Verification has become an increasingly important area of focus in mortgage lending, but success is about more than just coverage. In the latest episode of the Chrisman Commentary Podcast, Experian's Jamie Norris, Senior Manager of Strategic Alliances, shares why distribution and integration are increasingly the keys to driving adoption, automation, and better borrower experiences.  Why Distribution Matters in Verification  As lenders continue to pursue faster, more efficient mortgage processes, verification solutions must fit seamlessly into the systems they already use. Norris explains how Experian's strategy is focused on helping lenders access trusted income and employment data while minimizing workflow disruption by making Experian Verify accessible across loan origination systems (LOS), point-of-sale platforms, underwriting technologies, and reseller networks.  Building a Smarter Verification Strategy  The conversation explores why lenders benefit from having access to multiple verification providers, how they can optimize verification strategies to maximize automation while minimizing costs and borrower friction, and why an "instant-first" approach is gaining momentum across the industry.  Looking Ahead: AI, Automation, and the Future of Mortgage Lending  Norris also discusses how AI-driven underwriting and decisioning are reshaping mortgage technology. As lending platforms become increasingly automated, real-time verification data is expected to support faster decisioning and more streamlined borrower experiences.  She shares Experian's vision for expanding its verification ecosystem and delivering a broader suite of solutions that meet lenders wherever they work.  Listen to the full episode above to hear Jamie's insights on verification strategy, partner integrations, AI-enabled lending, and what's next for mortgage automation. 

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Underwriting data analytics uses data, statistical models and machine learning to evaluate an applicant's risk and make credit decisions. Instead of relying on a single score or manual review, it draws on thousands of data points to deliver faster, more accurate and more inclusive lending decisions. Apply for a car loan at lunch, and you might have an answer before your coffee arrives. For consumers, credit has never felt more effortless. A few taps, an instant decision, funds on the way. This seamless experience is no accident. Behind every quick “yes” is decades of credit history, thousands of predictive data attributes and machine learning models weighing them all in seconds. That behind-the-scenes engine is underwriting data analytics. How data becomes a lending decision At its best, underwriting data analytics delivers something every lender wants: more creditworthy applicants approved, fewer unnecessary declines, lower losses and fraud, and the near-instant experiences today’s consumers and businesses expect. Underwriting data analytics is the practice of using data, statistical models and machine learning to evaluate risk and make credit decisions. Rather than relying solely on a single score or a manual file review, today’s underwriting draws on thousands of data attributes derived from traditional credit history, alternative data such as rent and utility payments, cash flow insights, commercial data and more. Experian’s Premier Attributes alone includes more than 2,100 attributes across 51 industries building a fuller, fairer picture of each applicant. And the payoff is measurable. Research shows that pairing expanded data with advanced analytics can extend reliable credit scoring to 96% of American adults, up from 81% with conventional scores relying on traditional credit data alone. In practice, underwriting data analytics spans four connected activities: Data aggregation. Attribute and score development. Decisioning. Monitoring and refinement How to put underwriting data analytics to work The short answer: any organization that extends credit or takes on risk.The longer answer includes several distinct audiences, each with different needs. Banks Need: Scale underwriting decisions that balance portfolio growth, risk management, and operational efficiency while meeting evolving regulatory expectations.Unique challenge: Banks are modernizing underwriting in an environment of heightened fraud, regulatory scrutiny, and pressure to grow quality loan portfolios. Many are looking to automate more decisions, reduce manual reviews, and integrate credit, fraud, and decisioning capabilities without disrupting existing core systems Credit unions Need: Deliver fast, member-friendly lending experiences while responsibly expanding access to credit and supporting financial inclusion.Unique challenge: Credit unions must compete with larger banks and fintechs while staying true to their member-first mission. They need underwriting strategies that help grow loans, serve a broader range of borrowers, and improve digital experiences despite more limited technology resources and tighter budgets. Fintechs and digital lenders Need: Make instant, data-driven lending decisions that support rapid growth and seamless digital experiences.Unique challenge: Fintechs rely on modern underwriting to evaluate consumers and businesses that may have limited traditional credit histories. They need flexible decisioning, alternative and cash flow data, rapid model iteration, and API-first solutions that allow them to innovate quickly while managing fraud and credit risk. Auto lenders Need: Deliver fast, accurate underwriting and risk-based pricing across a broad range of borrowers.Unique challenge: Auto lenders must balance dealer expectations for instant approvals with the need to accurately assess borrower, vehicle, and fraud risk. Success depends on combining credit, vehicle, and alternative data into a streamlined underwriting workflow. Commercial and small business lenders Need: Confidently underwrite businesses of all sizes, including newly established and thin-file companies, while delivering faster lending decisions.Unique challenge: Small business underwriting often requires evaluating both the business and the owner. Limited commercial credit history, fragmented data, and growing fraud risks make it difficult to assess risk using traditional approaches alone. Blending consumer and commercial data provides a more complete picture and helps lenders confidently extend credit to more businesses. One of the biggest shifts in recent years is that underwriting analytics is no longer reserved for institutions with large in-house data science teams, the right platform puts advanced capabilities within reach of lenders of every size. It takes more than a score Plenty of providers offer a score, a model or a piece of software. But underwriting is a chain — data, analytics, decisioning and it’s only as strong as its weakest link. The chain starts with data, because analytics is only as good as what feeds it. Deep data alone doesn’t get a model into production. The culprit is usually fragmentation. Data lives in one place, models are developed somewhere else and deployment runs through yet another system. Those disconnected systems often means having to recode, revalidate, and re-approve resulting in a slower process while market conditions move. The Experian Ascend Technology Platform was designed to close those gaps, bringing analytics, decisioning and fraud capabilities together in a single environment so the path from insight to live decision is measured in weeks, not quarters. That same connected approach also widens the lens on who can be scored. Expanded data sources like rent, utilities, cash flow and more power scores which can reach consumers and small businesses that traditional data alone can’t, potentially improving access to credit for millions of Americans who are credit invisible or unscoreable today. That’s not just good business; it expands access to fair and affordable credit. Turning predictions into opportunities Underwriting data analytics is how lenders turn data into confident decisions. Faster approvals, smarter risk management, broader access to credit and better experiences for the consumers and businesses they serve. It’s a discipline that demands three things working together: comprehensive data, powerful analytics and decisioning technology. That combination is exactly what Experian was built to deliver. See how underwriting data analytics works

The why behind Experian's acquisition of AtData What happens when a comprehensive email intelligence database joins a global leader in data, analytics and fraud prevention? The acquisition of AtData adds 25+ years of building a complete view of email as an identity signal. Financial institutions can recognize, engage and protect customers unlocking a new standard for the way their teams work and the customer experience. That's what Experian's acquisition of AtData delivers. How we got here Not all email addresses tell the same story. Some are newly created. Some exhibit bot-like patterns. Some are inconsistent with every other signal you have about that person. Imagine a real customer. You have a job. You shop online. You have a primary email from your employer, a personal Gmail you've used for 15 years, and an old Yahoo address you still use for shopping because you've been using it since college. You're an engaged customer who interacts with brands, makes purchases and pays bills on time. But each system sees a different version of you. When you apply for credit, the lender sees one email. When you shop, the retailer sees another. When you sign up for a service, you might use the third. For financial institutions: You slow down the approval process to manually verify identity or approve applicants without the full picture. For retailers: You can't tell which version of "customer" is the most engaged, so you either over-mail or under-serve. For fraud systems: Sees a new account created under one email and flags it as suspicious because it doesn't have the history. This was the original problem AtData was built to solve in 1999. Twenty-five years later, that problem didn’t go away, it became more complex. Email fragmentation and device sharing are more common, and identity theft is more sophisticated. Capabilities that now work together Experian has built sophisticated identity and fraud solutions backed by consumer data resources and decades of expertise in credit and risk. AtData brought the ability to assess whether an email address is trustworthy, reachable and consistent—at scale, in real time. Experian is now making email intelligence foundational, not optional. This matters for: Fraud prevention and risk management: Distinguishing a returning customer from a new threat. Knowing whether an email is newly created, exhibiting bot-like patterns or inconsistent with other identities is crucial. Compliance: Building audit trails that can explain identity decisions. Email data history and behavioral signals create the documentation needed to defend your decisions. Credit: Verifying identity in a world where traditional signals are shifting. Email signals provide a persistent, durable identifier that confirms who someone actually is. Marketing: Reaching the right person across email, mail and digital channels. Email intelligence reveals which addresses are actively engaged and reachable. Research shows email remains one of the highest-ROI marketing channels outperforming paid search and social advertising1. The problem every marketer faces: You end up burning budget on addresses that bounce, are unmonitored or are associated with users who never open mail. For credit marketing specifically, email enables faster, more targeted delivery of firm offers across channels, something that's increasingly important in a post-cookie world. "Email is a persistent identifier in a fragmented world. It's what connects a person's postal address, phones, devices, behaviors—the full picture of who they are. By embedding that into our infrastructure, we're not just adding another data point. We're fundamentally improving how businesses understand who their customers are."- Ashley Knight, Senior Vice President, Financial Services and Data Why now? AI is reshaping how decisions are made in every industry. Models are getting faster, more automated and more embedded in core workflows. But AI is only as effective as the data behind it. Fragmented data + fast models = faster, larger-scale misclassifications. In an era of synthetic identities, AI agents, deepfakes and AI-generated activity, the value of durable, persistent, real-world data signals has increased dramatically. Deloitte’s Center for Financial Services projects that generative AI could drive fraud losses in the U.S. up to $40 billion by 2027, a 32% growth rate since 2023. And email sits at the center of it with business email compromise already being one of the most common and costly fraud types. People change phones, move homes and swap devices, but they often hold onto their email for years. That's the signal that protects your business, and the one we've built into the core of how we help you make decisions with confidence. View the press release here

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Fintech growth is returning, but growth alone is no longer enough to separate market leaders from the rest. The next stage of fintech will be shaped by how well organizations understand the consumers they serve, how accurately they assess risk and how consistently they make decisions across the customer lifecycle. That requires more than speed, more data or a single new model. It requires a unified view of the consumer that brings together identity, credit and behavioral signals into one decisioning strategy. Experian’s 2026 State of Fintech Report identifies partnerships, data and fraud as three forces shaping the next phase of fintech growth. The report also makes a clear point: institutions that integrate these forces into cohesive strategies will be better positioned to grow with confidence. For many fintechs, the challenge is not a lack of innovation. It is the increasing complexity of turning innovation into scalable, explainable and profitable growth. Fintech organizations span a wide range of maturity, from early-stage startups to scaled lenders, and many are experimenting with new products, technologies and customer engagement models at the same time. That creates opportunity, but it also creates pressure to make more disciplined decisions. The market is rewarding institutions that connect product strategy, risk management and customer experience in a more coordinated way. This is why the unified consumer view is becoming so important. It helps fintechs turn fragmented signals into consistent decisions that support both growth and resilience. Why a unified consumer view matters now A unified consumer view means bringing together the signals that define a customer’s identity, credit behavior, financial capacity and risk profile. It moves fintechs away from isolated decision points and toward a more connected picture of the customer across origination, account management and servicing. This matters because consumer behavior is becoming more fluid, fraud is becoming more sophisticated and product strategies are becoming more specialized. A customer may appear strong through one lens and risky through another. An application may pass an onboarding check, but later show behavior that suggests emerging fraud or repayment stress. Without a connected view, those signals may stay trapped in different systems or teams. The 2026 State of Fintech Report highlights this shift across several areas. Fintechs are managing credit cards and unsecured personal loans with greater precision, recognizing that each product requires different strategies and risk controls. Credit cards require ongoing account management because exposure continues after origination. Unsecured personal loans follow a fixed repayment structure, which makes underwriting precision especially important at the point of origination. These differences show why a one-size-fits-all strategy cannot support modern fintech growth. A unified consumer view helps lenders apply the right data, risk framework and customer strategy to the right product at the right time. Siloed decisions create blind spots Many fintechs already use multiple sources of data. They may rely on traditional credit data, alternative data, fraud tools, cash flow information, identity verification and internal account performance data. If those signals are managed separately, the organization may still lack a clear view of the customer. Data can become fragmented. Risk teams can reach different conclusions than fraud teams. Product teams can pursue growth without a full understanding of emerging portfolio pressure. The State of Fintech Report points out that fintech competition is increasingly defined by the ability to align data strategies with decision frameworks. That means data is not just a support function. It is becoming central to growth, risk management and customer experience. Organizations are investing in richer datasets and more advanced analytics, but the differentiator is how effectively those inputs are operationalized. This is where many fintechs still have work to do. The value comes not from any single dataset, but from how signals are layered, interpreted and applied together. For example, a lender may understand a consumer’s credit score, but that does not always reveal broader financial behavior. Cash flow data may add insight into income and expenses, but it needs to be categorized and normalized to support reliable decisions. Identity signals may help detect fraud, but they become more powerful when combined with credit and behavioral data. A unified view brings these inputs together so fintechs can better determine whether a customer represents a growth opportunity, a fraud risk, an emerging credit risk or a borrower who needs a different product experience. Product complexity requires better decisioning The need for a unified consumer view becomes even clearer when looking at how fintechs manage different credit products. Fintech lenders continue to originate approximately 1.5 unsecured personal loans for every one credit card, which reinforces the importance of both products within portfolio strategy. Credit card originations continue to grow moderately while unsecured personal loan originations have slowed after tighter lending standards. These patterns suggest that fintechs are not simply shifting from one product to another. They are becoming more mature in how they manage each product based on its structure, risk profile and consumer use case. Credit cards and installment loans behave differently. Credit cards introduce ongoing exposure and require active account management, line management and monitoring of utilization behavior. Unsecured personal loans carry fixed terms and structured repayment schedules, which makes origination quality especially important. For fintechs, this means product strategy and risk strategy must be tightly connected. The same consumer may need to be evaluated differently depending on the product, loan amount, repayment expectations and observed behavior. A unified consumer view gives lenders the context needed to make those differences actionable. This is also where segmentation becomes more sophisticated. The State of Fintech Report’s loan segmentation framework connects strategy, risk and data advantage across small-dollar, mid-tier and large-ticket loans. Small-dollar lending can support thin-file acquisition, but may require alternative data and stronger identity visibility. Mid-tier lending may involve debt consolidation and cash flow pressure, where transaction insights and trended data can be particularly useful. Large-ticket lending can support higher-value growth, but it also creates greater exposure and may require a fuller combination of credit, fraud and identity signals. This kind of framework helps fintechs align product strategy with risk and data strategy in a more deliberate way. Fraud is making the unified view even more urgent Fraud is another reason fintechs need to move beyond siloed decisioning. Fraud is becoming more complex across the customer lifecycle. Synthetic identities, first-party misuse and AI-driven threats are reshaping the risk landscape. Traditional controls that focus primarily on onboarding are no longer enough. Effective strategies now require continuous monitoring across account access, transactions and servicing. That shift changes how fintechs should think about customer intelligence. Fraud is no longer something that only happens at the point of application. It can emerge later through account behavior, suspicious activity or patterns that look normal when viewed in isolation. Advanced identity signals, including email intelligence, are becoming more central to fraud prevention because they add context that traditional data may not capture. The report also highlights Experian’s acquisition of AtData as part of a broader recognition that email-based identity signals represent a critical layer in digital identity and fraud detection.   The takeaway for fintech leaders is clear. Identity, fraud and credit risk cannot be treated as separate problems. A customer who appears creditworthy may still present identity risk. A fraud signal may also influence credit exposure. A repayment pattern may reflect financial stress, misuse or both. A unified view helps lenders evaluate these signals together so they can make decisions with more confidence and less friction for legitimate customers. Trust is becoming a growth strategy Trust has always mattered in financial services, but fintechs now need to think about trust as a measurable part of decisioning. Customers expect fast applications, seamless experiences and fair outcomes. Regulators and internal governance teams expect transparency, explainability and consistency. Business leaders expect growth without unnecessary exposure. These expectations are difficult to meet when data and decisions are fragmented. The State of Fintech Report’s 2026 action playbook identifies trust as a function of decision accuracy, identity confidence and customer transparency. That framing is important because it moves the conversation beyond speed alone. A fast decision is not valuable if it approves the wrong customer, declines a good customer or creates unnecessary friction in the wrong place. Fintechs should evaluate where friction improves outcomes, such as preventing fraud or identifying risk, and where it creates unnecessary loss of good customers. For many lenders, the path forward is not removing friction everywhere. It is applying the right level of friction at the right moment based on a clearer view of the consumer. This is where unified decisioning becomes a competitive advantage. It allows fintechs to create experiences that feel faster and more relevant while still protecting the portfolio. It supports better segmentation, more informed offers and more consistent risk treatment. It also gives teams a shared understanding of why decisions are made, which is essential as AI and automation become more embedded in lending workflows. What should fintech leaders do next? A unified view of the consumer is not built by adding one more tool or one more dataset. It requires a decisioning strategy that connects data, analytics, fraud, identity and product objectives. Fintech leaders should start by evaluating where their current decisioning frameworks fall short. Are credit and fraud signals looked at together? Are cash flow insights being used consistently? Are identity signals monitored after account opening? Are decisions explainable across teams and channels? The 2026 State of Fintech Report recommends prioritizing experimentation tied to measurable decision lift and model performance. This means testing combinations of credit, alternative, cash flow and identity signals to determine where incremental data improves response rates, approval rates, early-loss reduction and fraud mitigation. It also means treating data and decisioning as connected priorities, with a focus on signal quality, integration and measurable impact. The goal is not to collect more inputs for the sake of volume. The goal is to understand which signals improve outcomes and how those signals should be applied at scale. For fintechs, this is the next competitive frontier. Growth will continue to depend on product innovation, customer acquisition and speed to market. But the lenders that separate themselves will be the ones that can connect those growth priorities to a stronger decisioning foundation. That requires a consumer view that is broader than a credit profile, deeper than a fraud check and more actionable than a data warehouse. It requires a unified framework that helps lenders understand who the customer is, how the customer behaves and how risk may change over time. Download the 2026 State of Fintech Report The next phase of fintech will not be defined by a single innovation. It will be defined by the ability to connect identity, credit and behavioral data into more confident decisions across the full customer lifecycle. Fintechs that build this unified view will be better positioned to grow, manage risk and strengthen customer trust in a more complex market. To explore the trends shaping fintech growth and decisioning in 2026, download Experian’s 2026 State of Fintech Report. Read now To learn more about how Experian partners with fintechs, visit www.experian.com/fintech. Learn more

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Every business makes decisions about people and transactions all day long. Should we approve this loan? Is this purchase fraud? Which customer should get this offer, and what should it be? For a long time, those decisions were made in one of two ways: a person reviewed each case by hand, or the company wrote fixed rules, like "approve anyone with a credit score above 700." Both work. Both also leave value on the table. The manual review is slow and hard to scale. The fixed rule can turn away good applicants and is slow to adapt when the market shifts. AI decisioning is a third way. What makes AI decisioning work Instead of relying on a single reviewer or a rigid rule, automated decisioning uses models that learn from data — studying how thousands of past cases turned out, finding the patterns that predict an outcome, and applying them to each new decision, often in real time. The result is faster, more consistent decisions. But a model on its own isn't the whole story. Getting real value from AI decisioning takes good data to learn from, AI analytics to generate insights, the tools to act on it and the governance to keep it compliant. What we've found is that the pieces only pay off when they work together, and that is where we're built differently. A model is only as good as what it learns from, and we pair your data with one of the deepest views of consumer and commercial credit: decades of full-file history and vetted attributes. Then we give you the tools to act on it. Use cases across your business Whether you're trying to grow your customer base, reduce fraud, manage lending risk, or improve collections, automated decisioning brings all the pieces together to make more accurate, consistent and explainable decisions at scale. Fraud and Identity A fraudulent transaction that slips through costs money and erodes trust. Rules are static, and fraudsters move fast. They'll probe boundaries, find the blind spots and move to the next scheme. By the time the rules are updated, they're already three steps ahead. How AI decisioning changes this: AI fraud detection with real-time risk scoring and decisioning across transactions and customer interactions Intelligence that continuously learns from results to help adapt fraud strategies as threats evolve Reduced false positives and less friction for customers at account opening and checkout Identity verification tools that confirm someone is who they say they are without slowing down the experience Credit and Lending Loan approval is where the relationship begins. Credit risk decisioning helps lenders find that delicate balance between approving enough people to grow, but carefully enough to manage risk. Missing that balance means turning away good customers or taking on losses that are difficult to absorb. How AI decisioning changes this: Increased approval opportunities for creditworthy applicants without increasing overall risk Models you can update and deploy quickly as market conditions change, rather than waiting months Ability to run "what-if" scenarios to test how a new strategy would have performed on your historical data before putting it live Collections Which customer should your team reach out to today? Through which channel? What kind of message? If you reach out too aggressively, you push someone who might have recovered into default. If you wait too long, you lose them. If you call someone at work, they resent you; if you text, they might ignore it. If you offer a payment plan, they might accept it, but only if the terms make sense to their financial situation. How AI decisioning changes this: Optimized next-best-action and contact-channel strategies for each individual customer Improved recovery potential through better targeting Less time spent on accounts with a lower propensity to pay, freeing your team for higher-impact cases Ability to segment and test new strategies before rollout Customer Acqusition Finding the right customers is about reaching the right people with the right offer at the right time. To stay competitive, it’s now a requirement to balance growth with risk while creating a seamless experience converting prospects into customers. How AI decisioning changes this: More precise prospect targeting using credit, behavioral, and alternative data, where permitted, to identify consumers most likely to respond Personalized offers delivered in real time Dynamic decision strategies that can be updated quickly as market conditions and customer behavior change Ongoing testing and optimization of acquisition strategies to improve campaign performance and support customer lifetime value Driving results with AI decisioning Every customer interaction is a decision. Businesses that can adapt quickly will be better positioned to grow, manage risk, and deliver the experiences customers expect. The technology will continue to evolve, but the goal remains the same: making informed decisions that balance business objectives, risk, and customer experience. Learn more about our decisioning software

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