All posts by Zohreen Ismail

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

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

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

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

Learn how alternative data enhances credit decisioning by giving lenders deeper visibility into consumer financial behavior and improving access to credit for more consumers.