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Artificial intelligence (AI) can raise the value of an organization’s data, but it also raises the standard that data needs to meet. According to Gartner, only 14% of leaders say their data is AI-ready.  A dataset might be accurate enough for a monthly report or a traditional analytics workflow and still fall short when it is used to train, inform or operate an AI system. That is because AI readiness depends not only on whether data is clean, but also on whether it is appropriate for the specific use case, sufficiently complete and representative, properly governed and maintained as conditions change. For financial institutions, that distinction matters. Banks, credit unions and fintechs may use AI across areas such as credit decisioning, fraud detection, risk management and customer engagement. Each use case puts different demands on the underlying data. Understanding those demands is the first step toward building an AI-ready data foundation. What is AI-ready data?  AI-ready data is data that has been prepared and managed, so it is fit for an intended AI use case.  There is no single condition that makes every dataset ready for every AI application. A dataset suitable for a marketing model may need different attributes, controls and levels of completeness than data supporting a credit risk or fraud workflow.  A practical way to think about AI readiness is through four characteristics: Reliable: Is the data accurate, complete, valid and consistent enough for its intended purpose?   Relevant: Does it contain the right information, context, definitions, attributes and level of detail for the AI application?   Safe: Are permissions, sensitive information, restricted attributes and governance requirements understood and appropriately managed?   Representative: Does the data reflect the people, entities, behaviors or processes the AI will encounter when it is used?   This is where AI-ready data differs from simply having clean data.  Traditional data quality often focuses on issues such as invalid values, inconsistent formats, duplicates and missing information. Those fundamentals still matter. But AI introduces additional questions: Does the dataset represent the population the model will encounter? Can teams understand where the data came from and how it changed? Is its use permitted? Are changes in data quality or distribution being monitored?  In other words, clean data is an important foundation. AI readiness asks the next question: Is this data fit for this AI use?  How to make your data AI-ready: a practical framework  Preparing data for AI does not have to mean rebuilding your data environment from scratch. A more practical approach is to assess the data against the intended use case and address the gaps that could affect trust, usability or governance.  Resolve identities and establish trusted records  AI systems can struggle when the same customer, account or entity appears differently across systems.  Variations in names, addresses and other identifying information can create duplicates or fragmented records. That makes identity resolution and data matching an important starting point.  Matching and deduplication can help organizations identify records that refer to the same entity and create a more trustworthy view of the data. Depending on the use case, this may mean establishing trusted records (or golden records) that downstream analytics and AI processes can use more consistently.  The goal is not simply to eliminate duplicates. It is to give an AI workflow a clearer understanding of who or what each record represents.  Standardize and maintain data hygiene  Consistency matters when AI systems need to interpret information across multiple datasets.  Different formats, incomplete fields, outdated contact details or inconsistent naming conventions can make it harder to connect and use information reliably. Data hygiene processes can help validate, cleanse and standardize information before it enters AI workflows. This foundation also makes matching, enrichment and governance more effective because records are being evaluated from a more consistent starting point.  The right standard still depends on the use case. Rather than asking whether every field is perfectly clean, ask which data quality dimensions could materially affect the AI application.  Enrich data when important context is missing  AI can only work with the information it has available.  In some cases, internal data may be accurate but incomplete for the intended purpose. Data enrichment can add relevant context or attributes to existing data, helping create a more complete dataset for analysis and decision-making.  The principle here is relevance, not simply volume. More data is not automatically better data.  Before adding attributes, determine what information the use case actually needs. An enrichment strategy should help fill meaningful gaps rather than expand the dataset without a clear purpose.  Build governance and lineage into the process  For AI, knowing what a data point says may not be enough. Organizations also need to understand where it came from, what it can be used for and who is responsible for it.  Governance can help document elements such as:  Data ownership and stewardship   Approved purposes and permitted uses   Policies and controls   Data lineage and transformations   Approvals and audit information   Sensitive or restricted information   These considerations are especially important in regulated industries such as financial services, where organizations may need evidence of how information moved through a process and how it was managed.  Making governance part of the AI-data workflow can also help teams answer a fundamental question later: Why did we trust this data for this use?  Monitor data readiness continuously  AI readiness is not a one-time cleanup project.  Customer information changes. New records enter systems. Data sources and business processes evolve. The characteristics of a dataset can also change over time. Ongoing profiling and quality monitoring can help teams identify changes earlier and determine whether data remains fit for the AI process it supports.  That means measuring data health against the use case, not simply running a cleanup before launch and assuming the work is finished. A sustainable AI-ready data practice creates a feedback loop to assess, improve, monitor and reassess.  AI-ready data in financial services  Financial services offer a useful picture of why AI readiness depends on context. Consider three potential applications.  Credit decisioning  For AI decisioning, reliable and well-governed data can be foundational to how organizations develop and operate decision processes.  The data needed will depend on the particular credit decisioning model and decision being made. Teams may need to consider completeness, consistent definitions, representativeness, lineage and controls around permitted use, not simply whether the source records pass conventional quality checks.  Fraud detection and identity  Fraud signals often come from multiple systems and interactions. Connecting information to the correct person, device, account or entity can therefore become particularly important.  Strong identity resolution, matching and data quality practices can create a more consistent foundation for AI fraud detection and other fraud analytics.  As autonomous technologies become part of digital interactions, identity questions are also expanding beyond people. Learn more about AI agent identity verification and the emerging challenges associated with establishing confidence in AI-driven transactions.  Marketing and customer engagement  AI-driven marketing may rely on information from customer, account, interaction and third-party data.  Identity resolution can help connect fragmented records, while enrichment can add relevant contexts where appropriate. Data quality monitoring can then help organizations maintain the usefulness of that information as customer records and conditions change.  Across these use cases, the underlying principle is the same: the definition of “ready” follows the business purpose.  How we can help build an AI-ready data foundation  Becoming AI-ready requires more than fixing individual errors. Organizations need a repeatable way to understand, improve, govern and monitor the data feeding important business processes.  We support those steps through data quality solutions that can help organizations address different parts of the readiness framework. For example, identity resolution and matching can help connect records that represent the same entity.   Aperture Data Studio brings data quality and governance capabilities together in a data intelligence solution. It can support activities including profiling, cleansing, standardization, matching, deduplication, enrichment, cataloging, governance and monitoring, helping organizations manage data as an ongoing business asset rather than a one-time preparation task.  For organizations considering AI, that integrated approach can help shift the conversation from “Is our data clean?” to a more useful question: Is our data fit, trusted and governed for the AI use case we want to pursue?  Explore the data intelligence platform

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Experian NeuroID for Account Defense has been named Best Authentication Innovation in Datos Insights’ 2026 Fraud & AML Impact Awards.

The average credit union manages between 100-300 vendor relationships, and nearly a third have no dedicated staff managing them. What was supposed to drive efficiency is stunting growth and compromising the member experience. Application volume is continuing to rise, and consumers expect a loan decision at digital speed. But for credit unions, meeting that expectation isn't the only goal. They must also continue to make the right lending decisions while also prioritizing risk management. The institutions pulling ahead aren't the ones with the most tools. They're the ones that have turned credit, fraud and decisioning into a single, coordinated system. A single loan decision touches: Credit data Identity verification Fraud signals Decisioning logic Reporting Rarely does all of that live in one place. More often, it’s stitched together across separate platforms, each with its own contract and its own support line. When a credit view and a fraud view are generated separately, they must be reconciled instead of read together in the same instant. Every handoff is a place where a decision slows down and a signal gets missed. It's why a fraud pattern that would be obvious next to the credit file often surfaces only after the funds are already gone. Fragmentation also makes it harder to optimize lending strategies over time. When data, analytics and decisioning are disconnected, credit unions have less visibility into what's working, what's changing and where strategies need to be adjusted. An integrated decisioning ecosystem closes those seams by design, with credit, fraud and lending strategy running on one platform and accessed through a single Application Programming Interface (API). Identity and compliance capabilities can be an integrated decisioning flow. The data doesn't need to be reconciled after the fact because it was never separated in the first place. Stop overengineering, start removing friction When decisioning underperforms, the instinct is to build something more custom: a proprietary model, tighter segmentation, more configuration.  Custom modeling has its place for institutions with the scale and data to justify it. But often, for small or mid-sized credit unions, the bigger opportunity is a flexible decisioning strategy that uses a strong, continuously refreshed model. This shouldn't be either/or. The right approach gives credit unions a strong decisioning strategy today, with the ability to introduce more customized analytics and modeling as their needs evolve.  The same principle applies to strategy management: credit unions can gain greater control through self-service capabilities as they build a more sophisticated data science team.   That kind of flexibility is important as credit unions modernize their decisioning infrastructure. Research surveying more than 2,000 senior credit risk and fraud decision-makers across 11 countries found that organizations investing in unified platforms and modern infrastructure are moving faster and realizing the greatest Return on Investment (ROI).   As their needs evolve, credit unions can adjust decisioning strategies without introducing another layer of technology or relying on third parties to make every change. Until self-service is the right fit, choose a partner that understands what makes your credit union’s membership and risk appetite different from others.  Same member, same afternoon, faster answer Consider a credit union growing its auto and personal loan originations.  A member applies for an auto loan on Saturday afternoon. Credit data, identity and fraud signals are read together in a single real-time pass instead of separate reviews:  Most applications resolve immediately, approved or declined based on a more comprehensive view of the applicant. A smaller number are flagged for quick step-up verification and cleared within minutes. Only genuinely ambiguous cases reach an underwriter, who is working from one complete picture instead of reconciling multiple signals. The member has an answer before the weekend is over. The credit union books the loan before competitors even open on Monday. Built to scale This kind of process holds up today. However, the better test is whether it holds up eighteen months from now when application volume has grown, new products are live and the data flowing through the system looks nothing like it does right now. Future-proofing isn't only about handling more volume. It's about having room to be nimbler and make adjustments as quickly as the market changes. A credit union that relies on standard models within a decisioning strategy may want more customized analytics and greater control over its strategies tomorrow. Connect with us With more than three decades of credit expertise and one of the industry's largest data footprints, Experian helps credit unions do more than solve this year's friction. We're helping to build a decisioning foundation that scales, so growth becomes something the system absorbs, rather than something it must be rebuilt around. Explore More

For decades, credit data has given lenders a trusted foundation for assessing risk. Now, consumer-permissioned cash flow data is adding another layer of insight into consumers’ financial lives, including income, spending, liquidity and changing financial obligations. The opportunity is significant. Nearly one in five consumers lacks access to or has damaged credit. New Experian research also shows that 60% of consumers who have previously been denied credit or received less favorable terms than expected believe the outcome would have been different if lenders had considered their recent income and banking activity alongside their credit history. 1,2 For lenders, cash flow insights can provide additional context about a consumer’s financial capacity and support more informed underwriting decisions. The next challenge is making those insights easier to use consistently and responsibly at scale. What are cash flow insights? Cash flow insights are derived from consumer-permissioned banking data and provide visibility into how money moves into and out of a consumer’s accounts. Depending on the information available, cash flow data can provide insight into financial behaviors such as: Income and income stability: Regular and irregular income streams and changes over time Spending and recurring expenses: Everyday spending patterns and ongoing financial obligations Account balances and liquidity: Available funds and changes in account balances Debt obligations: Loan payments and other recurring debt commitments Cash flow patterns: How money moves into and out of accounts over time Traditional credit information provides a rich history of how consumers have managed credit. Cash flow insights add context about their current financial activity and capacity, providing lenders with a more complete view of financial health. Cash flow insights can also add predictive value to risk assessment. Experian analysis shows Cashflow Score can deliver up to a 25% lift in predictive performance over conventional credit scores when tailored to targeted risk tiers.3 Cash flow data is having its moment In this Forbes article, Sandy Anderson, Experian's Executive Vice President of the Data Office and Operations, explains why more data alone isn’t enough — and what it takes to turn cash flow insights into trusted, actionable intelligence. Read the article How lenders can put cash flow insights to work at scale As interest in cash flow insights grows, the conversation is moving beyond what the data can reveal to how financial institutions can use it consistently and responsibly. That requires standardized consumer reporting, clear governance and decision-ready insights that can work within established lending processes. The launch of Experian Cashflow Data Bureau at Vision 2026 reflects this shift from access to adoption. The new consumer reporting agency operates under the Fair Credit Reporting Act (FCRA) and is designed to help financial institutions adopt consumer-permissioned cash flow insights at scale. A key part of that foundation is the cash flow consumer report, which organizes consumer-permissioned banking information into a standardized consumer report regulated under the FCRA. This gives lenders a consistent foundation for incorporating cash flow information into lending decisions while providing consumers with access to the information in their report. Watch this video to see how Experian Cashflow Solutions turns consumer-permissioned cash flow data into decision-ready attributes, scores and custom analytics, backed by bureau-grade governance. Turning greater visibility into better decisions The next evolution in credit isn’t about choosing between traditional credit and cash flow data. Each provides a different perspective. Traditional credit information offers deep insight into how consumers have managed credit over time. Cash flow insights can add context about financial activity and capacity. Bringing those perspectives together can give lenders greater visibility into consumers’ financial health and help them better distinguish between risk and resilience, support more informed decisions and identify opportunities to responsibly expand access to credit. As cash flow adoption continues to grow, its value will come from how effectively it complements the information lenders already use. The opportunity isn't simply to see more data. It's to turn that additional visibility into smarter, more informed decisions. Explore how Experian Cashflow Data Bureau is helping financial institutions put consumer-permissioned cash flow insights into practice at scale. Learn more [1] Federal Reserve Bank of New York, The Market for Credit Building: Demand, Product Characteristics and Consumer Uses, 2026. [2] Experian commissioned Atomik Research to conduct an online survey of 2,000 U.S. adults age 18+. Fieldwork took place July 16–21, 2026. Margin of error: +/- 2.2 percentage points at a 95% confidence level. [3] Based on Experian analysis when Cashflow Score is compared with conventional credit scores, tailored to targeted risk tiers. Predictability based on KS.

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Leaders from across the industry came together to explore the technologies, strategies and opportunities shaping what’s next.

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Identity intelligence and alternative data can help lenders validate consumers and support more informed decisions across the customer lifecycle.

Customer acquisition strategies are constantly evolving toward more precise targeting. From a marketing lens, you can track every step, optimize communication channels and still miss the person most likely to convert. Attribution can tell us which channels work and automation can make marketing spend more efficient. But both assume we know who is actually on the other end. Financial institutions are learning that finding audiences and targeting them is no longer the biggest challenge. As acquisition optimization marketing becomes more sophisticated, teams can measure and act on more signals than before. What they can't always know is whether the person on the receiving end is real. Customer acquisition has evolved into an identity problem. The challenge is not that every questionable signal represents malicious activity. It's that acquisition systems must make increasingly intelligent decisions with an imperfect understanding of who they're actually engaging. When identities are fragmented, duplicated, temporary or synthetic, optimization becomes a question of trust as much as targeting. When your signals don't reliably identify customers The customer journey often includes searching, filling out a form, creating an account, requesting a quote and subscribing. All of these signals work well when identity is relatively stable.  However, financial institutions are finding that these signals are becoming less reliable. A single person can operate across multiple personas, devices, browsers, aliases, accounts and intermediaries while several apparent “people” may actually represent one underlying actor. Financial instituions are finding: Fragmented customer signals Difficulty distinguishing an old account from a new one Different digital pathways associated with the same individual Signals that are generated by automation Real customers getting flagged because signals are too thin to evaluate confidently Legacy signals continue to be challenged Marketing has historically treated intent as a valuable signal because intent was relatively difficult to produce. A search required human intent. A form required someone to fill it out. An inquiry implied a meaningful amount of human effort. Financial institutions are already combating AI-enabled fraud, and now marketing teams are starting to face it on a massive scale. AI can mimic human behavior by researching products, comparing prices, filling out forms, creating accounts and signing up for services. A valid email address is no longer enough. Marketers need to know: How long has it existed? How recently has it been active? Does its activity appear consistent or suddenly anomalous? Has it gone dormant and returned? Is it associated with patterns that suggest stability or unusual behavior? How to build on your strongest signal Email remains one of the most persistent identifiers in digital commerce, following people across devices, platforms, transactions, subscriptions, accounts and years of activity. For over two decades, this has shaped how AtData thinks about identity. Now, as part of Experian, it’s shaping how an entire platform and team approach identity. A marketer doesn’t need every prospect to have existed online for twenty years. But understanding whether a newly acquired prospect has meaningful identity context can dramatically improve the quality of the decision being made around it. Better identity intelligence can help organizations reduce unnecessary friction by improving their ability to recognize legitimate customers. With a strong identity foundation, marketing teams can better address: Which audiences are more likely to convert? Which leads are high quality? Which channels are driving incremental growth? What do the best prospects look like? The value isn't simply having an email address. It's understanding the history and behavioral context associated with it. That context can provide a stronger digital identity signal, helping marketers understand how long they have been active, whether its behavior is consistent with that of a real person and whether current activity aligns with past patterns. It continues to be one of the most persistent identifiers in digital commerce. An infrastructure built for what's coming The acquisition of AtData by Experian reflects a fundamental shift in how identity infrastructure needs to work. Experian's scale and decisioning capabilities, combined with AtData's real-time email intelligence, create a strong platform. Read more about the why behind the acquisition and see how email works as an identity anchor for fraud prevention. Contact us to learn about our customer acquisition solutions

Manual processes are quietly expensive. Every handoff between teams, every file transfer waiting in a queue and every decision that sits on someone's desk adds cost, introduces risk and slows the customer experience. For financial institutions, those delays translate directly into lost revenue and eroded margins. That’s why workflow automation is becoming critical for financial institutions looking to stay competitive. Done well, it doesn't just make existing tasks faster. It reshapes how decisions get made across the entire customer lifecycle, from the first marketing touch to account servicing and beyond. What is workflow automation? Workflow automation is the use of technology to run a sequence of tasks, decisions and handoffs with minimal manual intervention. Instead of a person moving work from one step to the next — pulling data, applying a rule, routing an account and sending a communication — software executes those steps automatically based on defined logic and real-time data. For financial institutions, workflow automation usually combines four ingredients: Data Connecting to the internal and external data sources that inform a decision. Analytics Scores, models and attributes that turn raw data into insights. Decisioning A rules engine that determines the right action for each customer or account. Execution The operational layer that carries out the action, whether that's an offer, a credit line change or outreach. The benefits of workflow automation The value of automation goes well beyond "doing the same thing faster." The benefits financial institutions consistently see include:Greater efficiency and lower operating costsAutomation frees underwriters, analysts and agents to focus on exceptions and high-value work rather than repetitive processing. Faster, more consistent decisionsA credit application that once waited in a queue can be assessed in real time against consistent, auditable policies, improving both the applicant's experience and portfolio quality. Better customer experiencesAutomation enables financial institutions to personalize communications at the point of interaction and offer the self-service options that many people now prefer. Improved compliance and governanceReduce the risk of costly compliance failures with built-in controls, audit trails and guided workflows. ScalabilityRespond to changing volumes without sacrificing speed, consistency or the customer experience. Where workflow automation makes the biggest difference Workflow automation tends to deliver the most value where decisions are frequent, repeatable and informed by data. In financial services, those opportunities exist across the customer lifecycle. Onboarding Onboarding is a customer's first experience of your organization, and it's also where friction can cause customers to abandon the process and turn to another provider. Forty percent of U.S. consumers have considered walking away from opening a new account when the process felt burdensome.1 An automated onboarding workflow can bring together document verification, device intelligence, behavioral analytics, credit attributes and more, then orchestrate them into a single decision. The result is a lower-friction experience for the customer and a consistent, auditable process. Once customers are on the books, serving them well means making continuous, high-volume decisions: credit line changes, cross-sell and up-sell opportunities, risk monitoring and retention actions. Automation makes it practical to run these recurring decisions consistently across an entire portfolio, using a holistic view of each customer that draws on multiple scores and attributes. Lending The underwriting process is a great example of how workflow automation can help prevent applicants from waiting days for an answer. Loan origination and credit decisioning capabilities are designed to create a seamless review process across consumer and commercial lending. After automating originations with our solutions, Michigan State University Federal Credit Union cut application processing time to under 24 hours. Fraud Financial institutions are checking fraud at every touchpoint, and the standard for AI fraud detection continues to rise as fraudsters use AI to slip under the thresholds of any single detection tool. Rather than running fraud checks in isolation, an automated workflow can run multiple fraud and identity verification services in parallel and weigh signals together. A fraud decisioning platform connects signals across internal systems, Experian data and third-party services, allowing teams to stay on top of evolving threats. Build a strong foundation for workflow automation Workflow automation can connect these stages, creating a consistent decisioning framework. What ultimately separates good automation from great automation is the quality of the data and decisioning software underneath it. An automated workflow is only as good as the information feeding it. That's where our comprehensive credit, alternative and identity data with the tools financial institutions need to act on it. Learn more here FAQs How does automated decisioning improve credit decisions? Automated decisioning applies consistent logic to every account in real time or in bulk, enabling faster and more informed decisions, quicker responses to market and regulatory changes at the point of interaction. What is workflow automation in financial services? It's the use of software to execute sequences of data gathering, analysis, decisioning and action. Does workflow automation replace human judgment? No. The goal is to automate routine, high-volume decisions so skilled staff can focus on the exceptions and complex cases that genuinely require human judgment. For example, a sensitive collections conversation or a nuanced underwriting call. Are we still compliant with regulations if we use an automated workflow process? Well-designed platforms include built-in governance, audit trails and compliance controls that help institutions align with requirements like the Fair Credit Reporting Act (FCRA) and other regulatory guidelines improving compliance compared with manual processes. How long does it take to implement? It varies by solution and scope, but modern cloud-based platforms are designed for fast onboarding and limited IT involvement. 1Global Fraud Snapshot 2025: Opportunities and challenge in identity, fraud and financial crime