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Not long ago, every online transaction shared a simple assumption: there was a human on the other side of the screen. Someone browsing, clicking and confirming a purchase. That assumption is starting to break. Today, Artificial Intelligence (AI) agents can search for products, compare options, make payments and even complete transactions on behalf of users, without the need for human supervision. This shift, often called agentic commerce, is redefining how decisions are made and how transactions occur. But it also introduces a new and urgent question: how do you trust something that isn’t human? That’s where Know Your Agent (KYA) comes in. Understanding Know Your Agent At its core, Know Your Agent is a framework for establishing trust in AI-driven interactions. It extends traditional identity verification into a world where software, not people, is acting. Instead of asking “Who is the customer?”, KYA asks a broader question: Who is this agent, who is it acting for and is it authorized to act? In practice, KYA connects three critical elements: The human A verified individual The agent The authenticated AI agent acting on behalf of the consumer The intent What the agent is trying to do as instructed by the consumer This connection ensures that every action taken by an AI agent can be traced back to a real, verified person and that the action itself is legitimate. Why KYA is emerging now The rise of AI agents isn’t theoretical; it’s already happening. From shopping assistants to financial co-pilots, agents are beginning to act autonomously in ways previously reserved for humans. But this evolution exposes a gap in today’s trust models. Most fraud prevention, identity verification and risk systems are designed to evaluate human behavior. Additionally, most merchant checkout processes use risk controls focused on identifying the consumer interacting with the merchant site or application (app). When an AI agent initiates a transaction, those signals become harder to interpret. Is it a trusted assistant acting on behalf of a real customer, or a sophisticated bot attempting fraud? KYA is emerging to solve exactly this problem. A new trust layer for agentic commerce Agentic commerce changes not just who transacts, but how trust is established. In a traditional transaction, trust is built through familiar signals, such as login credentials, device data, location data and behavioral patterns. In an agent-driven interaction, those signals are abstracted away. The agent acts, but the human intent sits behind it. Know Your Agent introduces a new trust layer that bridges this gap. It allows businesses to answer critical questions in real time: Who is the consumer behind the agent? Is this authenticated agent linked to that consumer? Has the user authorized this specific action? Is the agent behaving consistently and within its permissions? Can this transaction be trusted? Without these answers, agentic commerce introduces risks like fraud, misrepresentation and unauthorized activity.  With KYA, those risks become manageable and, more importantly, scalable. From KYC to KYA: an evolution of identity For decades, organizations have relied on Know Your Customer (KYC) to verify people and reduce fraud. But KYC alone isn’t enough in a world where AI agents act independently. KYA doesn’t replace KYC; it builds on it. If KYC verifies the individual, KYA verifies the relationship between the individual and the agent acting on their behalf. It adds context, continuity and accountability to every interaction and both are necessary for safe, agentic commerce. In other words, KYC answers who you are. KYA answers who (or what) is acting for you, and whether it should be trusted. ConceptKnow Your Customer (KYC)Know Your Agent (KYA)FocusHuman identityAI agent identity PurposePrevent fraud, ensure compliance Enable safe automation and delegationEntity verifiedIndividual or business Agent + human + authorizationScopeStatic identity checks Dynamic identity + behaviors + permissions How KYA works in practice While the concept is still evolving, most KYA approaches share a common goal: creating a verifiable chain of trust between humans and AI agents. This typically involves: Establishing a secure and auditable link between a verified person and their agent Confirming that the agent is authorized to act within defined permissions Continuously evaluating behavior and risk over time Ensuring a verified connection between humans and AI agents confirms that agent-initiated transactions are grounded in real identity.  Why KYA matters for businesses For organizations, KYA is more than a security concept; it’s an enabler of growth. As agentic commerce expands, businesses will increasingly interact with AI agents as a new customer base. Those who can confidently verify and trust these interactions will be able to: Accept agent-initiated transactions with lower risk Reduce friction for legitimate users Unlock new, automated customer experiences Those that can’t may find themselves required to block or challenge these interactions, limiting adoption and missing out on emerging revenue streams. The reality is simple: agentic commerce will not scale without trust.  Bringing it to life with Experian® Agent TrustTM This is exactly the challenge we’re addressing with our first-of-its-kind framework, Experian® Agent TrustTM. Experian Agent Trust is designed to create a secure, verifiable link between consumers and the AI agents acting on their behalf, bringing identity, intent and accountability into AI-driven transactions.  At the center of this approach is Human-to-Agent Binding, which connects a verified individual, their device and their AI agent. This binding is recorded in Experian’s Agent Trust Registry and creates a persistent trust signal that allows businesses to understand exactly who is behind every agent-driven action.  By grounding agent activity in verified identity, we are extending our expertise in fraud prevention and identity verification into the next era of commerce, one where AI agents don’t just assist, but act. The future of trust starts with knowing your agent As AI agents grow more capable, they won’t just support transactions, they’ll initiate them, negotiate them and complete them autonomously. This evolution demands a new foundation for trust, one that extends beyond verifying customers to understanding and validating the agents acting on their behalf. As agentic commerce accelerates, organizations that embrace Know Your Agent (KYA) will be better equipped to innovate with confidence, scale responsibly and strengthen trust at every interaction. Learn more about Experian Agent Trust

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

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 model drift” 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 SpotModern decisioning environments are interconnected ecosystems. Forecasting models may influence reserve calculations. Marketing models may be repurposed across product lines. Data transformations may feed 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

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

Credit marketing is entering a new era of precision. Data privacy, personalization and digital-first expectations are rewriting the playbook for financial services marketers. The winners in 2026 won’t just optimize; they’ll orchestrate, using connected intelligence — the linking of data, AI models and insights across platforms — to find, know and grow the right customers. Our latest checklist breaks down what it takes to compete in this new environment, including how to: Master the new prospecting formula Use data to drive personalization at scale Create cohesive, compliant messaging across channels Whether your focus is to expand your portfolio, deepen existing relationships or improve marketing efficiency, this checklist can help you drive stronger, smarter growth all year round. And if you're interested in diving deeper, register for our upcoming webinar on January 15, 2026 to hear directly from Experian experts. Access checklist Register for webinar

In today’s fast-evolving digital landscape, fraud prevention is no longer a reactive function, it’s a strategic imperative. As financial institutions, fintechs and government agencies face increasingly sophisticated threats, the need for scalable, transparent and AI-powered solutions has never been greater. Experian stands at the forefront of this transformation, delivering proven technology, unmatched data intelligence and regulatory-ready innovation that empowers organizations to stay ahead of fraud. One platform. Every fraud challenge. Experian’s fraud prevention ecosystem delivers scale, speed and sophistication. Unlike fragmented solutions that require patchwork integrations, Experian offers a unified platform that spans the entire fraud lifecycle from identity verification to transaction monitoring and case management.  With the exciting acquisition of NeuroID, Experian is delivering more value than ever before with our shared commitment to staying ahead of emerging fraud threats.   Embedding NeuroID’s behavioral expertise into Experian’s data systems and platforms is transformative. Together, we’re redefining what fraud prevention can look like in a real-time, AI-driven world. – Kathleen Peters, Chief Innovation Officer, Experian With tools like NeuroID, FraudNet and Precise ID, Experian delivers real-time decisioning and orchestration across diverse use cases. These technologies are not just buzzwords, they’re battle-tested engines driving measurable impact across millions of daily decisions. Data dominance that drives accuracy Experian’s proprietary datasets and global consortia provide unparalleled access to fraud intelligence. This data advantage enables clients to detect anomalies faster, reduce false positives and optimize fraud strategies with precision.  Experian supports over five billion fraud events annually across the largest banks, fintechs and government agencies. That’s 10x more fraud and identity use cases than most competitors can manage across industries and institutions of all sizes. AI innovation with guardrails While many vendors are just beginning to explore AI, Experian has spent the last two decades embedding it into its core products and services. The launch of the Experian Assistant for Model Risk Management exemplifies this commitment. Integrated into the Ascend Platform and powered by ValidMind technology, this AI assistant streamlines model governance, enhances auditability, and accelerates deployment, all while remaining compliant with evolving regulations. Experian’s AI is not a black box. It’s explainable, auditable and developed with governance in mind. This transparency gives clients the confidence to innovate without compromising compliance.  Compliance is built in, not bolted on Experian’s solutions are designed with compliance at the core. From FCRA and GLBA to KYC and CIP, Experian has a long-standing track record of aligning with regulatory frameworks. The company’s ability to demystify machine learning and make it transparent and explainable sets it apart in an industry where trust is paramount. As AI adoption accelerates, Experian’s governance models ensure that innovation doesn’t outpace accountability. Clients benefit from automated documentation, synthetic data generation and model transparency which are all essential for navigating today’s complex regulatory landscape. Empowering clients to own their outcomes Experian doesn’t just deliver tools, it empowers users. With self-service model building, clients can customize fraud strategies, optimize performance, and respond to threats in real time. This flexibility ensures that organizations aren’t just reacting to fraud, they’re proactively shaping their defenses.  Experian’s fraud prevention solutions are designed to be intuitive, scalable, and user-centric, enabling teams to make smarter decisions faster. A global brand you can trust Trust is earned, not claimed. Experian’s decades-long commitment to data stewardship, innovation and client success has made it a globally recognized authority in fraud prevention. With thousands of enterprise clients and strategic partnerships, Experian delivers unmatched reliability and scale. From supporting the largest financial institutions to enabling fintech startups, Experian’s infrastructure is built to manage complexity with confidence. Thought leadership that moves the industry  Experian continues to lead the conversation on fraud prevention and identity verification. As a sponsor of the 2025 Federal Identity Forum & Expo, Experian showcased its latest innovations in behavioral analytics and fraud detection, helping government agencies stay ahead of evolving threats.   The company’s U.S. Identity & Fraud Report, now in its tenth year, provides actionable insights into shifting fraud patterns and consumer behavior reinforcing Experian’s role as a trusted thought leader. In a market flooded with noise, Experian delivers clarity. Its unified fraud prevention platform, backed by decades of AI innovation and regulatory expertise, empowers organizations to protect their customers, optimize operations, and lead with confidence. Experian isn’t just keeping up with the future of fraud prevention, it’s defining it. Learn more

Three winners were announced at Experian’s inaugural Vision Awards ceremony held on Tuesday, October 7 in front of more than 800 attendees at Experian’s Vision Conference held in Miami, Fla. Figure, PREMIER Bankcard and Members First Credit Union were recognized for their work in artificial intelligence, innovation and financial empowerment. The four-day gathering provided a dynamic forum for exploring the latest innovations shaping the future of data-driven decisioning. “Our Vision Awards celebrate the unique impact financial industry leaders can have when data, technology and purpose align,” said Jeff Softley, CEO, Experian North America. “We are proud to recognize these three organizations with whom we collaborate to drive opportunities and help create change for society as a whole.” The Vision Awards recognize the achievements of organizations that accelerate action. These forward-thinking institutions leverage artificial intelligence, innovation and financial empowerment to drive opportunities and create actionable change for consumers, businesses and society. Recognizing Leaders in AI, Innovation, and Financial Empowerment A panel of interdisciplinary judges reviewed nominations from across industries across the regions, evaluating submissions based on rigor, originality, and impact. The 2025 winners reflect how organizations are leveraging data and technology to advance innovation and inclusion. Excellence in AI: Figure Figure’s submission showcased how it has redefined consumer lending outreach through an AI-driven targeting engine powered by more than 90 machine learning models and 5,000+ behavioral and financial features. By combining Experian’s prescreen data with proprietary insights, Figure delivers highly precise, cost-efficient firm offers of credit — helping it become one of the top three home equity line of credit lenders in the U.S. “This win reflects more than just a successful application of AI. It represents the broader innovative culture deeply embedded in our company’s DNA,” said Ruben Padron, Chief Data Officer at Figure. “Our work with Experian has been instrumental in helping us assess creditworthiness and predict borrower intent with greater precision.” Excellence in Innovation: PREMIER Bankcard PREMIER Bankcard continues to demonstrate how financial inclusion and innovation go hand in hand. From modernizing its technology to reimagining its product suite, PREMIER has made bold strides to serve the underserved and democratize access to credit. “This award affirms our belief that financial inclusion and innovation must go hand in hand,” said Chris Thornton, Senior Vice President of Credit at PREMIER Bankcard. “We’re committed to reaching those who need it most, and Experian has proven to be an exceptional partner in that mission.” With more than 30 million customers served, PREMIER has become a leader in first-time and second-chance credit, while also giving back more than $4 billion to charitable causes through its partnership with First PREMIER Bank and founder Denny Sanford. “We’re here to change lives,” Thornton added. “That’s how we measure success — and that’s ultimately what we’re investing in.” Excellence in Financial Empowerment: Members First Credit Union Members First Credit Union was honored for its commitment to inclusive lending and community development across Michigan. In 2024 alone, the credit union’s programs helped thousands of members access fair and affordable credit, supported 166 community organizations, and contributed nearly $230,000 in donations — backed by 2,000 volunteer hours from its employees. “Our impact demonstrates how mission-driven financial institutions can meaningfully expand access, strengthen communities, and foster long-term financial health,” said Carrie Iafrate, CEO/President at Members First Credit Union. “We’re honored to receive this recognition and inspired to continue helping individuals thrive financially.” Honoring the Judges Behind the Vision The 2025 Vision Awards were evaluated by a distinguished panel of judges representing both Experian and external associations and partners in the financial inclusion community, including: Lisa Cantu-Parks, Vice President of Resource Development, Unidos Jean Carlos Rosario Mercado, Juntos Avanzamos Program Officer, Inclusiv Ian P. Moloney, Senior Vice President, Head of Policy and Regulatory Affairs, American Fintech Council Marc Morial, President and CEO, National Urban League Kevin O’Connor, Senior Vice President, Membership and Sponsorship, Consumer Bankers Association Their expertise ensured that the winners reflect the industry’s highest standards of innovation, integrity, and impact. Ian P. Moloney, Senior Vice President, Head of Policy and Regulatory Affairs, American Fintech Council, and Rhonda Spears Bell, Senior Vice President and Chief Marketing Officer, National Urban League, were at the recognition session at Vision and shared about their organizations and experience serving as a judge. Video messages were also shared from Jean Carlos Rosario Mercado of Inclusiv and Kevin O’Connor of Consumer Bankers Association, who were unable to attend the live event. “I greatly appreciated the opportunity to participate as a judge in the Experian Vision Awards because it provided me a chance to look beyond my usual day-to-day, and understand the myriad of innovations and projects going on to help consumers and the industry,” Moloney said. “The award winners tonight showcase the best of our industry, and I appreciate the opportunity to take part in highlighting their success.” “I’m inspired by the outstanding organizations we’re celebrating tonight - each making a lasting impact in our country and globally,” Spears Bell said. “I want to take a moment to recognize Experian - not only as a valued corporate partner, but as a true ally in our mission to advance financial literacy, stability, and generational wealth.” Looking Ahead: Vision Awards 2026 Experian will continue to champion progress in financial services and across all industries, and the Vision Awards offers one of the avenues through which the industry can recognize organizations driving change through responsible innovation. Submissions for the 2026 Vision Awards open on June 1, 2026. To learn more about this year’s winners and how to apply for next year’s program, visit the Vision Awards page.

Day 1 of Vision 2025 is in the books – and what a start. From bold keynotes to breakout sessions and networking under the Miami sun, the energy and inspiration were undeniable.  A wave of change: Jeff Softley opens Vision 2025  The day kicked off with a powerful keynote from Jeff Softley, Experian North America CEO, who issued a call to action for the industry: to not just adapt to change, but to lead it.  “It isn’t a ripple – it’s a tidal wave of technology,” Jeff said. “Together we ride this wave with confidence.”  His keynote set the tone for a day centered on innovation and the future of financial services – where technology, insight and trust converge to create lasting impact. Jeff continues this conversation in the latest Experian Exchange episode, where he explores three forces shaping the industry: the rise of AI, the demand for personalized digital experiences and the mission to expand credit access for all.  Turning vision into action: Alex Lintner on agentic AI  Building on Jeff’s message, Alex Lintner, CEO of Experian Software and Technology, took the stage to show how Experian is turning innovation into measurable results. His keynote explored how agentic and advanced AI capabilities are redefining financial services ROI and powering the next generation of the Ascend Platform™.  For a deeper look into how Experian is reshaping the economics of credit and fraud decisioning, read the latest American Banker feature.  Unfiltered insights from “Mr. Wonderful”  The day’s highlight came from Kevin O’Leary, investor, entrepreneur and the always-candid “Mr. Wonderful.” With his trademark wit and honesty, Kevin shared sharp insights on thriving in a disruptive economy, offering candid advice on leadership, risk and opportunity. He even gave attendees a peek behind the Shark Tank curtain, revealing a few surprises and the mindset that drives his bold business decisions.  Breakouts that inspired and informed  The conference floor buzzed with energy as attendees joined breakout sessions on fraud defense, AI-driven personalization, regulatory trends and consumer insights. Sessions highlighted how Experian’s unified value proposition is fueling double-digit growth, how to future-proof credit risk strategies and how data and innovation are redefining customer engagement across the lifecycle.   Hands-on innovation and connection  The Innovation Showcase gave attendees an up-close look at Experian’s latest tools and technologies in action. Meanwhile, friendly competition kept the excitement high through the Vision mobile app leaderboard – with every check-in and connection earning points toward the top spot.  Networking beyond the conference hall walls  As the sun set, Vision 2025 shifted into high gear with unforgettable networking events across Miami – from golf at the Miller Course to art walks, brewery tours and a scenic cruise through Biscayne Bay.   An evening to remember  The day closed with the first-ever Vision Awards Dinner, celebrating standout leaders who are shaping the future of financial services.   Up Next: Day 2  The momentum continues tomorrow as more keynote speakers take the stage. Stay tuned for more insights, innovation, and inspiration from Vision 2025. 

Lending fraud – what is it? Lending fraud is a deceptive practice in which individuals or entities intentionally provide false or misleading information during the loan application process to secure credit or financial gain. This can include using fake identities, inflating income, forging documentation, or applying for loans without the intention of repayment.   The consequences are significant: lenders suffer financial losses, consumers experience identity theft or damaged credit scores, and the economic system bears increased risk and regulatory scrutiny. Loan fraud is a growing concern across consumer, commercial, and mortgage lending sectors, affecting institutions of all sizes. How do I safeguard my organization from loan fraud?    Preventing lending fraud is a complex, ongoing challenge that requires a multi-layered and holistic approach. As fraud tactics become more sophisticated, especially with the rise of generative AI and digital lending channels, financial institutions must continually evolve their defenses.  Strong identity verification is the first line of defense. Lenders should implement advanced authentication tools beyond basic KYC (Know Your Customer) checks. This includes biometric verification, document verification, and device intelligence —technologies that assess the authenticity of the user and the device used during the application process. These tools can help detect synthetic identities — false identities created using a blend of real and fabricated information — increasingly used in loan fraud schemes.  Another crucial strategy is real-time data analytics and behavioral monitoring. Lenders can quickly identify anomalies that may indicate fraudulent activity by analyzing applicant behavior, credit history, device usage patterns, and geolocation data in real time. For example, if an applicant submits multiple loan applications from different IP addresses in a short time frame, that could raise a red flag for potential lending fraud.  Employee training and awareness are also essential. Frontline staff must be equipped to identify warning signs, such as inconsistencies in application documents or rushed, high-pressure loan requests. Regular fraud prevention training helps employees stay alert and aligned with the organization’s risk management protocols.  57% of financial institutions reported direct fraud losses exceeding $500,000 in the past year, with 25% exceeding $1 million.1 Consumers reported losing more than $12.5 billion to fraud in 2024, which represents a 25% increase over the prior year.2 In addition, robust internal controls and auditing mechanisms are critical in prevention. Organizations should regularly audit loan origination processes and investigate unusual approval patterns to detect insider fraud or systemic vulnerabilities.  Finally, consumer education is a vital, often overlooked, aspect of combating loan fraud. Lenders should provide resources to help customers understand the risks of identity theft, encourage them to monitor their credit reports regularly, and empower them to report any suspicious activity. A well-informed customer base can be a valuable early warning system for fraud.  With digital lending becoming the norm, preventing lending fraud means staying ahead of increasingly tech-savvy fraudsters. Leveraging data, technology, and education together builds a stronger, more resilient fraud defense framework.  Lending fraud + Experian – How we can help  With access to the industry’s most advanced fraud detection and identity verification tools, partnering with us gives you a potent edge in combating lending fraud. As a global leader in data, analytics, and technology, our comprehensive and accurate sets of consumer information enable you to spot risks that might be invisible through conventional means. Our approach combines rich data insights with powerful machine learning algorithms, delivering fraud prevention tools that are intelligent, scalable, and highly adaptive.  Our fraud detection technologies are designed to protect every stage of the lending lifecycle. From real-time identity verification and multi-factor authentication solutions to behavioral biometrics and device intelligence, so you can detect synthetic identities, manipulated applications, and other forms of loan fraud before they lead to financial loss.  In an era where trust is currency, partnering with us doesn’t just help protect against lending fraud — it enhances your reputation as a secure, responsible lender. You gain the confidence of your customers by providing safe, streamlined lending experiences while meeting compliance requirements and reducing operational risk. With us, you’re not just reacting to fraud—you’re anticipating it, preventing it, and confidently growing your business.  Learn more 1State of Fraud Benchmark Report. Alloy. (2024). 2New FTC Data Show a Big Jump in Reported Losses to Fraud to $12.5 Billion in 2024. Federal Trade Commission. (2025, March 10). 

Now in its tenth year, Experian’s U.S. Identity and Fraud Report continues to uncover the shifting tides of fraud threats and how consumers and businesses are adapting. Our latest edition sheds light on a decade of change and unveils what remains consistent: trust is still the cornerstone of digital interactions. This year’s report draws on insights from over 2,000 U.S. consumers and 200 businesses to explore how identity, fraud and trust are evolving in a world increasingly shaped by generative artificial intelligence (GenAI) and other emerging technologies. Highlights: Over a third of companies are using AI, including generative AI, to combat fraud. 72% of business leaders anticipate AI-generated fraud and deepfakes as major challenges by 2026. Nearly 60% of companies report rising fraud losses, with identity theft and payment fraud as top concerns. Digital anxiety persists with 57% of consumers worried about doing things online. Ready to go deeper? Explore the full findings and discover how your organization can lead with confidence in an evolving fraud landscape. Download report Watch on-demand webinar Read press release  

In today’s evolving economic climate, lenders face a growing challenge: how to accurately assess creditworthiness — especially for consumers with limited credit histories. That’s where cash flow insights come into play. Our latest infographic illustrates how cashflow data helps lenders achieve a more comprehensive understanding of borrowers' financial health. What you'll learn: Why cashflow data is essential for modern, inclusive lending The key financial behaviors that cash flow insights can uncover How these insights help lenders expand market reach and make more precise decisions Read the infographic to learn more. View infographic

In early 2025, European authorities shut down a cybercriminal operation called JokerOTP, responsible for over 28,000 phishing attacks across 13 countries. According to Forbes, the group used one-time password (OTP) bots to bypass two-factor authentication (2FA), netting an estimated $10 million in fraudulent transactions. It's just one example of how fraudsters are exploiting digital security gaps with AI and automation. What is an OTP bot? An OTP bot is an automated tool designed to trick users into revealing their one-time password, a temporary code used in multifactor authentication (MFA). These bots are often paired with stolen credentials, phishing sites or social engineering to bypass security steps and gain unauthorized access. Here’s how a typical OTP bot attack works: A fraudster logs in using stolen credentials. The user receives an OTP from their provider. Simultaneously, the OTP bot contacts the user via SMS, call or email, pretending to be the institution and asking for the OTP. If the user shares the OTP, the attacker gains control of the account. The real risk: account takeover OTP bots are often just one part of a larger account takeover strategy. Once a bot bypasses MFA, attackers can: Lock users out of their accounts Change contact details Drain funds or open fraudulent lines of credit Stopping account takeover means detecting and disrupting the attack before access is gained. That’s where strong account takeover/login defense becomes critical, monitoring suspicious login behaviors and recognizing high-risk signals early. How accessible are OTP bots? Mentions of OTP bots on dark web forums jumped 31% in 2024. Bot services offering OTP bypass tools were being sold for just $10 to $50 per attack. One user on a Telegram-based OTP bot platform reported earning $50,000 in a month.   The barrier to entry for fraudsters is low, and these figures highlight just how easy and profitable it is to launch OTP bot attacks at scale. The evolution of fraud bots OTP bots are one part of the rising wave of fraud bots. According to our report, The Fraud Attack Strategy Guide, bots accounted for 30% of fraud attempts at the beginning of 2024. By the end of the year, that number had risen to 80% — a nearly threefold increase in just 12 months. Today’s fraud bots are more dynamic and adaptive than before. They go beyond simple scripts, mimicking human behavior, shifting tactics in real time and launching large-scale bot attacks across platforms. Some bypass OTPs entirely or refine their tactics with each failed attempt. With generative AI in the mix, bot-based fraud is getting faster, cheaper and harder to detect. Effective fraud defense now depends on detecting intent, analyzing behavior in real time and stopping threats earlier in the process. Read this blog: Learn more about identifying and stopping bot attacks. A cross-industry problem OTP bots can target any organization that leverages 2FA, but the impact varies by sector. Financial services, fintech and buy now, pay later (BNPL) providers are top targets for OTP bot attacks due to high-value accounts, digital onboarding and reliance on 2FA. In one case outlined in The Fraud Strategy Attack Guide, a BNPL provider saw 25,000+ bot attempts in 90 days, with over 3,000 bots completing applications, bypassing OTP or using synthetic identities. Retail and e-commerce platforms face attacks designed to take over customer accounts and make unauthorized purchases using stored payment methods, gift cards or promo credits. OTP bots can help fraudsters trigger and intercept verification codes tied to checkout or login flows. Healthcare and education organizations can be targeted for their sensitive data and widespread use of digital portals. OTP bots can help attackers access patient records, student or staff accounts, or bypass verification during intake and application flows, leading to phishing, insurance fraud or data theft. Government and public sector entities are increasingly vulnerable as fraudsters exploit digital services meant for public benefits. OTP bots may be used to sign up individuals for disbursements or aid programs without their knowledge, enabling fraudsters to redirect payments or commit identity theft. This abuse not only harms victims but also undermines trust in the public system. Across sectors, the message is clear: the bots are getting in too far before being detected. Organizations across all industries need the ability to recognize bot risk at the very first touchpoint; the earlier the better. The limitations of OTP defense OTP is a strong second factor, but it’s not foolproof. If a bot reaches the OTP stage, it's highly likely that they've already: Stolen or purchased valid credentials Found a way to trigger the OTP Put a social engineering play in motion Fighting bots earlier in the funnel The most effective fraud prevention doesn’t just react to bots at the OTP step; it stops them before they trigger OTPs in the first place. But to do that, you need to understand how modern bots operate and how our bot detection solutions, powered by NeuroID, fight back. The rise of GenAI-powered bots Bot creation has become dramatically easier. Thanks to generative AI and widely available bot frameworks, fraudsters no longer need deep technical expertise to launch sophisticated attacks. Today’s Gen4 bots can simulate human-like interactions such as clicks, keystrokes, and mouse movements with just enough finesse to fool traditional bot detection tools. These bots are designed to bypass security controls, trigger OTPs, complete onboarding flows, and even submit fraudulent applications. They are built to blend in. Detecting bots across two key dimensions Our fraud detection solutions are purpose-built to uncover these threats by analyzing risk signals across two critical dimensions. 1. Behavioral patternsEven the most advanced bots struggle to perfectly mimic human behavior. Our tools analyze thousands of micro-signals to detect deviations, including: Mouse movement smoothness and randomness Typing cadence, variability and natural pauses Field and page transition timing Cursor trajectory and movement velocity Inconsistent or overly “perfect” interaction patterns By identifying unnatural rhythms or scripted inputs, we can distinguish real users from automation before the OTP step. 2. Device and network intelligenceIn parallel, our technology examines device and network indicators that often reveal fraud at scale: Detection of known bot frameworks and automation tools Device fingerprinting to flag repeat offenders Link analysis connecting devices across multiple sessions or identities IP risk, geolocation anomalies and device emulation signals This layered approach helps identify fraud rings and coordinated bot attacks, even when attackers attempt to mask their activity. A smarter way to stop bots We offer both a highly responsive, real-time API for instant bot detection and a robust dashboard for investigative analytics. This combination allows fraud teams to stop bots earlier in the funnel — before they trigger OTPs, fill out forms, or submit fake credentials — and to analyze emerging trends across traffic patterns. Our behavioral analytics, combined with device intelligence and adaptive risk modeling, empowers organizations to act on intent rather than just outcomes. Good users move forward without friction. Bad actors are stopped at the source. Ready to stop bots in their tracks? Explore Experian’s fraud prevention services. Learn more *This article includes content created by an AI language model and is intended to provide general information.

Powered by GenAI and increasingly accessible fraud tools, fraud threats are evolving faster than ever. Traditional fraud detection solutions alone are struggling to keep up with evolving fraud rings, fraud bots, and attack strategies, pushing businesses to explore smarter, more adaptive defenses. That’s why many organizations are turning to User and Entity Behavior Analytics (UEBA) as protection against growing threats, especially internal ones. But what exactly is UEBA, and how does it differ from other solutions, like behavioral analytics?

Bot fraud has long been a major concern for digital businesses, but evolving attacks at all stages in the customer lifecycle have overshadowed an ever-present issue: click fraud. Click fraud is a cross-departmental challenge for businesses, and stopping it requires a level of insight and understanding that many businesses don’t yet have. It’s left many fraud professionals asking: What is click fraud? Why is it so dangerous? How can it be prevented? What is click fraud? A form of bot fraud, click fraud occurs when bots drive fraudulent clicks to websites, digital ads, and emails. Click fraud typically exploits application flows or digital advertising; traffic from click bots appears to be genuine but is actually fraudulent, incurring excessive costs through API calls or ad clicks. These fraudulent clicks won’t result in any sales but will reveal sensitive information, inflate costs, and clutter data. What is the purpose of click fraud? It depends on the target. We've seen click bots begin (but not complete) insurance quotes or loan applications, gathering information on competitors’ rates. In other cases, fraudsters use click fraud to drive artificial clicks to ads on their sites, resulting in increased revenue from PPC/CPC advertising. The reasons behind click fraud vary widely, but, regardless of its intent, the impacts of it affect businesses deeply. The dangers of click fraud On the surface, click fraud may seem less harmful than other types of fraud. Unlike application fraud and account takeover fraud, consumers’ data isn’t being stolen, and fraud losses are relatively minuscule. But click fraud can still be detrimental to businesses' bottom lines: every API call incurred by a click bot is an additional expense, and swarms of click bots distort data that’s invaluable to fraud attack detection and customer acquisition. The impact of click fraud extends beyond that, though. Not only can click bots gather sensitive data like insurance quotes, but click fraud can also be a gateway to more insidious fraud schemes. Fraud rings are constantly looking for vulnerabilities in businesses’ systems, often using bots to probe for back-door entrances to applications and ways to bypass fraud checks. For example: if an ad directs to an unlisted landing page that provides an alternate entry to a business’s ecosystem, fraudsters can identify this through click fraud and use bots to find vulnerabilities in the alternate application process. In doing so, they lay the groundwork for larger attacks with more tangible losses. Keys to click fraud prevention Without the right tools in place, modern bots can appear indistinguishable from humans — many businesses struggle to identify increasingly sophisticated bots on their websites as a result. Allowing click fraud to remain undetected can make it extremely difficult to know when a more serious fraud attack is at your doorstep. Preventing click fraud requires real-time visibility into your site’s traffic, including accurate bot detection and analysis of bot behavior. It’s one of many uses for behavioral analytics in fraud detection: behavioral analytics identifies advanced bots pre-submit, empowering businesses to better differentiate click fraud from genuine traffic and other fraud types. With behavioral analytics, bot attacks can be detected and stopped before unnecessary costs are incurred and sensitive information is revealed. Learn more about our behavioral analytics for fraud detection.

Consumers are experiencing the highest loan rejection rates in a decade, driven by strict lending standards.1 While crucial for mitigating risk, these measures can also limit growth opportunities for financial institutions. Our latest one pager explores how cash flow data, obtained from consumer-permissioned transaction data, empowers lenders with unique insights into consumers’ financial health, enabling them to expand their portfolios while managing risk effectively. Read the full one pager to learn how cashflow data can help you make smarter, more confident lending decisions. Access one pager 12024 Q4 Lending Conditions Chartbook, Experian.