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

Discover why only 1 in 3 mortgage shoppers close and how lenders can improve conversion using borrower readiness signals, alternative data and AI-driven insights.

Learn how Matrix continues to deliver a secure, trusted rental experience as fraud tactics evolve. Read more!

Discover how borrower payment behavior and expanded credit data improve mortgage prepayment, delinquency risk, and portfolio value predictions.

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

Learn how Members First Credit Union uses innovation and data-driven insights to better serve members and expand financial opportunity.

A visibility gap lenders can't afford to ignore Alternative data is often associated with thin-file or credit invisible consumers. But its value extends far beyond those segments. Experian's Clarity Services database includes approximately one in five credit-active consumers, including one in four consumers with prime-and-above credit profiles. That means lenders may be missing important signals, not only for emerging borrowers, but also for applicants who appear well qualified using traditional bureau data alone. Consider two consumers with the same credit score. Based on traditional credit data, they may appear equally creditworthy. But when Clarity data is added, one consumer may demonstrate stable repayment behavior while another shows recent defaults on alternative finance products. The credit score hasn't changed, but the decisioning context has. That's where alternative data creates value: helping lenders distinguish between consumers who look similar on paper but represent very different levels of risk and opportunity. In this Ask the Expert session, Experian’s Julius Heim, Vice President of Analytics Product Build, Innovation and Scores, and Natasha Madan, Senior Director, Analytics Consulting, explain how different alternative data assets solve different business challenges and why the greatest return comes from using them together throughout the credit lifecycle. What that visibility gap is really costing lenders Better visibility matters because every lending decision carries consequences. Without alternative data, lenders may approve applicants whose repayment behavior suggests elevated risk but isn't reflected in a traditional credit file. Without cash flow insights, they may decline consumers who appear thin file on bureau data despite demonstrating strong income and responsible financial management. The result is a two-sided cost: avoidable bad debt on one side and missed growth opportunities on the other. But ROI extends beyond approvals alone. It also appears through stronger marketing strategies, improved conversion, reduced friction and more precise risk segmentation throughout the lending lifecycle. "ROI can mean many things ... marketing to the right people, achieving better approval rates, reducing risk, getting less friction and overall profitability."Julius Heim, Vice President of Analytics Product Build, Innovation and Scores Where alternative data creates ROI Improve approval strategies Use additional consumer signals to recover creditworthy applicants while avoiding unnecessary declines. Reduce portfolio risk Identify elevated repayment risk earlier through enhanced visibility beyond traditional bureau data. Improve portfolio performance Increase conversion, reduce friction and strengthen profitability across the credit lifecycle. Different data. Different jobs. Not all alternative data solves the same problem. Clarity Services can help lenders strengthen decisions early in the customer journey. It provides additional visibility during prospecting and acquisition, helping identify potential risk before an application moves through the underwriting process. Cash flow insights can provide value in a different way. When traditional credit information offers part of the picture, consumer-permissioned cash flow data can provide greater insight into income, spending patterns and financial capacity. That makes it especially valuable as a second look during underwriting. Together, these complementary data assets help lenders improve decisioning throughout the credit lifecycle. They can support acquisition, underwriting, account management and collections while building on the trusted foundation of traditional bureau data. Research also continues to demonstrate measurable lift when cash flow insights are combined with traditional credit information. "I recently did a study with a client where we actually saw a 20% lift in KS [Kolmogorov-Smirnov] above and beyond credit bureau data. Again, the bureau data itself was very predictive. But even from the cash flow data, we still got a 20% lift, which is an amazing stat." Julius Heim, Vice President of Analytics Product Build, Innovation and Scores The greatest value comes from using these data sources together for a more holistic consumer view. Start with proof, then build Adopting alternative data doesn't have to begin with a large transformation. A practical first step is a data study. By comparing current decision strategies with enhanced data, lenders can identify where additional visibility creates measurable lift within their own portfolios. This approach allows institutions to validate results before making broader operational changes. Every lender has different workflows, technology environments and business priorities. A flexible implementation strategy helps organizations incorporate new data in ways that support existing processes rather than disrupting them. Three ways to get started Run a data study Benchmark current decision strategies and quantify potential lift. Start simple Begin with targeted data attributes or proven scores before expanding to more advanced use cases. Build with confidence Scale implementation based on measured business outcomes and organizational priorities. This approach allows lenders to validate results, build confidence and expand their strategy over time. Explore alternative data with a trusted partner Every lending decision benefits from better consumer insight. Experian helps lenders combine trusted credit data with alternative data, cash flow insights and advanced analytics to strengthen decisioning, improve portfolio performance and uncover new opportunities for growth. Whether you're evaluating alternative data for the first time or expanding an existing strategy, Experian can help you identify where additional consumer insight can create measurable business value. Learn more Contact us About our experts Julius Heim Vice President of Analytics Product Build, Innovation and Scores, Experian Julius Heim works at the intersection of financial services, analytics and innovation. He focuses on leveraging data to drive smarter decision-making and support more inclusive financial ecosystems. Julius brings a practical perspective on how organizations can translate insights into real-world impact, with particular interest in emerging trends across fintech, credit, and the use of alternative data, such as cash-flow data, across the credit lifecycle. Previously, he served as Head of Analytics on the lender side and held roles in insurance analytics earlier in his career. Natasha Madan Senior Director, Analytics Consulting, Experian Natasha Madan partners with lenders to drive smarter, data-driven credit and risk decisions. She specializes in leveraging alternative data and advanced analytics to help organizations improve portfolio performance, optimize customer acquisition, and expand responsible access to credit. During her 15 years at Experian, Natasha has held leadership roles spanning data analytics, product analytics and consulting, giving her a broad perspective of how data can be leverage to solve complex business challenges. She has worked with a diverse range of lenders – including banks, credit unions, fintechs and specialty finance companies to develop analytics strategies that optimize customer acquisition, underwriting and portfolio management. Natasha is passionate about helping organizations unlock the full potential of data to improve both business outcomes and consumer financial inclusion.

Learn how HomeFree-USA and Experian partner to expand financial education, strengthen communities and help consumers achieve homeownership.

ValidMind CEO Jonas Jacobi shares insights on AI, innovation and why Experian's partnership is helping shape the future of responsible AI.

Due to new federal student loan regulations, the families of undergrad and graduate students may look to private lenders to fill the gap.

Learn how Caliber Financial uses Experian data to improve lead targeting, underwriting and AI-driven decisioning for better outcomes.

Learn what agentic commerce is, why AI agents are transforming digital commerce and how Agent Trust builds trusted AI interactions.

Consumer visibility is changing Roughly 45 million Americans, or 1 in 5 consumers, are considered credit invisible or unscoreable.[1] They’re working, paying bills and participating in the economy, yet many are not fully visible during the lending process. That creates both a visibility challenge and a growth opportunity for lenders. In this Ask the Expert session, Corliss Hill, Senior Director, Inclusion and Belonging at Experian, joins Dr. Vaneesha Dutra, Endowed Professor of Finance at Morehouse College, to discuss how evolving consumer behaviors are reshaping conversations around financial inclusion and lending decisions. For lenders, visibility matters because confident decisions depend on reliable context and insight. Broader consumer signals can help institutions better understand repayment behaviors, financial stability and consumer capacity. “The benefit of banks using alternative data is that they capture a very significant and new consumer base. That's 20% of the population, 45 million Americans.”Dr. Vaneesha Dutra, Endowed Professor of Finance A more complete understanding of today’s consumers Today’s consumers often manage obligations across a wide range of payment types and financial channels, creating additional signals through cash flow activity, recurring payments and consumer-permissioned financial data. Rent, utilities, subscriptions and mobile phone payments can all provide meaningful insight into how consumers manage their financial lives. What’s changing isn’t the need for risk assessment. It’s the amount of consumer behavior lenders can now evaluate. For example, a consumer experiencing temporary financial disruption may fall behind on certain obligations while continuing to consistently pay rent, utilities and phone bills. Those recurring payment behaviors can provide important context into financial priorities and stability. “These are consumers that pay rent on time every month, pay utilities every month on time and meet many other financial obligations in a timely manner.”Dr. Vaneesha Dutra, Endowed Professor of Finance From visibility to more-informed decisioning Broader consumer insights may help lenders move from limited visibility to more informed decisioning. The conversation shifts when lenders move from asking: “Should we take a risk on this consumer?” to: “Do we have enough information to fully understand this consumer?” That broader context can help institutions: Strengthen risk assessment. Identify financially active consumers with strong repayment behaviors. Support more informed lending strategies. Alternative data isn’t about replacing established credit approaches. It’s about helping lenders build on trusted credit foundations with additional context and insight. Responsible lending starts with better context For lenders, the path forward is practical and actionable. As lenders evaluate broader consumer behaviors, three priorities become increasingly important: Modernize data strategies Incorporate broader consumer signals alongside existing credit data to create a more holistic view of repayment behavior and financial stability. Engage consumers earlier Earlier intervention may help lenders better support consumers before financial challenges become more severe. Create pathways to financial access Smaller lending opportunities can help consumers establish stronger financial profiles and demonstrate positive repayment behaviors over time. The institutions that lead will be the ones that can combine strong risk practices with a broader understanding of consumer behavior. Whitepaper: Bridging the credit divide: income, risk and inclusion in consumer finance Building on the themes discussed in this Ask the Expert session, Dr. Dutra explores how demographic shifts, evolving borrower behaviors and broader consumer visibility are reshaping lending strategies and what they mean for lenders seeking to balance growth, risk management and financial inclusion. Download whitepaper Explore alternative data with Experian Experian can help lenders combine broader consumer insights with trusted credit data to strengthen decisioning, improve risk assessment and support more-informed lending strategies. With solutions spanning identity, cash flow and advanced analytics, lenders can gain a more complete view of consumer behavior and expand access to credit with greater confidence. Learn more Watch episode 1 About our experts Corliss Hill Senior Director, Belonging Business Partner, Experian Corliss Hill is a collaborative leader well-versed in working with executive stakeholders, crossfunctional teams, external partners and community organizations to design and deliver initiatives and programs that create sustainable impact. With over 25 years of extensive experience in multicultural marketing, communications, PR and inclusion and belonging initiatives, she is dedicated to advancing equitable access to financial. Her mission is to drive impactful marketing initiatives that foster meaningful change and address systemic barriers to inclusion and the communities they serve.Hill has been a part of the Experian family since 2021, and resides in Atlanta with her daughter who is a rising 11-year-old entrepreneur. Vaneesha Dutra, Ph.D. Endowed Professor of Finance and Associate Dean, Morehouse College Vaneesha Dutra, Ph.D., serves as Associate Dean in the Division of Business and Economics. With more than 20 years of experience spanning higher education, banking and real estate, Dr. Dutra’s work focuses on the racial and gender wealth gap, financial literacy and financial decision-making. She is an active researcher and consultant whose work has earned numerous grants and fellowships, including serving as the inaugural Tracy A. Pruitt Visiting Research Faculty Fellow at the Wharton School of Business. Dr. Dutra has also been named a Research Faculty Fellow for both the Center for Black Entrepreneurship and the PNC Bank Center for Entrepreneurship. [1] Consumer Financial Protection Bureau, Expanding access to credit.

Ian P. Moloney of the American Fintech Council discusses responsible fintech innovation and Experian’s role in expanding credit access.

Explore how the Experian Verify™ preview report helps mortgage lenders reduce unnecessary verification costs, avoid overbuying records, and make smarter VOIE decisions with greater visibility before they purchase.