Lending hasn’t slowed down—but many decisioning processes have. Applications are coming in faster. Fraud is becoming more sophisticated. Borrowers expect near-instant responses. And yet, inside many organizations, decisions are still being made across fragmented systems, manual reviews, and rigid strategies that weren’t designed and aren’t optimized for today’s environment. That broadening gap isn’t just an operational issue but often stems from a lack of innovation as well. And it’s quietly costing lenders growth, efficiency, and competitive position. When decisioning falls behind, some symptoms are easy to recognize, like applications taking days to process, teams overloaded with manual reviews, and credit and fraud decisions happening in separate platforms. Others are not as obvious, but arguably more impactful, slipping bottom lines and fraud and therefore losses lurking in lenders’ portfolios. The root issue is a fragmented infrastructure. Experian has reported that while 79% of financial institutions surveyed globally want fewer vendors or more unified approaches, they typically use eight or more tools across credit, fraud and compliance. As most decisioning environments cannot integrate data, adapt strategies, and execute decisions in real time, lenders often have to make tradeoffs. Speed vs. accuracy; growth vs. risk; and automation vs. control are just some. Meanwhile, the market has moved on. Leading lenders are no longer optimizing individual steps. They’re rethinking decisioning as a connected, intelligent system. Gaps forming from status quo in 8 key decision areas Across the lending lifecycle, there are eight critical moments where decisioning can either accelerate growth or create friction. Pre-qualification: Pre-qualification should expand your funnel with confidence. But limited data access and static criteria often result in overly conservative targeting or missed opportunities. Additionally, the delay in acting on a pre-qualification funnel highlights a key area for opportunity among many lenders. Instant credit decisions: Customers expect real-time outcomes. When decisions rely on manual intervention or fragmented inputs, speed and conversions suffer. Prescreen and targeting: Disconnected data and rigid segmentation can lead to poorly aligned offers, reducing response rates and wasting acquisition spend. Credit line management: Without dynamic strategies, credit lines may be too restrictive (limiting growth) or too aggressive (increasing risk). Early delinquency management: Missed early signals and delayed interventions make it harder to prevent accounts from deteriorating. Mid- and late-stage delinquency: Strategies that don’t adapt to evolving borrower behavior reduce recovery effectiveness and increase losses. Collections and recovery: Manual, one-size-fits-all approaches limit recovery rates and increase operational cost. Ongoing strategy optimization: Perhaps the most overlooked gap: many lenders lack the ability to continuously test, learn, and refine decision strategies as conditions change. What these gaps are really costing you Individually, each of these breakdowns may seem manageable. Together, they can create systemic drag on performance. That shows up in four critical ways: Missed growth opportunities: Good borrowers are declined, abandoned, or never targeted in the first place. Credit offers fail to align with actual borrower potential. Higher operational costs: Manual reviews and disconnected workflows consume time and resources that could be spent on higher-value work. Increased fraud exposure and friction: Fraud is proliferating and becoming more expensive to manage. The Federal Trade Commission reported $12.5B were lost to fraud in the U.S. in 2024, a 25% increase over the prior year. For many financial institutions, the first reaction is often to add more steps to the decisioning process, which can impact good borrowers. Increased competitive pressure: Fintechs and modern lenders are focused on delivering faster, more personalized experiences, capturing share while traditional processes lag behind. 80% of banks and credit unions plan to increase their technology spending in 2026, yet many continue to fall short on planned system deployments, according to Cornerstone Advisors’ annual “What’s Going On in Banking” research report. What innovative decisioning leaders are doing differently Leading lenders are changing how decisions are made, creating a competitive advantage. Instead of stitching together point solutions, they’re adopting a more integrated approach that brings together: Comprehensive data – including both credit and fraud insights Optimized decision strategies – designed to balance growth and risk Real-time execution – enabling faster, more consistent outcomes Continuous optimization – adapting to changing market conditions Strategic partnerships – leveraging third-party industry expertise to augment their own This shift eliminates the need for tradeoffs and instead allows lenders to increase approvals while maintaining control, reducing manual effort while improving consistency, and responding faster without sacrificing confidence. The stakes are high and the competition for consumers is even higher, particularly against a backdrop of ever-evolving fraud risks, continuously increasing consumer expectations for seamless, digital-first experiences and often limited resources. Nearly half of banks and 59% of credit unions have already deployed generative AI, with more investing now, according to the Cornerstone Advisors’ report. Closing the innovation gap requires a more fundamental shift toward decisioning systems that are connected, scalable, and built for continuous change. A new foundation for decisioning This is where platforms like Experian Decisioning are changing the landscape. By bringing together credit and fraud insights, decision strategies, and a flexible technology architecture, lenders can move beyond fragmented processes and build a more unified, intelligent decisioning approach. One that fits within existing systems but also evolves with your needs. Where to start Impactful change doesn’t need to be an overhaul of everything at once for most organizations. The first step is understanding where your biggest gaps exist, and which decision areas are creating the most friction or missed opportunity. Once you can see where decisioning is not optimized, you can begin to redesign it in a way that’s faster and more adept for what lending has become. By making better decisions, faster, and with greater confidence, lenders can process applications more efficiently and also break away from the pack by leveraging decisioning as a strategic advantage. Learn more

As the market finds its footing, evolving consumer demand is driving changes in new and used vehicle registrations. In response, manufacturers are balancing affordability and production efficiency to protect their market share. According to Experian’s Automotive Market Trends Report: Q4 2025, new vehicle registrations slightly decreased to 3.8 million, from 4 million this time last year. On the used side, registrations ticked up slightly year-over-year, going from 9 million to 9.1 million. With elevated new vehicle pricing and higher interest rates likely playing a role in new vehicle registrations dipping, buyers seem to be gravitating toward lower-cost alternatives in the used market. Familiar OEM leaders remain steady at the top of market share Despite shifts in vehicle registrations, leaders in new vehicle manufacturer market share have remained consistent. For instance, data through the fourth quarter of this year reveled General Motors (GM), Toyota, and Ford continue to hold the top three positions in new vehicle market share, with GM coming in at 17.4% share, followed by Toyota (16.5%), and Ford (12.6%). At the make level, Toyota held the top position for the fourth consecutive year in new vehicle market share, coming in at 14.1% through Q4 2025; they were followed by Ford (11.9%) and Chevrolet (11%). Sustained leadership in today’s market isn’t just about scale, it relies on how quickly manufacturers can respond and adapt to shifting consumer preferences and industry changes. Those that adapt their portfolios and go-to-market approaches will be best positioned not just to protect their share, but to grow it as the market continues to evolve. To learn more about vehicle market trends, view the full Automotive Market Trends Report: Q4 2025 presentation on demand.

A new reality for screening providers Everything about the candidate checked out. Their resume reflected the right experience. Their references confirmed it. The background screening process came back clean. From outside, there was no reason to hesitate. So, the company didn’t.  But within weeks, small inconsistencies began to surface. The employee struggled in ways that didn’t match their credentials. Follow-up questions led to vague answers. Eventually, a deeper review uncovered the issue; this wasn’t just a case of exaggeration. It was candidate fraud. And increasingly, it’s not just individuals acting alone.  In a widely reported scheme, foreign operatives posed as legitimate remote IT workers, using stolen identities and AI-assisted interviews to secure jobs at major Fortune 500 companies. Once hired, access was handed off, allowing bad actors to infiltrate corporate systems and generate millions in illicit revenue. In one case, a single individual funneled over $17 million to a foreign operation. These weren’t obvious scams. The candidates passed interviews. They cleared checks. And that’s exactly the point. For background screening and verification providers, this shift presents both a challenge and an opportunity. As candidate fraud becomes more sophisticated, your clients are no longer just looking to verify records – they’re looking to trust identity itself, and they’re looking to you to help them do it. The assumption that no longer holds For decades, hiring has relied on a simple premise: verify the records, resume, and you can trust the candidate. That model worked when identity was easier to validate in person. But in today’s digital-first hiring environment, identity can oftentimes be asserted, not proven. At the same time, identity-based fraud is accelerating. Synthetic identity fraud alone accounts for billions in annual losses, and employers are increasingly encountering candidates whose identities are far more difficult to validate than their resumes. This creates a critical disconnect: Organizations are still verifying records, but those records may be tied to identities that were never legitimate to begin with. Increasingly, they’re turning to their screening partners to close that gap. The reality of candidate fraud 31% of employers have interviewed candidates using a false identity Only 19% feel confident they can detect fraud in hiring 1 in 4 companies report losses of$50K+from fraudulent hires Why candidate fraud is getting harder to see The nature of candidate fraud has fundamentally changed. At one end of the spectrum, companies are still dealing with candidates who falsify resumes, costing businesses time and money when the truth comes to light later. But at the other end, the threat has escalated dramatically. Coordinated fraud rings are now using stolen identities and AI-assisted interviews to place individuals into remote roles, sometimes without ever revealing their identity. And this isn’t slowing down. According to Gartner, by 2028, 1 in 4 candidates could be fake, driven by AI, remote hiring, and identity manipulation. For screening providers, this introduces a new level of complexity. The challenge is no longer just delivering verified records; it’s helping clients surface risks that traditional screening processes were not designed to identify. What traditional screening still gets right None of this diminishes the importance of pre-employment screening. Verifying employment history, education, and background remains a critical part of responsible hiring, and it should. But even the most thorough screening process is designed to answer a specific question: Do the records align with the identity provided? What it does not answer is the question that matters most now: Is that identity real? That gap between record verification and identity validation is where modern fraud operates. And it represents an opportunity for screeners to expand their role from record validation to helping enable stronger identity confidence. The cost of believing everything is working When fraud moves through the hiring process undetected, the consequences aren’t always immediate, but they can be significant. There are financial risks, compliance exposure and potential access to sensitive systems. But there’s also a more subtle —and often overlooked — impact: The assumption that existing processes are working as intended. When fraudulent candidates pass through screening, it reinforces confidence in processes that may not be equipped for today’s threat landscape. Over time, that false sense of security can become a vulnerability. From screening provider to strategic partner As hiring evolves, so do expectations. Employers are no longer just looking for faster background checks - they’re looking for greater confidence in who they’re hiring. This shift creates an opportunity for screening providers to move upstream in the hiring process. By introducing identity verification earlier in the workflow, providers can help clients detect candidate fraud sooner, reduce downstream risk, and strengthen the integrity of hiring decisions.  More importantly, it allows providers to differentiate their offerings in an increasingly competitive market, shifting from a transactional service to a more strategic capability. A shift in thinking: Identity before everything else To address modern candidate fraud, organizations don’t just need better tools; they need a different starting point. Instead of beginning with records, leading providers are beginning with identity. They are asking a more fundamental question earlier in the process:  Is this person who they say they are? Is this person a real, consistent and verifiable person? When that foundation is established, everything that follows becomes more meaningful. Background checks become more reliable. Verification becomes more consistent. And the ability to detect candidate fraud improves, not because the process is longer, but because it’s more informed. In this model, identifying potential fraud becomes proactive rather than reactive. Why identity verification matters more now than ever The shift to remote and digital hiring hasn’t just changed how companies hire – it’s changed how fraud occurs. Today, a significant portion of fraudulent activity targets the employment process, making it a key point of exposure for identity misuse. In fact, 45% of all false document submissions now occur in the employment sector. In many cases, candidates who falsify information still progress through hiring workflows. A study revealed that 70% of candidates who falsify information still get hired. This reinforces today’s reality: Fraud is no longer slipping through the cracks; it’s moving through the front door. How Experian helps close the identity gap Experian® helps background screeners and verification providers bridge the gap between who a candidate claims to be and who they are. By combining identity verification, fraud detection, and verification solutions, Experian enables providers to enhance their existing solutions – without disrupting their workflows. This allows you to extend your value beyond traditional screening, help clients detect candidate fraud earlier, and strengthen confidence in hiring outcomes.   The result is not just better screening, it’s a stronger strategic position in your clients’ hiring ecosystem, one that reduces risk while improving speed and confidence. Candidate fraud isn’t an edge case anymore. It reflects a broader shift in how identity works in a digital world. And while traditional screening remains essential, it may not be sufficient on its own. Because if identity is uncertain, every subsequent check is built on unstable ground. But when identity is established earlier in the process, everything that follows becomes more dependable. Don’t just verify the candidate records, verify the identityLearn how Experian helps screening providers embed identity verification at the start of the hiring journey to help detect candidate fraud earlier, reduce risk, and strengthen screening outcomes.  Explore Experian’s Fraud Prevention Playbook for Pre-Employment Screening FAQs

Stressed-out employees aren’t just bad for morale; they’re a drain on productivity and can even affect revenue.   A study last year found that 72% of U.S. workers were to some extent stressed about their household finances, while one-third said they were extremely stressed.1 The growing financial pressure on today's workforce is real and widespread. Rising costs, mounting debt, tightening credit markets, and an accelerating wave of identity theft and fraud have created conditions where financial stress isn't limited to low-income earners. Inflation, rate hikes, and relentless data breaches are impacting workers at every level, eroding not just their bank accounts but also their focus, resilience and ability to function at their best. Some of this stress is due to the threatening environment we live in. Every employee, regardless of role or income level, is at risk from cyberattacks and increasingly sophisticated fraud schemes. Identity theft is ever-present, from phishing attacks and credential stuffing, ransomware and social engineering. The escalation in cybercrime has made personal and financial data more vulnerable than ever. The Federal Trade Commission Consumer Sentinel Network Report for 2025 stated that millions of identities are compromised each year, often without the individual realizing it until the damage is already done.2 Given this ongoing threat landscape, it is little wonder that employee financial stress and workplace productivity loss levels are high. The correlation between employee financial stress and productivity loss When employees feel stressed and burned out, it negatively affects their work productivity, which can lead to a loss of revenue for the company. A recent survey found that 66% of employees blamed financial stress for negatively affecting their work and personal lives.3 The same survey found that 83% of HR leaders are concerned that employee financial concerns are harming productivity.4 When employees are consumed by financial stress or anxious about identity theft, their attention is split. They're managing debt, monitoring accounts, or dealing with fallout from fraud, all while trying to do their jobs. The end result is cognitive overload. Simply put, financially stressed employees are more distracted, less engaged, and more likely to make errors. Identity theft compounds the problem because resolving it can take dozens of hours and even months of follow-up. And much of that effort is likely to be happening during work hours. The upshot of all this employee financial stress is presenteeism. In other words, employees are on the job, but their minds are elsewhere. Bottom line: workplace productivity takes the hit. The cost of this stress on employees is ultimately borne by employers. A recent report found that U.S. employers lose approximately $250 billion a year due to lost productivity and distraction caused by financial stress.5 A Gallup survey also reported that "disengaged employees — often dealing with financial stress, cost the global economy $8.9 trillion in lost productivity yearly."6 The cost of ignoring this issue can be staggering for employers, who ultimately bear the weight of workplace productivity loss, increased healthcare costs, and higher turnover, all of which impact the bottom line. Fortunately, employers can avoid most of these problems by taking the right proactive steps that better support employees in managing financial stressors. This is key to building a more resilient workforce. Targeting the source of stress with financial wellness tools Today’s employers are recognizing that simply providing a steady paycheck, basic insurance, and a 401(k) is no longer sufficient to attract and retain the best and brightest employees. The U.S. Bureau of Labor Statistics found that 87% of workers would consider leaving a company that doesn’t prioritize employee well-being and that 84% feel their employer should be more involved in helping them through financial challenges.7 Conversely, 70% of those surveyed indicated that benefits that better support their financial wellness would increase their loyalty to their companies.8 What is clear is that employees want personalized support in managing finances, building credit and securing their financial future. Providing the depth and breadth of support employees are clamoring for is essential for addressing and preventing workplace productivity loss and revenue impacts.   To address employee concerns, employers need to offer more than just tools. They need connected solutions that offer all-in-one financial protection, enabling employees to better safeguard their paychecks, protect their identities and plan for tomorrow. Instead of simply reacting to threats, a more holistic approach that proactively equips employees with the insights, tools, and support they need is called for. Zooming in on what a holistic solution should do A comprehensive, fully integrated set of holistic employee financial wellness tools offered as benefits can be instrumental in alleviating some of the stress workers feel. The right set of financial wellness tools should include: Identity protection and restoration – Avoiding becoming avictim of identity theft and fraud is crucial. Effective tools monitor personal information, send fraud alerts and help with resolution services to facilitate faster recovery. Credit education and financial management – Employees are interested in learning how to pay down debt and increase their credit score. Providing instructive credit education resources empowers them to set goals, make actionable plans and track their progress. Device and data protection – The threat to personal data is constant. Providing proactive digital privacy tools can help employees keep passwords and other personal information secure while browsing. Financial wellness benefits only work if employees actually trust them. Our My Financial Expert® platform enables employers to offer holistic benefits, including more than 50 financial wellness features designed to help employees take control of their finances. With a proven track record in credit education and identity protection, we have supported and protected more than one billion consumers. When employees stop worrying about money, they start focusing on work. It's that simple. By giving workers real tools to tackle financial stress, productivity loss, and security concerns, employers reduce distractions, boost satisfaction and make a compelling case for why good people should stay. The payoff isn't just cultural. It shows up through improved productivity and a more robust bottom line. Learn more about our financial wellness programs

Model inventories are rapidly expanding. AI-enabled tools are entering workflows that were once deterministic and decisioning environments are more interconnected than ever. At the same time, regulatory scrutiny around model risk management continues to intensify. In many institutions, classification determines validation depth, monitoring intensity, and escalation pathways while informing board reporting. If classification is wrong, every downstream control is misaligned. And, in 2026, model classification is no longer just about assigning a tier, but rather about understanding data lineage, use case evolution, interdependencies, and governance accountability in a decentralized, AI-driven environment. We recently spoke with Mark Longman, Director of Analytics and Regulatory Technology, and here are some of his thoughts around five blind spots risk and compliance leaders should consider addressing now. 1. The “Set It and Forget It” Mentality The Blind Spot Model classification frameworks are often designed during a regulatory remediation effort or inventory modernization initiative. Once documented and approved, they can remain largely unchanged for years. However, model risk management is an ongoing process. “There’s really no sort of one and done when it comes to model risk management,” said Longman. Why It Matters Classification is not merely descriptive, it’s prescriptive. It drives the depth of validation, the frequency of monitoring, the intensity of governance oversight and the level of senior management visibility. As Longman notes, data fragmentation is compounding the challenge. “There’s data everywhere – internal, cloud, even shadow IT – and it’s tough to get a clear view into the inputs into the models,” he said. When inputs are unclear, tiering becomes inherently subjective and if classification frameworks are not reviewed regularly, governance intensity can become misaligned with real exposure. Therefore, static classification is a growing risk, especially in a world of rapidly expanding AI use cases. In a supervisory environment that continues to scrutinize model definitions, particularly as AI tools proliferate, a dynamic, periodically refreshed classification process can demonstrate institutional vigilance. 2. Assuming Third-Party Models Reduce Governance Accountability The Blind SpotThere is often an implicit belief that vendor-provided models carry less governance burden because they were developed externally. Why It Matters Vendor provided models continue to grow, particularly in AI-driven solutions, but supervisory expectations remain firm. “Third-party models do not diminish the responsibility of the institution for its governance and oversight of the model – whether it’s monitoring, ongoing validation, just evaluating drift model documentation,” Longman said. “The board and senior managers are responsible to make sure that these models are performing as expected and that includes third-party models.” Regulators consistently emphasize that institutions remain responsible for the outcomes produced by models used in their decisioning environments, regardless of origin. If a vendor model influences credit approvals, pricing, fraud decisions, or capital calculations, it directly affects customers, financial performance and compliance exposure. Treating third-party models as inherently lower risk can also distort internal tiering frameworks. When vendor models are under-classified, validation depth and monitoring rigor may be insufficient relative to their true impact. 3. Limited Situational Awareness of Model Interdependencies The Blind Spotfeed multiple downstream models simultaneously. Why It Matters Risk often flows across interdependencies. When upstream models degrade in performance or introduce bias, downstream models inherit that exposure. If multiple material decisions depend on the same data transformation or feature engineering process, concentration risk emerges. Without visibility into these dependencies, tiering assessments may underestimate cumulative risk, and monitoring frameworks may fail to detect systemic vulnerabilities. “There has to be a holistic view of what models are being used for – and really somebody to ensure there’s not that overlap across models,” Longman said. Supervisors are increasingly interested in understanding how model risk propagates through business processes. When institutions cannot articulate how models interact, it raises broader concerns about situational awareness and control effectiveness. Therefore, capturing interdependencies within the classification framework enhances more than documentation. It enables more accurate tiering, more targeted monitoring and more informed governance oversight. 4. Excluding Models Without Defensible Rationale The Blind SpotGray-area tools frequently sit outside formal inventories: rule-based engines, spreadsheet models, scenario calculators, heuristic decision aids, or emerging AI tools used for analysis and summarization. These tools may not neatly fit legacy definitions of a “model,” and so they are sometimes excluded without robust documentation. Why It Matters Regulatory definitions of “model” have broadened over time. What creates risk is the absence of defensible reasoning and documentation. Longman describes the risk clearly: “Some [teams] are deploying AI solutions that are sort of unbeknownst to the model risk management community – and almost creating what you might think of as a shadow model inventory.” Without visibility, institutions cannot confidently characterize use, trace inputs, or assign appropriate tiers, according to Longman. It also undermines the credibility of the official inventory during examinations. A well-governed program can articulate why certain tools fall outside model risk management scope, referencing documented criteria aligned with regulatory guidance. Without that evidence, exclusions can appear arbitrary, suggesting gaps in oversight. 5. Inconsistent or Subjective Classification Frameworks The Blind SpotAs inventories scale and governance teams expand, classification decisions are often distributed across reviewers. Over time, discrepancies can emerge. Why It Matters Inconsistency undermines both risk management and regulatory confidence. If two models with comparable use cases and impact profiles are assigned different tiers without clear justification, it signals that the framework is not being applied uniformly. AI adds even more complexity. When it comes to emerging AI model governance versus traditional model governance, there’s a lot to unpack, says Longman: “The AI models themselves are a lot more complicated than your traditional logistic or multiple regression models. The data, the prompting, you need to monitor the prompts that the LLMs for example are responding to and you need to make sure you can have what you may think of as prompt drift,” Longman said. As frameworks evolve, particularly to incorporate AI, automation, and new regulatory interpretations, institutions must ensure that changes are cascaded across the entire inventory. Partial updates or selective reclassification introduce fragmentation. Longman recommends formalizing classification through a structured decision tree embedded in policy to ensure consistent outcomes across business units. Beyond clear documentation, a strong classification program is applied consistently, measured objectively, and periodically reassessed across the full portfolio. BONUS – 6. Elevating Classification with Data-Level Visibility Some institutions are extending classification discipline beyond models to the data layer itself. Longman describes organizations that maintain not only a model inventory, but a data inventory, mapping variables to the models they influence. This approach allows institutions to quickly assess downstream effects when operational or environmental changes occur including system updates or even natural disasters affecting payment behavior. In an AI-driven environment, traceability may become a competitive differentiator. Conclusion Model classification is foundational. It determines how risk is measured, monitored, escalated, and reported. In a rapidly evolving regulatory and technological environment, it cannot remain static. Institutions that invest now in transparency, consistency, and data-level visibility will not only reduce supervisory friction – they will build a governance framework capable of supporting the next generation of AI-enabled decisioning. Learn more

Alternative fuel vehicles continued to gain momentum in the fourth quarter of 2025, driven by expiring electric vehicle (EV) tax credits and a growing preference for options that bridge the gap between full electric and traditional gasoline vehicles. According to Experian’s Automotive Consumer Trends Report: Q4 2025, alternative fuel vehicles accounted for 38.6% of new retail car registrations in the last 12 months, with 11% battery electric (BEV) and 27.6% hybrids and plug-in hybrids (PHEVs). This signals that the narrative about growth in consumer interest for alternative fuel is increasingly towards hybrids, not just full EVs. Taking a deeper dive, the Toyota Camry Hybrid led all alternative fuel car models, coming in 31.7% in Q4 2025. Rounding out the top five were Tesla Model 3 (19%), Honda Civic Hybrid (10.1%), Honda Accord Hybrid (9%), and Toyota Prius (5.3%). Interestingly, the Toyota Camry also stood out as the top model in both new and used car markets in Q4 2025, holding 12.2% of new car market share and 6.3% of used car market share. The Honda Civic ranked second in new car market share, coming in at 10.5% this quarter, while the Honda Accord secured the second spot in the used car market at 5.8%. The prominence of these vehicles leading both new and used car markets reflects a combination of strong new-vehicle sales and sustained demand in the secondary market. Data in the report also revealed strong loyalty within Toyota and Honda, with significant inflow between the two brands. For instance, 38.4% of Toyota Camry buyers replaced their vehicle with another Camry in Q4 2025, and 39.7% of Honda Civic buyers replaced it with another Civic. These trends reinforce the value of dealers monitoring evolving consumer preferences and aligning inventory with vehicles that offer fuel efficiency and flexible powertrain options as the market continues to shift. To learn more about car insights, view the full Automotive Consumer Trends Report: Q4 2025 presentation.

  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.

The mortgage industry is adapting to a structural shift. Experian’s 2026 State of the U.S. Housing Market Report shows a market in transition. Conventional loans account for 72% of originations, FHA 17.5% and VA 10.8% with VA showing the strongest growth from 2023 to 2025. But origination mix only tells part of the story. Beneath it lies an arguably more consequential shift: borrower expectations, affordability pressures and regulatory changes are converging. On the regulatory front, the Homebuyers Privacy Protection Act (HPPA) may reduce mortgage trigger leads and limit broad competitive outreach. As competitive visibility narrows, the lender relationship becomes more central and important beyond the closing transaction. In this environment, lenders must provide value to win, and that increasingly means financial wellness. A growing trust gap Only 34% of first mortgage hard inquiries of first mortgage hard inquiries convert into funded originations, according to Experian. That means two-thirds of borrowers who initiate the process never close. External data confirms the trend as Mortgage Bankers Association reported retail mortgage pull-through rates declined to roughly 69% in early 2025 – the lowest in over a decade – and as low as 55% among depository lenders. While pull-through can be impacted by a number of factors not influenced by the lender, when borrowers abandon applications, it can be a biproduct of uncertainty – something that the lender can influence. This is where financial wellness becomes strategic and lenders can close the trust gap by providing proactive credit visibility and guidance before underwriting friction occurs. Read more in our white paper, “The New Unlock for Mortgage.” Affordability stress While rates have eased from their 2023 highs, they remain above 6%, sustaining the lock-in effect and limiting housing supply, according to Experian’s 2026 State of the U.S. Housing Market Report. Approximately 70% of homeowners are locked into sub-6% mortgages, according to Freddie Mac. Beyond mortgage rates, increases in property taxes and non-tax escrow amounts (i.e. insurance) increase affordability pressures for consumers. Financial wellness solutions that incorporate credit monitoring, budgeting insights and cashflow visibility help borrowers understand whether they are prepared. Opportunity among millennials and Gen Z Nearly 47% of U.S. renters expect to purchase a home within four years, rising to 67% within eight years, according to Experian. This signals the time to invest in financial wellness as a differentiator, and both a growth and retention driver, is now. Financial wellness as the new unlock for mortgage Financial wellness is not an ancillary service but the foundation upon which borrower confidence, long-term engagement, conversion and risk management connect. Lenders who embed solutions like credit education, score visibility, alerts, and identity protection directly into the consumer experience can differentiate themselves from the competition above and beyond rates alone. Read more in our white paper, “The New Unlock for Mortgage.” Learn more about Experian Mortgage

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

  Experian Verify is redefining how lenders streamline income and employment verification; a value clearly reflected in Marcus Bontrager’s experience at Freedom Mortgage. With access to the second-largest instant payroll network in the U.S., Experian Verify connects lenders to millions of unique employer records, including those sourced through Experian Employer Services clients, delivering instant results at scale. This reach enables lenders to reduce manual processes, accelerate loan decisions, and enhance the borrower experience from the very first touchpoint. Unlike traditional verification providers, Experian Verify offers transparent, value-driven pricing: it charges only when a consumer is successfully verified, not simply when an employer record is found. As lenders navigate increasing compliance requirements and secondary market expectations, they can also rely on Experian Verify’s FCRA-compliant framework, fully supporting both Fannie Mae and Freddie Mac. Combined with Experian’s industry-leading data governance and quality standards, lenders gain a verification partner they can trust for accuracy, security, and long-term operational efficiency. Perhaps most importantly, Experian Verify delivers 100% U.S. workforce coverage through its flexible, automated waterfall: instant verification, consumer-permissioned verification, and research verification. This multilayered approach ensures lenders meet every borrower where they are, whether they’re connected to a large payroll provider, a smaller employer, or require additional document-based validation. As Marcus highlights in the video, this comprehensive and configurable design empowers lenders to build verification workflows that truly fit their business needs while enhancing speed, completeness, and borrower satisfaction. Explore Experian Verify

Fraud is evolving faster than ever, driven by digitalization, real-time payments and increasingly sophisticated scams. For Warren Jones and his team at Santander Bank, staying ahead requires more than tools. It requires the right partner. The partnership with Santander Bank began nearly a decade ago, during a period of rapid change in the fraud and banking landscape. Since then, the relationship has grown into a long-term collaboration focused on continuous improvement and innovation. Experian products helped Santander address one of its most pressing operational challenges: a high-volume manual review queue for new account applications. While the vast majority of alerts in the queue were fraudulent and ultimately declined, a small percentage represented legitimate customers whose account openings were delayed. This created inefficiencies for staff and a poor first impression of genuine applicants. We worked alongside Santander to tackle this challenge head-on, transforming how applications were reviewed, how fraud was detected and how legitimate customers were approved. In addition to fraud prevention, implementing Experian's Ascend PlatformTM, with its intuitive user experience and robust data environment, has unlocked additional value across the organization. The platform supports multiple use cases, enabling collaboration between fraud and marketing teams to align strategies based on actionable insights. Learn more about our Ascend Platform