete, past-and-present view of consumers’ everyday financial behaviors. This applies across lending decisions, including those made in the mortgage ecosystem.  When consistent patterns—such as on‑time rent payments, responsible installment management, and steady cash‑flow habits—are visible, they can help create a clearer picture of an individual’s financial reliability. These signals are becoming increasingly important, especially as more future homebuyers have limited traditional credit histories.  Consider the impact of incorporating rental payment data:  More than 83% of consumers who had rental payments included in their credit files saw an improvement in their scores  15.1% of those individuals were previously unscoreable and gained a score  On average, consumers saw a 3.9% score increase after rental data was added  As more people use BNPL services and other nontraditional financial tools, it becomes increasingly important for mortgage lenders to evolve their evaluation inputs to reflect how consumers manage their financial lives today.  How mortgage lenders can prepare  With mounting regulatory and industry pressure, lenders need to move from passive observation to proactive implementation. Here’s how to begin:  1. Adopt the Experian Score Choice Bundle This solution provides both FICO 2 and VantageScore 4.0 on every mortgage transaction at no additional cost. It allows lenders to:  Compare and test new models without operational disruption  Maintain compliance with GSE guidelines  Serve more borrowers by evaluating modern credit behaviors  2. Score cash flow with decision-grade rigor Plaid captures the data; Experian turns it into decision‑ready insight. Experian’s Cashflow Attributes and Cashflow Score provide:  Decision-grade scoring built on permissioned transaction data  Clear reason codes for explainability  Stronger predictive lift backed by portfolio testing  With a growing majority of high-volume mortgage originators now implementing digital income and employment verification tools like Experian Verify, the industry is rapidly transitioning toward automated, real-time lending. As Experian positions it: “Plaid collects the signal. Experian makes it decision-ready.”  3. Align with market and regulatory trends  The FHFA’s shift to VantageScore 4.0 and FICO 10T, along with the emergence of cash flow payloads, signals that credit reporting is entering a new phase. Lenders proactively modernizing their credit strategies will be positioned to:  Expand access to credit for millions of underserved but creditworthy consumers  Reduce risk through more complete borrower insights  Stay ahead of compliance and investor expectations  With over 53% of high-volume mortgage originators already using Experian Verify, the industry is beginning to embrace this transformation. Broader adoption of inclusive scoring and permissioned data remains a critical next step.  Final thought  The CFPB’s action on BNPL, while not enforced at the moment, is not an isolated event—it is a preview of the future. The mortgage industry must prepare now for a world where rent, cash flow, and alternative financial behavior shape the foundation of credit scoring. Lenders who act early will not only meet regulatory expectations but will gain a strategic advantage in serving tomorrow’s homebuyers.  Experian is ready to support this shift with data, tools, and scoring models built for the next era of mortgage lending.  Start testing modern credit scoring strategies now—and let Experian show you the lift on your borrower population.   

As the U.S. rental housing market moves through 2026, renters, landlords, and property management companies face an increasingly complex operating environment. Elevated housing costs, economic uncertainty, slowing construction activity, and a rapidly evolving fraud landscape are converging to reshape both risk and opportunity across the rental ecosystem.   At the same time, advances in data, analytics, and verification technologies are equipping housing professionals with new tools to adapt — shifting decision‑making from reactive to proactive at a moment when precision matters most.  Mortgage rates continue to constrain housing mobility  One of the most significant structural forces supporting rental demand remains the cost of homeownership. In early 2025, the average 30‑year fixed mortgage rate hovered near 7%, with Freddie Mac’s weekly survey reporting a rate of 7.04% for the week of January 16, 2025. The report also showed the year beginning near 7% before ending at 6.15% (Freddie Mac, 2025a, 2025b).    This environment has created a pronounced lock‑in effect: homeowners with pandemic‑era low fixed mortgage rates are reluctant to sell, limiting for‑sale inventory and suppressing turnover (Federal Housing Finance Agency [FHFA], 2024; Bankrate, 2025). For renters, this results in longer tenures and fewer pathways to homeownership. For landlords and lenders, it reinforces expectations that rental demand will remain elevated well into 2026, even if mortgage rates ease modestly.  Rental housing supply faces structural constraints  Despite strong rental demand, rental housing supply growth remains uneven. Multifamily development has slowed as financing costs and construction expenses have risen. Industry data indicate that multifamily units under construction fell roughly 20% year over year by early 2025, while completions have outpaced new starts—approximately 1.5 apartments completed for every one that begins construction on a three‑month moving‑average basis (Nanayakkara Skillington, 2025). Forecasts from Yardi Matrix pointed to elevated completions in 2025, followed by a notable slowdown in 2026, with starts continuing to slump (Dale, 2025). Absent a meaningful acceleration in new construction, these dynamics are likely to sustain pressure on rents and intensify affordability challenges, particularly in high‑growth and high‑migration markets (Joint Center for Housing Studies, 2025).  Fraud risk is escalating in a digital-first rental market  As rental transactions increasingly move online, fraud has become a fast‑growing operational risk for property managers and owners. The Federal Trade Commission’s Consumer Sentinel data show sustained reports of identity theft and imposter scams (Federal Trade Commission [FTC], 2024), while industry surveys identify account takeover, payment fraud, and synthetic identities as some of the most frequently encountered issues (Experian, 2023). From 2024 to 2025, housing and real estate professionals reported rising exposure to AI‑enabled schemes—including deepfake voices, manipulated documents, and increasingly sophisticated application fraud (Housing Wire, 2025; Veriff, 2025; First American, 2025).  As digital leasing accelerates, robust identity verification and fraud prevention have become core components of sustainable portfolio management. FTC Consumer Sentinel data continue to highlight persistent patterns of identity theft and imposter scams (FTC, 2024), and industry research consistently shows that account takeover, payment fraud, and synthetic identities remain significant operational threats (Experian, 2023). Between 2024 and 2025, housing professionals noted a growing prevalence of AI‑enabled fraud techniques, such as deepfake audio, falsified documents, and advanced application manipulation (HousingWire, 2025; Veriff, 2025; First American, 2025).  Data and analytics are becoming the defining advantage  Access to high‑quality data and real‑time insights is increasingly decisive. Data‑driven solutions enable rental housing professionals to move beyond static screening and manual processes, supporting continuous risk assessment and smarter decision‑making. These capabilities allow housing providers to evaluate applicants and portfolios with greater accuracy, reduce operational friction, and respond more proactively to emerging risks—making data and analytics a defining advantage across the rental housing ecosystem.  Rent reporting as a credit building and risk signal building and risk signal  Rental payment history has emerged as a valuable indicator of consumer financial behavior. Surveys and evaluations show strong renter interest in having on‑time rent payments included in credit scores, and many participants experience measurable benefits. For example, Fannie Mae reports that more than 80% of renters want rent payments factored into credit scoring models (Fannie Mae, n.d.). Randomized trials also demonstrate increased credit visibility and movement into near‑prime tiers for previously unscorable consumers (Theodos, Teles, & Leiberman, 2025; Credit Builders Alliance, 2025). For property managers and owners, this creates a dual benefit: renters gain meaningful credit‑building opportunities, while housing providers gain a deeper, more reliable signal of payment behavior beyond traditional credit files.  Smarter screening and verification  Income and employment verification remain among the most critical—and historically inefficient—steps in the rental lifecycle. Digital verification tools that leverage payroll and employment databases, along with consent‑based bank data, significantly reduce friction, deliver faster decisions, and help mitigate fraud by validating applicant information at the source (Truework, 2024; MeasureOne, n.d.; U.S. Government Accountability Office [GAO], 2025). As application volumes rise, automated verification is becoming a baseline requirement rather than a competitive differentiator. These tools enhance accuracy, streamline workflows, and strengthen fraud prevention—capabilities that are increasingly essential as application tactics grow more advanced.  What to watch as the market moves into 2026  Looking ahead, three trends are likely to shape the rental housing market over the next 12 to 18 months:    Sustained rental demand amid elevated mortgage rates and constrained for‑sale inventory, as higher borrowing costs continue to limit mobility and suppress housing turnover (Freddie Mac, 2025a; Federal Housing Finance Agency [FHFA], 2024).    Widening affordability gaps, with rent‑to‑income pressures intensifying—particularly in high‑cost and high‑growth regions (Joint Center for Housing Studies, 2025).    Data‑driven decision‑making is becoming standard across screening, pricing, fraud prevention, and portfolio monitoring, reflecting broader industry adoption of automated tools and analytics (U.S. Government Accountability Office [GAO], 2025; Snappt, 2025).   Final perspective  The U.S. rental housing market in 2026 is defined by both complexity and opportunity. Success will depend on the ability to adapt quickly, manage risk proactively, and deploy data‑driven solutions with precision. For renters, tools such as rent reporting offer pathways to greater financial stability and transparency. Ultimately, this moment is about resilience, readiness, and the systems that will shape rental housing outcomes well into the next cycle. Organizations that invest now in smarter data, stronger controls, and forward‑looking strategies will be best positioned to navigate what comes next—for themselves and for the broader rental housing ecosystem. 

Utilities are managing elevated arrears, expanding digital service channels and shifting grid demand patterns at the same time. These developments are appearing at key points, including service starts, billing and collections. Energy and utilities industry trends for 2026 reflect how these dynamics are surfacing across the customer lifecycle and influencing broader planning decisions.  Energy and utilities trends shaping the industry The state of energy and utilities 2026 reflects a sector adapting to financial exposure, fraud risk and demand variability across both regulated and deregulated markets. Rising arrearagesArrearage levels across the utilities sector are estimated at approximately $23 billion. Economic uncertainty may be contributing to a rise in arrearages, often reflected in delayed payments, extended repayment plans or variability in monthly collections. Digital expansion introduces new risk considerationsAs utilities expand digital service channels and self-service tools, identity-based fraud risk may appear during digital service starts and account changes, particularly as more interactions shift online. Fraud behaviors are becoming more sophisticatedMore complex fraud patterns, including synthetic identities, name game fraud and prior bad debt, may span multiple points of the customer journey, making risk more difficult to detect. Grid demand uncertaintyIn certain regions, data center expansion may influence load forecasting and long-term infrastructure planning timelines. Data centers consumed approximately 4.4% of U.S. electricity in 2023 and are projected to account for between 6.7% and 12% by 2028, reflecting the potential scale of demand shifts utilities may be evaluating. What these trends signal for utility planning Together, these energy and utilities industry trends 2026 highlight where risk could first emerge. When risk indicators appear during service start, screening before service starts may help reduce downstream exposure rather than relying only on collections-based controls. As more interactions shift online, identity risk may be harder to identify without stronger verification. When fraud spans from service start through collections, visibility across systems becomes more important. As grid demand grows, planning for reliability may require adjustments to how forecasting and infrastructure decisions are informed. Enabling data-driven utility decisions To navigate these energy sector trends, utilities may benefit from a more connected view of identity, risk and customer behavior. Experian supports providers with data-driven energy and utilities solutions designed to help reduce losses, strengthen customer trust and support utility fraud prevention across the customer lifecycle. For a closer look at how these themes are unfolding across the sector, explore our 2026 State of Energy and Utilities Report, which examines each trend in greater depth through data-driven insights and industry examples. Read our first-ever State of Energy and Utilities Report examining the forces shaping the industry this year. Download now

Across agencies, decisions about digital services, staffing and oversight are often tied together. Public sector trends for 2026 reflect how these considerations are shaping modernization efforts and citizen trust today. At the federal, state and local levels, the public sector outlook 2026 highlights how modernization, program integrity, workforce resilience and citizen trust influence how services are delivered and how resources are prioritized. Four trends shaping the public sector in 2026 Agencies are navigating a set of trends that are influencing both strategic planning and day-to-day execution. Fiscal pressure and program integrityBudget volatility and increased scrutiny may elevate the importance of payment accuracy and operational consistency, particularly as eligibility rules evolve and caseloads remain high. This can surface in areas such as eligibility verifications, benefits recertifications or grant administration, where data inconsistencies may have a broader operational impact. Modernization and technology accelerationAs agencies continue public sector modernization, digital access may expand faster than existing controls can keep pace. This is often most visible in online applications, self-service portals and account management tools, where verification processes may not evolve at the same pace as access. Fraud losses across the U.S. have been estimated at approximately $160 billion, highlighting the extent of identity and payment risks present in digital environments. Decisions about identity assurance and fraud prevention can influence how agencies scale online services. Workforce resilienceStaffing constraints and skill gaps may affect processing timelines, oversight capacity and institutional knowledge, potentially contributing to longer review cycles or greater reliance on manual quality checks. Workforce data shows roughly 200,000 federal positions were reduced in the past year, which may influence how agencies approach automation and oversight. Automation and government data analytics can play a more central role in supporting consistency across programs. Citizen trust and digital experienceAs more interactions move online, citizen trust may be influenced by both security and usability. Public sector fraud prevention approaches that apply friction only when risk indicators are present can help agencies maintain accessibility while managing exposure. What these signal for agencies Together, these trends point to a shift in how agencies evaluate risk and prioritize investment. Choices about modernization, staffing and oversight may increasingly shape one another. Approaches that strengthen government program integrity, improve visibility across digital interactions and support informed decision-making may help agencies sustain service levels while managing evolving risk. For a closer look at how these trends are unfolding across agencies, explore our 2026 Public Sector Trends and Impact Report, which delves into each theme in greater depth through data-driven insights and real-world agency use cases. Read our first-annual 2026 Public Sector Trends & Impact Report to understand the forces reshaping agency operations and trust. Download now

Mortgage rates remain elevated by historical standards: the average 30-year fixed rate ended 2025 at 6.15% (Freddie Mac’s PMMS), after spending much of the year closer to 7% (52-week high ≈ 7.04%) (Freddie Mac, 2025; Mortgage News Daily, 2025). At the same time, the Federal Reserve’s December 2025 Summary of Economic Projections signaled a modest easing path into 2026 (median fed funds projection 3.4% at end-2026), reinforcing expectations of lower borrowing costs ahead rather than an immediate return to pre-2022 conditions (Federal Reserve, 2025). Affordability pressures persist and vary widely by metro and region: rent-to-income ratios in many Midwestern markets are below 30%, while parts of the Northeast (e.g., New York City) exceed 50% of income for a typical renter household (Moody’s Analytics, 2023; 2025). Given this fragmentation, national averages no longer provide sufficient guidance. Lenders need a data-driven playbook that translates insight into action across the lending lifecycle.  Pillar 1: Borrower insights  Today’s renter profile skews younger: Gen Z already accounts for ~30.5% of renters and, together with younger millennials (under 35), represents over half of the rental population (Experian, 2025). Zillow’s Consumer Housing Trends Report similarly shows Gen Z makes up 25% of all renters and 47% of recent movers—evidence that the next cohort of first-time buyers is emerging from today’s rental pool (Zillow Research, 2024). Traditional credit files can miss reliable payment behavior. Both Fannie Mae and Freddie Mac now consider positive rent payment history in automated underwriting—using bank or payroll-verified data to augment limited credit histories—improving access for qualified renters (Fannie Mae, 2025; Freddie Mac, 2025). Data-driven edge: Broader borrower views—incorporating verified rent payments, student loan performance, and alternative credit signals—help identify “hidden prime” consumers and responsibly expand the addressable market.  Pillar 2: Operational efficiency  Margin pressure is persistent, and manual income/employment verification remains a top pain point: manual methods can take 30 minutes to several days, raise costs, and increase drop-offs (MeridianLink, 2025). Modern VOE/VOI solutions—e.g., Mastercard Open Finance (Finicity/Argyle), Truework—deliver GSE-accepted digital verifications that reduce friction, lower per-loan costs, and provide rep/warranty relief when validations succeed ( Mastercard; Business Wire/Morningstar). Data-driven edge: Verification and documentation automation enables speed, consistency, and scalability without proportional staffing or risk increases.  Pillar 3: Geographic precision  Affordability is deeply local. The national rent-to-income ratio has recently eased back toward ~27–30%, but disparities persist: several Midwest markets track below 30%, while New York City reaches ~67% and Miami exceeds 40% (Moody’s Analytics, 2023; 2025). Recent rent reports also show metros like Miami ranking as least affordable and others (e.g., Austin) more affordable for typical renter incomes, underscoring the need for metro-level targeting (Realtor.com, 2025). Data-driven edge: Market-level data—local affordability, migration, inventory, and labor trends—helps focus growth where demand is most likely to convert and perform over time.  Pillar 4: Refinance readiness  Refinance activity is muted but not gone. With rates dipping from 2025 highs, millions are positioned to benefit: as of Nov. 2025, about 4.1 million mortgage holders were “in the money” (≥ 75 bps savings), including 1.7 million highly qualified candidates; the cohort could grow toward ~5 million with small additional rate declines (ICE Mortgage Technology, 2025). Homeowners also held $11.2 trillion in tappable equity entering Q4 2025, supporting additional refinance and home-equity lending opportunities (ICE Mortgage Technology, 2025). Data-driven edge: Segment portfolios by rate sensitivity, pre-model operational capacity, and streamline digital processes to capture volume quickly while preserving experience.  Bringing it together  These four pillars—borrower insights, operational efficiency, geographic precision, and refinance readiness—form a unified framework for outperforming peers in today’s housing market. Lenders that operationalize this approach will be better positioned to: • Serve more borrowers responsibly by leveraging verified rent and payroll data to expand access (Fannie Mae; Freddie Mac). • Manage risk with greater precision through automated verifications and underwriting validations (Mastercard). • Build sustainable regional strength by deploying resources in metros where affordability and demand align (Moody’s; Realtor.com). • Capture refinance demand at scale as candidates and tappable equity expand when rates ease (ICE Mortgage Technology). The housing market is shifting—not back to what it was, but toward something more fragmented and data-dependent. Lenders who build strategy on insight rather than instinct will define the next generation of market leaders.  References  Experian. (2025, January 10). The shifting demographics of today’s renters. https://www.experian.com/blogs/insights/the-shifting-demographics-of-todays-renters/  Federal Reserve Board. (2025, December 10). Summary of Economic Projections (Table PDF). https://www.federalreserve.gov/monetarypolicy/files/fomcprojtabl20251210.pdf  Freddie Mac. (2025, December 31). Primary Mortgage Market Survey® (PMMS®) weekly data. FRED series MORTGAGE30US. https://fred.stlouisfed.org/series/MORTGAGE30US/  Fannie Mae. (2025, January). FAQs: Positive rent payment history in Desktop Underwriter. https://singlefamily.fanniemae.com/originating-underwriting/faqs-positive-rent-payment-history-desktop-underwriter  ICE Mortgage Technology. (2025, November 10). November 2025 Mortgage Monitor (press release & report). https://mortgagetech.ice.com/resources/data-reports/november-2025-mortgage-monitor  Mastercard. (2024, June 25; updated September 23, 2024). How data-enabled income and employment verifications deliver smarter, seamless financial experiences. https://www.mastercard.com/us/en/news-and-trends/Insights/2024/data-enabled-income-and-employment-verifications-deliver-smarter,-seamless-financial-experiences.html  MeridianLink. (2025, April 8). Instant verification: Rethinking income and employment tools. https://www.meridianlink.com/blog/its-time-to-take-a-new-look-at-income-and-employment-verification-tools/  Moody’s Analytics. (2023, November 27). 30% of income on rent remains the norm in U.S. metros (Data story). https://www.moodys.com/web/en/us/insights/data-stories/q3-2023-us-rental-housing-affordability.html  Moody’s CRE. (2025, March 11). Q4 2024 housing affordability update. https://www.moodyscre.com/insights/cre-trends/q4-2024-housing-affordability-update/  Mortgage News Daily. (2026, January 2). Freddie Mac mortgage rates—weekly survey (historic table). https://www.mortgagenewsdaily.com/mortgage-rates/freddie-mac  Realtor.com Economics. (2025, October 14). September 2025 rental report: Rental affordability improved compared to a year ago. https://www.realtor.com/research/september-2025-rent/  Zillow Research. (2024, October 14). Renters: Results from the Zillow Consumer Housing Trends Report 2024. https://www.zillow.com/research/renters-housing-trends-report-2024-34387/ 

For many banks, first-party fraud has become a silent drain on profitability. On paper, it often looks like classic credit risk: an account books, goes delinquent, and ultimately charges off. But a growing share of those early charge-offs is driven by something else entirely: customers who never intended to pay you back. That distinction matters. When first-party fraud is misclassified as credit risk, banks risk overstating credit loss, understating fraud exposure, and missing opportunities to intervene earlier.  In our recent Consumer Banker Association (CBA) partner webinar, “Fraud or Financial Distress? How to Differentiate Fraud and Credit Risk Early,” Experian shared new data and analytics to help fraud, risk and collections leaders see this problem more clearly. This post summarizes key themes from the webinar and points you to the full report and on-demand webinar for deeper insight. Why first-party fraud is a growing issue for banks  Banks are seeing rising early losses, especially in digital channels. But those losses do not always behave like traditional credit deterioration. Several trends are contributing:  More accounts opened and funded digitally  Increased use of synthetic or manipulated identities  Economic pressure on consumers and small businesses  More sophisticated misuse of legitimate credentials  When these patterns are lumped into credit risk, banks can experience:  Inflation of credit loss estimates and reserves  Underinvestment in fraud controls and analytics  Blurred visibility into what is truly driving performance   Treating first-party fraud as a distinct problem is the first step toward solving it.  First-payment default: a clearer view of intent  Traditional credit models are designed to answer, “Can this customer pay?” and “How likely are they to roll into delinquency over time?” They are not designed to answer, “Did this customer ever intend to pay?” To help banks get closer to that question, Experian uses first-payment default (FPD) as a key indicator. At a high level, FPD focuses on accounts that become seriously delinquent early in their lifecycle and do not meaningfully recover.  The principle is straightforward:  A legitimate borrower under stress is more likely to miss payments later, with periods of cure and relapse.  A first-party fraudster is more likely to default quickly and never get back on track.  By focusing on FPD patterns, banks can start to separate cases that look like genuine financial distress from those that are more consistent with deceptive intent.  The full report explains how FPD is defined, how it varies by product, and how it can be used to sharpen bank fraud and credit strategies. Beyond FPD: building a richer fraud signal  FPD alone is not enough to classify first-party fraud. In practice, leading banks are layering FPD with behavioral, application and identity indicators to build a more reliable picture. At a conceptual level, these indicators can include:  Early delinquency and straight-roll behavior  Utilization and credit mix that do not align with stated profile  Unusual income, employment, or application characteristics High-risk channels, devices, or locations at application Patterns of disputes or behaviors that suggest abuse  The power comes from how these signals interact, not from any one data point. The report and webinar walk through how these indicators can be combined into fraud analytics and how they perform across key banking products.  Why it matters across fraud, credit and collections Getting first-party fraud right is not just about fraud loss. It impacts multiple parts of the bank. Fraud strategy Well-defined quantification of first-party fraud helps fraud leaders make the case for investments in identity verification, device intelligence, and other early lifecycle controls, especially in digital account opening and digital lending. Credit risk and capital planning When fraud and credit losses are blended, credit models and reserves can be distorted. Separating first-party fraud provides risk teams a cleaner view of true credit performance and supports better capital planning.  Collections and customer treatment Customers in genuine financial distress need different treatment paths than those who never intended to pay. Better segmentation supports more appropriate outreach, hardship programs, and collections strategies, while reserving firmer actions for abuse.  Executive and board reporting Leadership teams increasingly want to understand what portion of loss is being driven by fraud versus credit. Credible data improves discussions around risk appetite and return on capital.  What leading banks are doing differently  In our work with financial institutions, several common practices have emerged among banks that are getting ahead of first-party fraud: 1. Defining first-party fraud explicitly They establish clear definitions and tracking for first-party fraud across key products instead of leaving it buried in credit loss categories.  2. Embedding FPD segmentation into analytics They use FPD-based views in their monitoring and reporting, particularly in the first 6–12 months on book, to better understand early loss behavior.  3. Unifying fraud and credit decisioning Rather than separate strategies that may conflict, they adopt a more unified decisioning framework that considers both fraud and credit risk when approving accounts, setting limits and managing exposure.  4. Leveraging identity and device data They bring in noncredit data — identity risk, device intelligence, application behavior — to complement traditional credit information and strengthen models.  5. Benchmarking performance against peers They use external benchmarks for first-party fraud loss rates and incident sizes to calibrate their risk posture and investment decisions.  The post is meant as a high-level overview. The real value for your teams will be in the detailed benchmarks, charts and examples in the full report and the discussion in the webinar.  If your teams are asking whether rising early losses are driven by fraud or financial distress, this is the moment to look deeper at first-party fraud.  Download the report: “First-party fraud: The most common culprit”  Explore detailed benchmarks for first-party fraud across banking products, see how first-payment default and other indicators are defined and applied, and review examples you can bring into your own internal discussions.  Download the report Watch the on-demand CBA webinar: “Fraud or Financial Distress? How to Differentiate Fraud and Credit Risk Early”  Hear Experian experts walk through real bank scenarios, FPD analytics and practical steps for integrating first-party fraud intelligence into your fraud, credit, and collections strategies.  Watch the webinar First-party fraud is likely already embedded in your early credit losses. With the right analytics and definitions, banks can uncover the true drivers, reduce hidden fraud exposure, and better support customers facing genuine financial hardship.

Financial security has become one of the most pressing concerns in today’s workforce. Rising living costs, higher debt, market volatility and an ever-growing threat of identity theft are putting pressure on employees across every income level. For employers, this shift presents both a challenge and an opportunity: how to meaningfully support employees’ financial well-being in a way that drives engagement, loyalty and long-term success. The reality is clear. Traditional benefits alone are no longer enough. While a steady paycheck, insurance coverage, and retirement plans remain essential, employees increasingly expect their employers to play a more active role in helping them manage day-to-day finances, build credit confidence and protect their identities. When financial stress goes unaddressed, it impacts productivity, mental health and retention, and employers feel the effects just as strongly as their people do. Why fragmented benefits fall short Many organizations offer pieces of financial protection, but those offerings are often disconnected. Identity protection may stop at basic monitoring and alerts. Credit education is frequently limited to static resources that don’t reflect real-life financial behavior. Financial wellness tools, when available, are often treated as optional perks rather than foundational benefits. The problem isn’t a lack of tools; it’s a lack of connection. Employees don’t need more standalone solutions; they need integrated support that meets them where they are and evolves with their financial lives. The rise of all-in-one financial protection Forward-thinking employers are redefining their benefits strategies by adopting a holistic approach to financial well-being. The new standard combines three essential pillars into a single, cohesive experience: Credit education helps employees understand their credit profiles, build healthier habits and make informed financial decisions that unlock better opportunities. Financial wellness tools provide personalized guidance for budgeting, saving, managing debt and planning for the future, reducing stress and improving confidence along the way. Identity protection safeguards employees against fraud and cyber threats with proactive monitoring, alerts and hands-on recovery support when it matters most. When these elements work together, employees are better equipped to protect their paychecks, secure their identities and plan for long-term stability. The result is a workforce that feels supported, empowered and engaged. Turning financial well-being into a strategic advantage At Experian®, we believe financial well-being should be a strategic advantage, not an afterthought. Our all-in-one employee benefits solutions are designed to deliver measurable impact, from improved credit outcomes and reduced financial stress to stronger engagement and retention. By partnering with us, employers can offer a seamless, scalable and trusted experience that supports employees through every stage of their financial journey, while reinforcing their commitment to employee well-being. Download the full report Want to learn more about how credit education, financial wellness and identity protection come together to create a stronger benefits strategy? Download our report to explore the data, insights and strategies shaping the future of employee financial benefits and how your organization can lead the way. Download now

Financial services leaders are dealing with numerous pressures at the same time. These growing challenges for financial services organizations include sophisticated fraud, rapid Artificial Intelligence (AI) adoption without clear regulatory direction, rising customer expectations and the need for compliant, sustainable growth. Businesses are rethinking how they manage risk, growth and customer trust. These financial industry challenges are no longer confined to internal risk teams. They directly impact long-term customer loyalty. How organizations navigate these challenges will determine how effectively they deliver value to their customers. We’ve outlined the six challenges for financial services oranizations that consistently rank highest among industry leaders today. Challenge 1: Fraud is becoming harder to detect and eroding customer trust 72% of business leaders expect AI-generated fraud and deepfakes to be major challenges by 20261 As fraud tactics evolve quickly, driven in part by AI, customers are being targeted through identity-based attacks from account takeovers to synthetic identities and misuse of personal information. When these threats go undetected, or when legitimate activity is incorrectly flagged, the result isn’t just financial loss. It’s a breakdown of trust. Organizations that want to stay ahead must move beyond isolated fraud controls. By embedding identity management and monitoring into the customer experience, organizations can move from reactive fraud response to proactive identity protection. Identity theft protection and monitoring help organizations turn fraud prevention into a visible, trust-building experience for customers — offering early alerts, guidance, and peace of mind when identity risks arise. Challenge 2: AI decisions must be trusted by customers, not just regulators 76% of businesses say implementing responsible AI is one of their biggest challenges2 As AI becomes more embedded in financial services, it shapes the experiences customers see every day. From credit decisions to eligibility outcomes and personalized offers. While AI can drive faster and more inclusive decisions, it also introduces a new expectation: customers want to understand why a decision was made. Responsible AI is no longer just about regulatory compliance. It’s about delivering outcomes that feel fair, consistent and easy to understand. When decisions appear unclear, confidence erodes. When organizations can clearly explain outcomes, not just internally, they build confidence across regulators, partners and customers. This allows AI to scale responsibly while reinforcing trust in every interaction. Financial wellness tools such as credit scores, reports and education help make AI-driven decisions more transparent, giving customers clarity into outcomes and confidence in how their financial health is assessed. Challenge 3: Digital experiences are failing to deliver clarity and confidence 57% of U.S. consumers remain concerned about conducting activities online3 Customer confidence is affected by day-to-day interactions such as onboarding, payments and issue resolution. Inconsistent decisions, unclear outcomes and friction in digital journeys can quickly erode confidence and increase confusion, disengagement and abandonment. Financial services leaders will need to rebuild and strengthen confidence. Improving key decision points with better data and analytics helps ensure customers receive timely insights, understandable outcomes and meaningful guidance, turning everyday interactions into opportunities to build stronger relationships. By delivering ongoing financial wellness insights and education, organizations can replace confusion with clarity — helping consumers better understand their financial standing and stay engaged over time. Challenge 4: Gen Z continues to raise the bar It's no secret that Gen Z stands out for its strong preference for digital financial services and digital interactions, but Gen Z is also pushing the envelope on financial wellness. 48% of Gen Z report that they do not feel financially secure, indicating strong demand for financial support and tools4 Their expectations for instant decisions, seamless digital experiences, transparency and tools that help them manage their financial lives are quickly becoming the baseline. To meet and exceed these expectations, financial institutions will need to support real-time, data-driven decisioning that adapt to individual needs. Delivering modern, app-like financial experiences, without compromising risk management. Increasingly, organizations are meeting Gen Z expectations by offering financial wellness and protection tools through employee benefits, supporting everyday financial confidence beyond traditional compensation. Challenge 5: Limited data limits meaningful consumer engagement 62 million U.S. consumers are thin-file or credit invisible under traditional credit scoring.5 Growth will always be a priority, but it must be responsible and inclusive. Traditional credit data alone often provides an incomplete picture of consumer financial behavior, limiting visibility and making it harder to confidently expand access. By incorporating alternative and expanded data, organizations can gain a more holistic view of consumers. This broader perspective supports smarter decisions, personalized insights and more inclusive engagement, which enables growth while maintaining compliance and managing risk responsibly. Expanded data supports more personalized financial wellness experiences, enabling organizations to provide relevant insights, responsible access and guidance tailored to individual consumer needs. Challenge 6: Disconnected decisions create inconsistent customer experiences Increasingly, fintech leaders are moving toward unified risk and decisioning strategies to deliver more personalized experiences6 While customers interact with a single institution, decisions are often made across disconnected data sources, systems and teams. These silos create inconsistent experiences, slow responses and operational complexities that customers feel directly through conflicting messages and uneven outcomes. Experian helps organizations break down these silos by unifying data, analytics and decisioning across the enterprise. When data incidents occur, integrated experiences enable faster data breach resolution, helping consumers understand what happened, take action, and recover with confidence. Looking ahead These challenges for financial services organizations are not emerging; they’re already here and reshaping how financial institutions engage with consumers. Leaders who proactively address financial industry challenges by connecting data, analytics, and responsible AI are better positioned to deliver trusted, transparent and meaningful experiences. Learn More References:1. https://www.experian.com/blogs/insights/2025-identity-fraud-report2. https://www.techradar.com/pro/businesses-are-struggling-to-implement-responsible-ai-but-it-could-make-all-the-difference3. https://www.experian.com/blogs/insights/2025-identity-fraud-report4. https://www.deloitte.com/global/en/issues/work/genz-millennial-survey.html5. https://www.experian.com/thought-leadership/business/the-roi-of-alternative-data6. https://us-go.experian.com/2025-state-of-fintech-report?cmpid=IM-2025-state-of-fintech-report-livesocial-share

As the U.S. housing market enters a new phase, the 2026 State of the U.S. Housing Market Report from Experian provides a data-driven overview for lenders, servicers, and property managers. This article synthesizes findings related to mortgage originations, affordability pressures, home equity utilization, credit risk, and generational sentiment, with implications for lender strategy in 2026 (Experian, 2026).  Mortgage market in flux: Opportunity amid transition  The mortgage market presents mixed signals. Rate moderation in late 2025 contributed to renewed demand, while the product mix continued to evolve. Conventional loans remained dominant at approximately 72% of originations, yet Veterans Affairs (VA) loans experienced the highest growth between 2023 and 2025, reaching 10.8% market share (Experian, 2026).  At the same time, second mortgages and home equity lines of credit (HELOCs) gained momentum as homeowners sought liquidity without refinancing out of historically low interest rates. This trend reflects growing demand for equity-based solutions that preserve favorable first-mortgage terms (Experian, 2026).   Pull-through challenges: Only 34% of inquiries become loans  Conversion efficiency remains a key challenge. Only 34% of first-mortgage hard credit inquiries resulted in a completed mortgage origination, highlighting friction between borrower interest and loan fulfillment (Experian, 2026).  Consumer research reinforces this gap. In an Experian survey, 50% of respondents reported that understanding what they could qualify for would be the most helpful step in their homeownership journey, suggesting that improved prequalification tools could materially increase pull-through rates (Experian, 2026).   Affordability pressure goes beyond the mortgage  Between 2021 and 2025, property taxes increased by 15.2%, while non-tax escrow costs—primarily homeowners' insurance—rose by 67.4% nationwide (Experian, 2026).  State-level variation further complicates affordability assessments. Florida recorded the highest average non-tax escrow expenses at $430 per month largely due to sharp increase in home insurance costs. California, by contrast, exhibited the highest average property tax burden at $626, largely driven by elevated home values despite lower statutory tax rates (Experian, 2026). These dynamics underscore the importance of holistic cost modeling, particularly for first-time buyers.   Home equity: A lender’s growth frontier  Home equity remains a significant growth opportunity. An estimated 96.2 million consumers reside in owner-occupied homes, with substantial portions owning their homes outright or holding more than 20% equity (Experian, 2026). HELOC usage is increasing, particularly among younger borrowers, 50% of whom utilize more than 60% of their available HELOC credit, compared with 36% of older borrowers (Experian, 2026).  Market share shifts are also notable. Fintech lenders experienced a 140.2% increase in HELOC originations from 2023 to 2025, significantly outpacing banks and credit unions. These gains suggest that digital-first experiences and streamlined workflows are increasingly decisive factors for borrowers (Experian, 2026).   Risk and resilience: What credit and property data reveal  Overall delinquency rates eased slightly; however, near-prime and prime borrowers demonstrated early signs of stress, particularly within first-mortgage portfolios (Experian, 2026).  Property-level risk is also intensifying. Flood exposure increased by 3.7% nationally, with 26.4% of Florida homes identified as at risk. Rising exposure has contributed to escalating insurance costs, further affecting affordability and credit performance (Experian, 2026).  From a credit hierarchy perspective, secured debt—especially mortgages and auto loans—continued to show the lowest delinquency rates. In contrast, student loans and credit cards exhibited higher delinquency risk, particularly among financially constrained renters and homeowners (Experian, 2026).   Generational optimism versus macroeconomic constraints  Despite affordability headwinds, consumer optimism persists. Approximately 47% of renters believe they will be ready to purchase a home within four years, increasing to 67% within eight years (Experian, 2026).  Structural constraints remain significant. Roughly 70% of homeowners hold mortgage rates below 6%, contributing to limited housing inventory as current owners remain rate-locked. With 30-year mortgage rates still above that level and a softening labor market, even modest increases in unemployment could further pressure affordability (Experian, 2026).   Implications for lenders  Experian’s analysis highlights several strategic priorities for housing industry stakeholders:  Expand access to credit. Incorporate alternative data sources, such as cash-flow analytics and rental payment history, to responsibly extend credit to underserved but qualified borrowers (Experian, 2026).  Capitalize on equity demand. Develop HELOC offerings that are fast, flexible, and digitally enabled to meet the needs of equity-rich, rate-locked homeowners (Experian, 2026).  Enhance risk precision. Integrate credit, property, and behavioral data to identify emerging risk early, particularly among near-prime segments, and to support more accurate pricing and portfolio management (Experian, 2026).   Conclusion  The 2026 housing market reflects a complex interplay of macroeconomic pressure, shifting borrower behavior, and growing reliance on home equity solutions. Agility and data-driven decision-making will be essential for lenders navigating this environment. The 2026 State of the U.S. Housing Market Report offers critical insight to support growth while managing risk in an evolving landscape (Experian, 2026).  📘 Access the full report here: Experian 2026 State of the U.S. Housing Market Report  References  Experian. (2026). 2026 state of the U.S. housing market report. Experian.     

The U.S. rental housing market has offered a clear window into broader economic pressures shaping household finances. Renting is no longer a short-term phase for many Americans; it has become a longer-term reality across generations. Rising housing costs, elevated interest rates, and persistent debt burdens are reshaping who rents, how long they rent, and the financial resilience of the rental population. For landlords, lenders, and housing stakeholders, understanding these shifts is essential not only for near-term risk management but also for anticipating how today’s renters will influence tomorrow’s housing demand.  A rental market increasingly defined by younger households  Younger Americans continue to dominate the rental landscape. More than half of U.S. renters are now under the age of 35, with Generation Z accounting for approximately one-third of the total rental population, according to Experian’s 2025 State of the Rental Housing Market report. While renting has always served as a starting point for younger households, the length of time spent renting is increasing. High home prices, ongoing student loan repayment obligations and mortgage rates that remain well above pre-2022 norms have delayed the transition to homeownership for many first-time buyers. As a result, renting has shifted from a temporary necessity to a longer-term financial strategy, often by default rather than by choice.  At the same time, renting is no longer confined to younger demographics. Business Insider reports that since 2023, the population of renters aged 44 and older has grown, reflecting a combination of affordability constraints and evolving lifestyle preferences.  Rent growth continues to outpace income  Affordability remains the defining challenge of the rental market. Experian data shows that average monthly rent has increased steadily over the past two years, rising from approximately $1,520 in early 2023 to roughly $1,760 by early 2025. Over the same period, the average renter’s income has stagnated or declined slightly, hovering near $51,600 annually. The combined effect of rising rents and softening income is a widening affordability gap. The national rent-to-income (RTI) ratio now averages 46.8%, far exceeding the 30% threshold commonly used to define housing affordability, according to Experian’s 2025 analysis.  Credit profiles reflect mounting financial pressure  As housing costs rise, renters’ financial health is increasingly under strain. Experian’s rental market insights indicate that more than half of renters now fall within Near Prime or Subprime credit tiers, representing a meaningful increase since 2023. This shift does not necessarily signal declining financial responsibility. Instead, it reflects younger and thinner credit files, higher overall debt balances, and sustained cost-of-living pressures. Experian also reports that renters paying more than $1,000 per month have experienced a double-digit increase in negative payment activity over the past two years.  Understanding payment priorities when budgets tighten  When households face financial strain, trade-offs become unavoidable. According to payment hierarchy data published by the Consumer Financial Protection Bureau (CFPB), renters tend to prioritize auto loans first, followed by personal loans and student loans. Rent payments and unsecured revolving credit, such as credit cards, often fall lower on the priority list. This dynamic helps explain why rent delinquencies can rise even among renters who remain current on other financial obligations.  Why Experian?  As the rental market evolves, having a clear, data-driven view of renter behavior is more critical than ever. Experian helps landlords, lenders, and housing stakeholders understand shifting demographics, affordability pressures, and payment priorities through trusted data and advanced analytics. With deeper insight into today’s renters, businesses can better manage risk, uncover opportunity, and prepare for what’s next in housing demand.   

Since 1996, The Internal Revenue Service (IRS) has issued more than 27 million individual taxpayer identification numbers (ITINs) –⁠ a 9-digit number used by individuals who are required to file or report taxes in the United States but are not eligible to obtain a Social Security number (SSN). Across the country, ITIN holders are actively contributing to their communities and the U.S. financial system. They pay bills, build businesses, contribute billions in taxes and manage their finances responsibly. Yet despite their clear engagement, many remain underrepresented within traditional lending models.  Lenders have a meaningful opportunity to bridge the gap between intention and impact. By rethinking how ITIN consumers are evaluated and supported, financial institutions can: Reduce barriers that have historically held capable borrowers back Build products that reflect real borrower needs Foster trust and strengthen community relationships Drive sustainable, responsible growth Our latest white paper takes a more holistic look at ITIN consumers, highlighting their credit behaviors, performance patterns and long-term growth potential. The findings reveal a population that is not only financially engaged, but also demonstrating signs of ongoing stability and mobility. A few takeaways include: ITIN holders maintain a lower debt-to-income ratio than SSN consumers. ITIN holders exhibit fewer derogatory accounts (180–⁠400 days past due). After 12 months, 76.9% of ITIN holders remained current on trades, a rate 15% higher than SSN consumers. With deeper insight into this segment, lenders can make more informed, inclusive decisions. Read the full white paper to uncover the trends and opportunities shaping the future of ITIN lending. Download white paper

Growth, risk and the rise of "hidden" business accounts As inflation remains elevated and early signs of labor market cooling emerge, the credit card landscape is entering its next phase. Over the past few weeks, policy actions and discussions around potential interest-rate caps have driven increased uncertainty across the credit card industry and broader global markets. Lenders face a careful balancing act: capturing growth opportunities while maintaining disciplined risk oversight. Our second annual State of Credit Cards Report explores the macroeconomic forces influencing the market, key shifts in originations and delinquency trends, and lender mix. New this year, the report also digs into an often‑overlooked segment: business accounts hidden inside consumer credit card portfolios. Additionally, the report offers actionable strategies to help lenders segment risk and drive disciplined growth more effectively. Key insights include: 30+ DPD delinquency rates remained above pre-pandemic levels in 2025, underscoring the need for disciplined asset‑quality monitoring. Fintechs continue to gain ground, posting a 71% YOY increase in account originations.  Business accounts masked in the consumer credit card universe represent roughly 14% of balances and are more than 50% larger than the business card universe — a material segment with distinct risk and profitability dynamics that many lenders are not explicitly managing today. The report also outlines practical strategies to: Identify and segment business behavior within consumer portfolios. Align underwriting and account management with actual usage patterns. Capture targeted growth while protecting long‑term portfolio performance. Ready to dive deeper? Download the full 2026 State of Credit Cards Report to uncover insights that can help your organization manage risk more precisely and grow with confidence. Download report

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.