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Powered by GenAI and increasingly accessible fraud tools, fraud threats are evolving faster than ever. Traditional fraud detection solutions alone are struggling to keep up with evolving fraud rings, fraud bots, and attack strategies, pushing businesses to explore smarter, more adaptive defenses. That’s why many organizations are turning to User and Entity Behavior Analytics (UEBA) as protection against growing threats, especially internal ones. But what exactly is UEBA, and how does it differ from other solutions, like behavioral analytics? What is UEBA? User and Entity Behavior Analytics is a cybersecurity and fraud prevention approach that uses behavior monitoring, network data, and machine learning to analyze users and entities (like devices, applications, and servers) within a network. By establishing a baseline of normal behavior and system usage, UEBA can detect anomalies that may indicate malicious activity (for example: a user who rarely uses large files downloading 5 GB of data in a short period of time, or one attempting several failed authentications). In short, UEBA monitors how users and systems typically behave and raises a red flag when something unusual happens. UEBA vs. behavioral analytics Behavioral analytics and UEBA are closely connected, sharing many of the same signals and goals. But, while the two terms are similar and often used interchangeably, they serve distinct purposes for fraud prevention. Behavioral analytics assesses risk based on how users interact with a website or mobile app session in real time. It evaluates data like mouse movements, keystrokes, swipes, and device and network intelligence to detect third-party fraud. These signals are typically used at the front end of digital interactions — during onboarding, login, or checkout flows — to prevent account opening fraud, account takeovers, fraudulent transactions, and more. Because it adds no additional user friction, behavioral analytics in fraud detection is a valuable first line of defense against fraud rings and bot attacks for financial institutions, merchants, fintechs, and other businesses that serve large volumes of external users. UEBA functions similarly, but operates at a deeper level and often serves a narrower population. UEBA starts with many of the same signals as behavioral analytics, but extends to include application usage, system access, server activity, and interactions between users and non-human entities like devices, service accounts, and cloud resources. UEBA is typically used to detect internal threats, such as insider attacks, compromised accounts, or lateral movement within a network. It builds long-term baselines and identifies anomalies that may indicate a security risk. Use cases for UEBA By analyzing the behavior of users and systems, UEBA helps organizations flag security threats within their networks. Below are some of the most impactful use cases where UEBA adds protection for businesses: Insider threat detection: Detects employees or contractors misusing access to steal data or sabotage systems. Example: An employee accessing sensitive files they’ve never touched before. Compromised account detection: Identifies accounts being accessed by someone other than their authorized owner. Example: A user logs in from a foreign country and downloads large volumes of data. Lateral movement detection: Tracks how attackers move within a network after gaining initial access. Example: A user account starts accessing multiple servers it has never interacted with before. A behavior-based approach to fraud prevention As fraud threats continue to evolve, behavior-based approaches like User and Entity Behavior Analytics are crucial to stopping sophisticated attacks. Behavioral analytics — the core of UEBA — can be the first step towards a more modern fraud prevention strategy, capable of stopping advanced threats without compromising the customer experience. Learn more about our behavioral analytics for fraud detection.

First mortgage delinquencies and foreclosures are increasing, particularly in later stages of delinquency. Home equity delinquencies remain low, signaling stability in that segment. Mortgage originations are up, with refinances beginning to recover. HELOC direct mail offers have surpassed first mortgage offers, driven by aggressive marketing and AVM-based personalization. Lenders using property data in marketing outperform peers relying on volume alone. Strategic focus for lenders: tighten risk analytics, integrate data into marketing, and adopt AVM-based personalization.

Fraud never sleeps, and neither do the experts working to stop it. That’s why we’re back with episode two of Meet the Maker, our video series spotlighting the brilliant minds behind Experian’s cutting-edge fraud solutions. In this episode, Nash Ali, Head of Operational Strategy, and Dave Tiezzi, Senior Vice President of Payments and New Markets, share how the power of NeuroID’s behavioral analytics and device and network intelligence, combined with Experian Link’s credit card owner verification, helps e-commerce merchants combat key fraud threats, while providing a seamless checkout experience. With decades of experience in payments and fraud, these fraud-fighting experts know exactly what it takes to stop fraud, minimize friction, and reduce chargebacks so e-commerce merchants can protect the most crucial stage of the buying process. Watch now for an exclusive look at the minds shaping the future of fraud prevention.  Interested in learning more about our fraud management solutions? Watch previous episode Learn more

Understanding generational trends and preferences is more crucial than ever, especially for the financial services industry.

With increasing regulatory complexities, compliance with model risk management requirements is crucial for operational resilience.

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

Experian and Plaid are teaming up to power smarter, faster, and more inclusive lending — fueled by real-time cashflow 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 cashflow 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 cashflow 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 cashflow 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 cashflow analytic capabilities and Plaid's streamlined access to real-time cashflow 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 cashflow 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.

Managing credit limits effectively can be a game-changer for both consumers and financial institutions. Understanding the benefits and behaviors associated with increased credit limits helps in appreciating the long-term impacts of this strategy. Proactive credit limit management offers numerous advantages that can significantly improve financial health and stability.

Managing credit limits effectively is vital for maintaining a profitable and resilient credit card portfolio. By adopting a proactive credit limit management approach, financial institutions can significantly improve customer satisfaction and increase revenue. As of Q3 2024, the average credit card limit in the U.S. stood at $32,025, marking a 4.1% rise from $30,763 the previous year.1 Proper management of these limits is crucial for financial institutions, as it impacts consumer borrowing capacity and overall financial health. Advantages of proactive credit limit increases A proactive credit limit increase occurs when a credit card issuer raises a cardholder’s credit limit without their request. This action is usually dependent on improvements to cardholders’ creditworthiness, including consistent, on-time payments and a reduction in debt. Proactive increases can enable financial institutions to: Increase interchange income: When financial institutions increase credit limits, they can boost transaction volumes, leading to higher interchange income. Generate additional interest income: Higher credit limits can lead to increased borrowing, resulting in more interest income for financial institutions. Enhance wallet share: By proactively increasing credit limits, institutions can encourage cardholders to use their cards more frequently, thereby increasing wallet share. Reduce customer attrition: When cardholders feel valued and supported, they are more likely to remain loyal to their financial institution. Proactive credit limit increases can reduce attrition rates by enhancing customer satisfaction and loyalty. Improve customer experience: Cardholders value the convenience and flexibility of higher credit limits, which can lead to increased satisfaction, positive sentiment and potential referrals. Implementing proactive credit limit increases To successfully implement proactive credit limit increases, financial institutions must address two key questions: Who receives an increase? Use insights to identify cardholders who meet the criteria for credit limit increases. Continuously monitor cardholders’ creditworthiness using advanced tools and technologies to ensure that only responsible cardholders receive increases. How much of an increase? Determine customers’ ability to afford additional credit by evaluating their annual income, debt-to-income ratio, and payment-to-income ratio. Set thresholds for these metrics to guide the decision-making process. Proactive credit limit management Effective credit limit management is essential for financial institutions aiming to grow their credit card portfolios and enhance customer satisfaction. Read this e-book on proactive credit limit management and discover how your institution can improve wallet share, enhance customer experience, and drive revenue. Read the e-book 1 Experian, Average Credit Card Debt Increases 3.5% to $6,730 in 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.

Loan loss analysis helps financial institutions identify the characteristics and performance of loans that have been lost to competitors.

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