Data & Analytics

In today’s fast-moving financial services landscape, fintechs face a dual challenge: scaling profitably while managing increasingly complex risk. From credit underwriting to fraud prevention, every decision carries both opportunity and exposure. That’s why forward-looking fintech leaders are turning to data-driven credit risk management strategies to sharpen decision-making, enhance compliance and unlock growth. Why data-driven risk management matters in fintech Fintechs are navigating an environment shaped by rapid innovation, shifting regulations and evolving consumer expectations. Within this landscape, three challenges come to the forefront: Evolving fraud threats: Fraudsters are advancing quickly, exploiting digital onboarding and consumer data. Siloed functions: Traditionally, credit, fraud and compliance were separate, but as fraud detection becomes a higher priority, forward-looking companies are now integrating these functions, with84% planning to share more data across the industry to help prevent fraud.1 Operational complexity: Fintechs must balance growth with compliance, often with lean teams, tech-debt that demands a strong return on investment (ROI)and aggressive timelines. These challenges make it clear that static, one-dimensional risk measures are no longer sufficient. By leveraging a unified decisioning platform that incorporates behavioral data and advanced analytics, fintechs can gain a more holistic view of consumer financial behavior. This broader perspective not only improves the accuracy of credit assessments but also strengthens defenses against sophisticated fraud threats. Driving efficiency through automation A data-driven risk management strategy is only as effective as its ability to be executed at scale. This is why automation is no longer a nice-to-have, but a competitive necessity in an industry defined by speed, complexity and rising consumer expectations. By embedding automation into credit and fraud risk management processes, fintechs can create systems that are more efficient, resilient and compliant. Key advantages include: Increased underwriting efficiency: Combined with data-driven insights, automated decisioning platforms allow fintechs to evaluate applications quickly and more accurately, resulting in faster and fairer credit decisions. Portfolio growth: Leveraging expanded data and automation allow enables smarter customer segmentation and more precise risk-based pricing, driving broader market reach and greater profitability. Fraud mitigation: Automated identity verification helps fintechs quickly validate customers, reduce friction in the onboarding process and block fraudulent activity before it impacts portfolios. Regulatory readiness: Unified, automated risk processes enable fintechs to adapt quickly to regulatory shifts, fraud trends and market disruptions, building long-term sustainability. Comparing legacy and modern credit risk approaches in fintech Data and automation have become essential for executing risk strategies at scale, highlighting just how far credit risk management has evolved. Below are key differences between traditional and modern approaches to credit risk. FeatureLegacy approachData-driven approachRisk detectionPoint-in-time scoresTrajectory-based modelingFraud preventionManual reviewAutomated, behavioral analyticsComplianceSiloed functionsUnified decisioning platformCustomer experienceSlow, manualFast, fair, automated Why fintechs choose Experian® As fintechs navigate an environment of increasing regulation, fraud sophistication and consumer expectations, the winners will be those who embrace a data-driven, automated and converged approach to credit and fraud risk management. Experian offers fintechs a partner with unmatched data accuracy, robust alternative data capabilities and end-to-end decisioning solutions designed for today’s converged risk landscape, including: Trended 3DTMattributes capture 24 months of key consumer credit activity, enabling fintechs to better manage portfolio risk and determine next best actions. Cashflow Score leverages consumer-permissioned banking transaction data to predict the likelihood of a borrower going 60+ days past due in the next 12 months, providing deeper visibility into financial health and repayment capacity. PowerCurve® is a unified, automated decision engine that incorporates data, strategy design, decision automation and detailed monitoring and reporting to help fintechs streamline credit decisions with speed and consistency. Our behavioral analytics capabilities, powered by NeuroID, provide a seamless, invisible gauge of user risk, allowing fintechs to proactively mitigate fraud while creating a secure, low-friction customer experience. Frequently asked questions What is data-driven risk management in fintech? It’s the application of advanced analytics, behavioral data and automation to help fintechs improve credit risk assessment, fraud prevention and compliance in digital-first environments. How does automation help fintechs manage credit risk? Automation enables fintechs to scale efficiently by streamlining underwriting, minimizing manual errors and ensuring consistent decision-making. What are the benefits of unified decisioning platforms? Unified platforms integrate credit, fraud and compliance decisions into a single workflow, helping fintechs onboard customers faster, respond quickly to fraud threats and maintain compliance without slowing down innovation. Discover how our fintech solutions can help your fintech strengthen credit risk management, reduce fraud and accelerate growth. Learn more 1Experian Vision

Mid-sized banks are large enough to pursue ambitious growth strategies, like expanding loan portfolios or entering new markets, but not so large that they can withstand major credit losses without consequence. So how do lending organizations manage their credit risk strategies to grow without taking on more risk than they can handle?

Credit decisioning has traditionally relied on static data like credit bureau scores, income statements, and past repayment history. As financial behavior becomes more dynamic and consumer expectations shift toward instant decisions, real-time data is emerging as a powerful tool in reshaping how lenders assess risk.

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

Financial institutions are sitting on a goldmine of data: customer transactions, credit histories, digital interactions, and more. But the real value is found when that data is transformed into insights that drive smarter decisions, faster responses, and better outcomes for both the business and consumers.

Risk management specialists, marketing departments, and customer success teams often work from different data sets, leading to inconsistent insights and missed opportunities. A unified data strategy can help break down these silos and unlock the full potential of an organization’s ability to turn raw data into actionable insights.

Experian and Plaid are teaming up to power smarter, faster, and more inclusive lending — fueled by real-time cash flow insights. The financial landscape is becoming more dynamic and digitally connected. Consumers are increasingly turning to digital platforms not only to pay bills and track spending, but to better understand their financial health, monitor their credit standing, and plan confidently for the future. This evolution presents a timely opportunity for innovation in underwriting — one that empowers consumers to take control of their financial futures and enables lenders to make faster, smarter, and more inclusive decisions. What happens when the leading global data and technology company joins forces with the largest open banking network in the world? Experian and Plaid are coming together to solve some of the most pressing challenges lenders face, bringing cash flow insights into credit decisions, seamlessly. Smarter lending: Elevating the credit decision process For lenders seeking a holistic view of borrowers to make faster, more informed decisions, this new collaboration is a game-changer. Experian and Plaid are combining real-time, unmatched cash flow data and analytics to help lenders improve decisioning, pinpoint risk precisely, and drive financial inclusion. This marks a pivotal shift in how credit is assessed, moving us toward faster, and fundamentally smarter lending decisions. This strategic collaboration delivers real-time cash flow insights in a comprehensive solution, built on core principles designed to directly enhance your lending capabilities: Speed and simplicity: Driving efficiency with seamless integration In today’s fast-paced financial landscape, efficiency in underwriting isn’t just an advantage; it’s a necessity. Our combined solution prioritizes speed and simplicity by offering easy integration through APIs. This ensures fast access to meaningful risk insights, streamlining your workflows. Imagine easily leveraging real-time cashflow risk insights directly into your existing processes for faster and smarter lending decisions. This is about delivering modern infrastructure that allows you to move at the speed of today's market, empowering your business to expand with confidence. Broader visibility: Unveiling a holistic consumer view Traditional credit scores are a reliable, crucial tool for measuring a borrower’s creditworthiness. When coupled with real-time cashflow data and risk insights, lenders are empowered with broader visibility, bringing to light a more holistic view of a borrower’s current financial reality and opportunities that may have been missed. You gain a comprehensive consumer financial picture, allowing for more precise identification of both strong financial capacity and potential risks, ultimately helping you target and acquire customers who align with your growth objectives. Smarter decisions: Enhancing models with combined intelligence The power to make truly informed decisions hinges on the quality and depth of your data. Without robust insights, risk models can be limited, impacting precision and speed. With Experian's advanced cash flow analytic capabilities and Plaid's streamlined access to real-time cash flow data via Consumer Report, you can enhance your risk assessment for smarter decisions. This synergy empowers financial institutions to expand credit access and uncover hidden risks, leading to more precise underwriting. It’s about leveraging advanced analytics in real-time to drive improved decision-making and build stronger portfolios. More inclusive lending: Expanding access, responsibly A significant challenge in lending is ensuring access for all creditworthy individuals, including those with limited traditional credit histories who may be overlooked. This represents an untapped market and a vital opportunity for responsible growth. Our solution champions more inclusive lending, enabling you to reach underserved communities and empower consumers who demonstrate strong financial capacity. This not only fosters stronger portfolios but critically helps your business grow by efficiently acquiring customers across a broader spectrum. Proven trust: Lending with confidence In the financial industry, the bedrock of any solution is trust – in the data, security, and partners. Lenders require unwavering confidence in the tools they adopt. This collaboration is built on proven trust, leveraging the reach, reliability, and security of two of the most trusted names in financial services. Experian’s expertise in credit data and consumer protection, combined with Plaid’s modern infrastructure and trusted open banking network, offers unparalleled assurance. You can securely integrate these powerful insights, knowing you are backed by industry leaders committed to best-in-class security and compliance, enabling your business to grow with confidence without compromise. Smarter lending starts now The evolution of underwriting demands a more dynamic, inclusive, and precise approach. With Experian and Plaid, you're not just adapting to change; you're leading it. Empower your organization to approve more borrowers, reduce risk more effectively, and make smarter, faster decisions for sustainable success. Ready to transform your lending strategy? Learn more about how to bring cash flow insights into your credit decisions seamlessly. Learn more

For financial institutions to achieve success, they need to develop high-performing models with easy access to top-tier data sources. It’s also important to focus on data governance, compliance, and risk management throughout the lending lifecycle. Industry leaders implement advanced analytics and AI solutions to improve their lending decisions, and they also incorporate integrated, efficient feature engineering into their business operations. What’s feature engineering? Feature engineering helps organizations turn raw data into comprehensive model development, following this process: Data collection Data cleaning and transformation Feature engineering Model training and evaluation Decision-making Effectively transforming data into valuable insights depends heavily on creating new custom features to enhance model performance, as well as the quality of the data being used. When data is fragmented or managed poorly, it can lead to increased operational costs, missed revenue opportunities, and compliance risks. Our feature engineering solution: Experian Feature Builder Financial institutions require optimized workflows that can accelerate development while supporting governance and ensuring transparency. Experian’s feature engineering tool, Experian Feature Builder, streamlines custom feature development and deployment across the modeling lifecycle. Providing access to 20+ years of proprietary data, Experian Feature Builder enables organizations to: Break data silos by creating unified access across multiple data types Ensure trust and compliance by embedding audit and lineage tracking at each stage Enable strategic agility with faster and more consistent feature experimentation, testing, and deployment Download our latest e-book to find out more about how Experian’s Feature Builder provides centralized feature development to accelerate time-to-market, enhance compliance, and minimize risk. Download the e-book

Generative AI (GenAI) is transforming the financial services industry by boosting operational efficiency, cutting costs, and enhancing customer experience. Today, industry leaders are leveraging GenAI technology to accelerate the modeling lifecycle, streamline workflows, and ensure regulatory compliance. However, financial institutions face several headwinds in their efforts to achieve strong business results. What industry challenges do financial institutions face? To drive profitability while fueling growth, organizations need to reduce costs, manage risks, and identify new revenue streams while complying with regulatory requirements. Growing customer bases are also a top priority for banking leaders in 2025, requiring personalized services and improved customer experiences to attract and retain customers.1 Staying one step ahead of the competition is another hurdle that many organizations need to overcome. A recent study states that 23% of U.S. consumers surveyed have opened a new bank account, and 28% have considered switching to a new bank in the past six months.2 Traditional financial institutions must continuously innovate to stay on pace with smaller, more agile fintech companies. Adopting technologies like GenAI is an effective way to stay relevant and top-of-mind with consumers. Why use GenAI technology in financial services? Financial organizations that use GenAI are achieving success by: Increasing productivity and efficiency Minimizing costs Strengthening customer relationships GenAI has revolutionized productivity, customer service, risk management, and financial data analysis within the financial services industry. Of all the various measurements of AI use, improved productivity was reported to be the leading indicator of successful implementation.3 Online tools like virtual assistants and chatbots provide personalized experiences to consumers and resolve issues in real time, leading to enhanced customer satisfaction. This AI technology reduces the workload on human agents and enables organizations to deliver value more quickly and with less friction. GenAI adoption at Experian Experian® is a leader in GenAI solutions, using advanced technology to manage and improve data. We champion responsible AI use, ensuring proper consumer data privacy, compliance, fraud prevention, and greater financial access and inclusion. Experian Assistant is our latest innovation in GenAI helping financial institutions to accelerate the modeling lifecycle, which enhances efficiency, reduces expenses, and promotes customer growth. Experian Assistant allows businesses to build and deploy models, monitor performance, and go to market more quickly and with less friction, which can translate to more business success. The tool provides instant expert recommendations and insights with comprehensive support, enabling users to make smarter and faster data-driven decisions. This technology offers multiple functions that are crucial for optimizing business efficiency: Natural language interface Deep insights into underlining data tables and metrics Reduced operational and cloud expenses Decreased risk of penalties Read our latest white paper to discover more about how our latest GenAI innovation, Experian Assistant, is empowering organizations to drive business growth and profitability. Read the white paper 1  BAI, 2025. Acquiring new customers and growing quality deposits are the top business challenges in 2025. 2 MX, 2023. What Influences Where Consumers Choose to Bank. 3 Forrester, Q2 AI Pulse Survey, 2024.

What is feature engineering?  Feature engineering helps organizations turn raw data into comprehensive model development. This process depends heavily on creating new custom features to enhance model performance, as well as the quality of the data being used. When data is fragmented or managed poorly, it can lead to increased operational costs, missed revenue opportunities, and compliance risks. The necessity of integrated feature management Feature engineering is essential for financial institutions to identify valuable features that provide significant insights and predictive power in various analytics applications. By integrating feature engineering into the feature lifecycle, organizations can convert raw data into more accurate and value-driving features, better manage features for audit purposes and compliance efforts, and build higher-performing models. At Experian, we have developed a unified feature engineering solution that integrates capabilities across various tools such as the Ascend Analytical Sandbox™ and Ascend Ops™. This comprehensive approach streamlines the feature engineering process, making it more efficient and effective in supporting the complete feature lifecycle. The challenges in feature engineering 54% of source data used by financial institutions for credit decisioning is not model-building ready.1 Financial institutions need access to high-quality data sources and the ability to modify and combine data to make more profitable data-driven decisions. In addition, organizations need the necessary tools to solve the myriad of challenges involved with feature engineering. These challenges include: Costs: Sourcing and centralizing data can be expensive, and managing and updating data definitions for engineering and analytics is costly. Collaboration: Managing a centralized feature library is difficult and often skipped. As AI and analytics teams become more complex across the enterprise, maintaining and governing feature definitions in a centralized library is a must-have. Inconsistencies: Calculating features can vary. Different calculations in development and production use cases across the lending lifecycle create model risks and compliance issues. Governance risks: Tracking lineage of data definitions is important to avoid elevating risks. Data engineers and scientists need to visualize upstream and downstream impacts as they modify feature definitions. Resources: Teams often have skills gaps and require additional expertise, as they may lack an understanding of automated credit reports amongst resources. Integration: Evaluating and integrating features into the analytics lifecycle is difficult. This can hinder understanding of the value of models and strategies throughout the lending lifecycle and create friction at deployment. Experian Feature Builder: a comprehensive solution Experian Feature Builder is a modern, integrated custom feature solution that combines development, deployment, and management technologies. It accelerates the feature lifecycle through efficient data management and streamlined end-to-end workflows. Users can access the Ascend Analytical Sandbox for custom feature development and seamless connection to Ascend Ops for deployment and ongoing management. This integration also significantly enhances compliance and governance by adding a layer of visibility into feature performance, thereby reducing risks through feature monitoring. Leveraging best-in-class technologies Feature Builder Notebooks enables users to review feature code in Jupyter Notebooks within Ascend Analytical Sandbox, explore data, execute small sample feature calculations, examine feature distributions, edit feature code, and register to the feature library. Feature Builder Studio enables users to review and manage features in the feature library, set up feature calculation jobs, and define feature sets for deployment. Users can also add Ascend Ops to deploy to production with little to no friction. Supporting advanced analytics in consumer credit with integrated feature management Experian Feature Builder provides a centralized feature library, ultimately improving time to market and decisions to extend credit while managing default and fraud risks. Centralized access to data sources used in custom features and intermediation of third-party data sourcing. Advanced lineage tracking for a clear view of the history of upstream and downstream feature dependencies for governance purposes. Streamlined feature registry for built-in version management and tracking with feature correlations and distributions. Key statistical reporting for out-of-the-box data visualizations and monitoring of feature correlations and distributions. Comprehensive feature lifecycle support through integration with Ascend Analytical Sandbox for rapid analytics use case iteration and experimentation as well as production-grade execution and deployment with Ascend Ops. The future of feature engineering Understanding how essential feature engineering is in producing value-driving features, managing and monitoring features for audit and compliance purposes, and more predictive and high-performing models is pivotal to maintaining competitiveness in the financial services industry. Experian Feature Builder is the future of feature engineering. With integration for advanced analytics, model development through deployment, and enhanced feature management capabilities supporting compliance and governance, Experian Feature Builder supports the complete feature lifecycle. To learn more about how Experian Feature Builder can revolutionize your feature engineering, please visit our website and book a demo with your local Experian sales team. Learn more about Feature Builder 1 Experian research 2023

Lending institutions need to use the right business strategies to win more business while avoiding unnecessary risk, especially regarding lending policies. A recent study revealed that 48% of American loan applicants have been denied over the past year, with 14% facing multiple rejections. Additionally, 14% of rejected applicants felt pressured to seek alternative financing like cash advances or payday loans.1 These statistics highlight the need for financial institutions to offer attractive loan options to stay ahead in the industry. Understanding loan loss analysis Loan loss analysis is a powerful tool that helps lenders gain insights into why applicants book loans elsewhere. Despite efforts to target the right consumers at the right time with optimal offers, applicants sometimes choose to book their loans with different institutions. The lack of visibility into where these lost loans are booked can hinder a lender’s ability to improve their offerings and validate existing policies. By leveraging loan loss analysis, lenders can turn valuable data into actionable insights, creating more profitable business opportunities throughout the entire customer lifecycle. Gaining deep consumer insights Loan loss analysis provides visibility into various aspects of competitors’ loan characteristics, such as: Type of financial institution: Identifying whether applicants prefer banks, credit unions or finance companies can help lenders tailor their offerings. Average loan amount: Understanding how much other institutions offer allows lenders to adjust their loan amounts to be more competitive. Interest rates: Comparing interest rates with competitors helps lenders calibrate their rates to attract more business. Loan term length: Knowing the term lengths offered by competitors can inform decisions on loan terms to make them more appealing. Average risk score: Determining the risk scores of loans booked elsewhere helps lenders optimize their policies to maximize earning potential without increasing default risk. Making profitable decisions with business intelligence Experian's loan loss analysis solution, Ascend Intelligence Services™ Foresight, offers comprehensive insights to help lenders: Book more loans Increase profitability Enhance acquisition strategies Improve customer retention Optimize marketing spend By determining where applicants ultimately book their loans, lenders can unlock deep insights into competitors’ loan characteristics, leading to more intelligent business decisions. Read our latest e-book to discover how loan loss analysis can help you gain visibility into competitor offerings, improve your lending policies, book higher-performing loans, and minimize portfolio risk. Read the e-book Visit our website 1 Bankrate, February 2025. Survey: Almost half of loan applicants have been denied over the past 12 months.

Customer retention is crucial for lenders to maximize lifetime value, especially during economic uncertainty. Increasing customer retention rates by just 5% can boost profits by 25% to 95%. However, many lenders struggle with loyalty, as seen in Q2 2024 when mortgage servicers’ retention rates for refinances dropped to 20%, the second lowest in 17 years. Nonbanks and banks also saw significant declines. This is due to increased competition, changing economic conditions, and a lack of personalization. Key strategies for improving customer retention Lenders can improve retention by leveraging data for personalization, maintaining consistent communication, offering loyalty rewards, and utilizing retention triggers. Leverage data for personalization. Use customer data to offer tailored products and refinancing options based on financial behaviors. Using credit attributes, trended data and alternative credit data (alternative financial services data,  cashflow attributes, etc.) can help provide deeper insights of your customers. Maintain consistent communication. Keep customers informed with regular updates about interest rate changes or new loan products. Use a variety of communication channels, including email and in-app messaging, to ensure customers are kept in the loop. Ensure your customer service team is always available and responsive, offering clear answers to any financial concerns. Offer loyalty rewards. Develop programs that reward repeat business and referrals. Offer special rates or discounts for returning customers or for those who refer friends and family to your services. Increase customer lifetime value (LTV) by offering additional services like financial planning or credit score monitoring. Utilize retention triggers. Identify key events for engagement with automated retention triggers. For example, a borrower who has a mortgage with a fixed rate may be less likely to consider refinancing unless prompted. Experian’s Retention TriggersSM can notify lenders when refinancing might be beneficial to their customer, offering them personalized incentives or new product options at the right time. Why Experian’s Retention Triggers? By integrating Experian’s Retention Triggers, lenders can keep borrowers engaged, increase retention, and boost profitability even in tough economic times. Advanced data insights: Gain deeper insights into your customers’ behavior to identify those at risk of leaving and take proactive action. Personalized engagement: Automate personalized communications based on customer behaviors, ensuring timely engagement. Increased revenue: By offering personalized, timely and relevant offers, you can increase the likelihood of retaining your customers and growing your revenue. Make customer retention a priority In today’s challenging economic climate, lenders who focus on personalized experiences, consistent communication, and relevant offers will stand out and retain borrowers. Leverage tools like Experian’s Retention Triggers to proactively engage customers, reduce churn, and foster long-term relationships for increased profitability and success. Learn more

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.

Market volatility, evolving regulations, and shifting consumer expectations are a catalyst to make energy providers to rethink how they operate. Rising energy costs, grid reliability concerns, and the push for sustainable energy sources add layers of complexity to an already challenging landscape. In this environment, data analytics in utilities has become a strategic imperative, enabling companies to optimize operations, mitigate risks, and enhance customer experiences. With a wealth of data at their disposal, utilities must harness the power of utility analytics to transform raw information into actionable intelligence. This is where Experian’s energy and utilities solutions come into play. With an unmatched data reach of more than 1.5 billion consumers and 201 million businesses, we are uniquely positioned to help energy and utility providers unlock greater potential within their organizations, whether that’s by boosting customer engagement, preventing fraud and verifying identities, or optimizing collections. Market Challenges Facing the Utilities Sector Utilities today face a series of economic, regulatory, and operational hurdles that demand innovative solutions. Regulatory and Compliance Pressures: Governments and regulatory bodies are tightening rules around emissions, sustainability, and grid reliability. Utilities must balance compliance with the need for cost efficiency. New carbon reduction mandates and reporting requirements force energy providers to adopt predictive modeling solutions that assess future demand and optimize energy distribution. Economic Uncertainty and Rising Costs: Inflation, fuel price fluctuations, and supply chain disruptions are impacting the cost of delivering energy. Utilities must find ways to improve financial forecasting and reduce inefficiencies—tasks well suited for advanced analytics solutions that optimize asset management and detect cost-saving opportunities. Grid Modernization and Infrastructure Investments: Aging infrastructure and increased energy demand require significant investments in modernization. Data-driven insights help utilities prioritize infrastructure upgrades, preventing costly failures and ensuring reliability. Predictive analytics models play a crucial role in identifying patterns that signal potential grid failures before they occur. Customer Expectations and Energy Transition: Consumers are more engaged than ever, demanding personalized service, real-time billing insights, and renewable energy options. Utilities must leverage advanced analytics to segment customer data, predict energy usage, and offer tailored solutions that align with shifting consumer preferences. Rising Fraud: Account takeover fraud, a form of identity theft where cybercriminals obtain credentials to online accounts, is on the rise in the utility sector. Pacific Gas and Electric Company reported over 26,000 reports of scam attempts in 2024 and has received over 1,700 reports of attempted scams in January 2025 alone. Utility and energy providers must leverage advanced fraud detection and identity verification tools to protect their customers and also their business. How Data Analytics Is Transforming the Utilities Industry Optimizing Revenue and Reducing Fraud Fraud and revenue leakage remain significant challenges. Utilities can use data and modeling to detect anomalies in energy usage, identify fraudulent accounts, and minimize losses. Experian’s predictive modeling solutions enable proactive fraud detection, ensuring financial stability for providers. Enhancing Demand Forecasting and Load Balancing With renewable energy sources fluctuating daily, accurate demand forecasting is critical. By leveraging utility analytics, providers can predict peak demand periods, optimize energy distribution, and reduce waste. Improving Credit Risk and Payment Management Economic uncertainty increases the risk of late or unpaid bills. Experian’s energy and utilities solutions help providers assess creditworthiness and develop more flexible payment plans, reducing bad debt while improving customer satisfaction. Why Experian? The Power of Data-Driven Decision Making Only Experian delivers a comprehensive suite of advanced analytics solutions that help utilities make smarter, faster, and more informed decisions. With more than 25 years of experience in the energy and utility industry, we are your partner of choice. Our predictive analytics models provide real-time risk assessment, fraud detection, and customer insights, ensuring utilities can confidently navigate today’s economic and regulatory challenges. In an industry defined by complexity and change, utilities that fail to leverage data analytics in utilities risk falling behind. From optimizing operations to enhancing customer engagement, the power of utility analytics is undeniable. Now is the time to act. Explore how Experian’s energy and utilities solutions can help your organization harness the power of advanced analytics to navigate market challenges and drive long-term success. Learn more Partner with our team

The credit card market is rapidly evolving, driven by technological advancements, economic volatility, and changing consumer behaviors. Our new 2025 State of Credit Card Report provides an in-depth analysis of the credit card landscape and strategy considerations for financial institutions. Findings include: Credit card debt reached an all-time high of $1.17 trillion in Q3 2024. About 19 million U.S. households were considered underbanked in 2023. Bot-led fraud attacks doubled from January to June 2024. Read the full report for critical insights and strategies to navigate a shifting market. Access report