Data & Analytics

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

Scott Brown presents at Reuters Next “If I were to look into a crystal ball, traditional lending methodology and processes will not be replaced; they will be augmented by consumers connecting to banks via APIs, contributing the data they are comfortable with, and banks using that in conjunction with historical credit data to offer newer and better products they didn’t have access to before. The convergence of data, tech and AI leads to more financial inclusion and a more modern way of underwriting.”Scott Brown, Group President Financial Services, Experian North America Scott Brown, Group President of Financial Services for Experian North America, recently presented at Reuters Next discussing the transformative power of AI and data analytics in financial services. The session also covered the top challenges that financial institutions face today and how advances in technology are helping organizations overcome those challenges. This keynote presentation was a timely follow-up to Brown’s previous appearance at the Money20/20 conference in Las Vegas, where he revealed the details of Experian’s latest innovation in GenAI technology, Experian Assistant. Brown, in a conversation with TV writer, producer and anchor Del Irani, spoke about the ethical considerations of AI innovation, what the future of underwriting may look like, and how open banking can drive financial inclusion and have a significant positive impact on both businesses and consumers. “If you are extending a line of credit to a given consumer, how do you do so in a way that’s integrated into their everyday lives? That’s where the concept of embedded finance comes in, and how to embed finance into a consumer’s life, and not the other way around.”Scott Brown, Group President Financial Services, Experian North America By embedding finance into consumers’ lives, and not the other way around, organizations can develop better strategies to balance risk and generate more revenue. He also focused on three foundational steps to take advantage of the capabilities AI offers: data quality, transparency, and responsibility. Areas of focus for implementing AI As organizations rely on more sophisticated approaches, data quality inputs are more important than ever. Inaccurate data can lead to poor business decisions that can have a negative impact on organizations’ bottom line. Transparency is also a crucial component of implementing AI solutions. Companies should be able to explain how their models work and why the end results make sense while avoiding biases. Leveraging data with AI tools allows organizations to get a better view of the consumer, which is a goal of most banks and lending institutions. Using that consumer data responsibly is important for financial institutions to establish and maintain trust with the people who use their services. While incorporating AI solutions into everyday business operations is important for financial institutions to better serve their consumers and remain competitive in the industry, a lack of access to AI tools can prevent some organizations from doing so. A fragmented approach leads to higher costs, lower efficiency, and greater risk Until recently, financial institutions have had to rely on several different technology providers and tools to optimize customer experience and operational efficiency while protecting consumers from the risk of identity theft and fraud. This fragmented approach can result in increased costs for organizations and higher risk for consumers. Now, AI technology is solving this issue by integrating functionality into a single platform, such as the Experian Ascend Technology Platform™. This streamlined access to a comprehensive suite of tools can help accelerate time-to-value while also eliminating compliance risks. Full interview available now Brown’s full interview at Reuters Next reveals more details about how Experian is empowering organizations to better serve their consumers’ financial needs through AI innovation while also helping more than 100 million Americans who don’t have access to the mainstream credit ecosystem due to being credit invisible, unscoreable, or have a low credit score. Watch the full interview to learn more about how Experian is continuing to bring financial power to all through innovative technology. Watch the full interview now

Do you know where your customers stand? Not literally, of course, but do you know how recent macroeconomic changes and their personal circumstances are currently affecting your portfolio? While refreshing your customers’ credit data quarterly works for some aspects of portfolio management, you need more frequent access to fresh data to quickly respond to risky customer behavior and new credit needs before your portfolio takes a hit. Use triggers to improve portfolio management Event-based credit triggers provide daily or real-time alerts about important changes in your customers’ financial situations. You can use these to manage risk by promptly responding to signs of changing creditworthiness or to prevent attrition by proactively reaching out to customers who are shopping for credit. Risk Triggers℠ and Retention Triggers℠ offer a real-time solution that can be customized to fit your needs for daily portfolio management. What are Risk Triggers? Experian’s Risk Triggers alert you of notable information, such as unfavorable utilization rate changes, delinquencies with other lenders and recent activity with high-interest, short-term loan products. This solution allows you to monitor how your customers manage accounts with other lenders to get ahead of potential risk on your book. You can use Risk Triggers to get daily insights into your customers’ activity — allowing you to quickly identify potentially risky behavior and take appropriate action to limit your exposure and losses. Types of Risk Triggers Choose from a defined Risk Triggers package that could help you identify high-risk customers, including: New trades Increasing credit utilization or balances over limit New collection accounts An account is charged-off A credit grantor closes an account New delinquency statuses (30 to 180 days past due) Consumers seeking access to short-term, high-risk financing options Bankruptcy and deceased events How to use Risk Triggers You can use the daily alerts from Risk Triggers to help inform your account management strategy. Depending on the circumstances, you might: Decrease credit limits Close or freeze accounts Accelerate payment requests Continue monitoring accounts for other signs of risk Spotlight on Experian’s Clarity Services events Included in Risk Triggers are events from Experian’s Clarity Services, which draw on expanded FCRA-regulated data* from a leading source of alternative financial credit data.  For example, you could get an alert when someone has a new inquiry from non-traditional loans. These triggers provide a broader view of the customer – offering added protection against risky behavior. What are Retention Triggers? Experian’s Retention Triggers can alert you when a customer improves their creditworthiness, is shopping for new credit, opens a new tradeline or lists property. Proactively responding to these daily alerts can help you retain and strengthen relationships with your customers — which is often less expensive than acquiring new customers. Types of Retention Triggers Choose from over 100 Retention Triggers to bundle, including: New trades New inquiries Credit line increases Property listing statuses Improving delinquency status Past-due accounts are brought current or paid off How to use Retention Triggers You can use Retention Triggers to increase lifetime customer value by proactively responding to your customers’ needs and wants. You might: Increase credit limits Offer promotional financing, such as balance transfers Introduce perks or rewards to strengthen the relationship Append attributes for improved decisioning  By appending credit attributes to Risk and Retention Trigger outputs, you can gain greater insight into your accounts.  Premier AttributesSM is Experian's core set of 2,100-plus attributes. These can quickly summarize data from consumers' credit reports, allowing you to more easily segment accounts to make more strategic decisions across your portfolio.  Trended 3DTM attributes can help you spot and understand patterns in a customer's behavior over time. Integrating trended attributes into a triggers program can help you identify risk and determine the next best action. Trended 3D includes more than 2,000 attributes and provides insights into industries such as bankcard, mortgage, student loans, personal loans, collections and much more.  By working with both triggers and attributes, you'll proactively review an account, so you can then take the next best action to improve your portfolio's profits. Customize your trigger strategy When you partner with Experian, you can bundle and choose from hundreds of Risk and Retention Triggers to focus on risk, customer retention or both. Additionally, you can work with Experian’s experts to customize your trigger strategy to minimize costs and filter out repetitive or unneeded triggers: Use cool-off periods Set triggering thresholds Choose which triggers to monitor Establish hierarchies for which triggers to prioritize Create different strategies for segments of your portfolio Learn more about Risk and Retention Triggers. Learn more *Disclaimer: “Alternative Financial Credit Data” refers to the use of alternative data and its appropriate use in consumer credit lending decisions, as regulated by the Fair Credit Reporting Act. Hence, the term “Expanded FCRA-Regulated Data” may also apply in this instance, and both can be used interchangeably.

How can lenders ensure they’re making the most accurate and fair lending decisions? The answer lies in consistent model validations. What are model validations? Model validations are vital for effective lending and risk-based pricing programs. In addition to helping you determine which credit scoring model works best on your portfolio, the performance (odds) charts from validation results are often used to set score cutoffs and risk-based pricing tiers. Validations also provide the information you need to implement a new score into your decisioning process. Factors affecting model validations Understanding how well a score predicts behavior, such as payment delinquency or bankruptcy, enables you to make more confident lending decisions. Model performance and validation results can be impacted by several factors, including: Dynamic economic environment – Shifts in unemployment rates, interest rate hikes and other economic indicators can impact consumer behavior. Regulatory changes affecting consumers – For example, borrowers who benefited from a temporary student loan payment pause may face challenges as they resume payments. Scorecard degradation – A model that performed well several years ago may not perform as well under current conditions. When to perform model validations The Office of the Comptroller of the Currency’s Supervisory Guidance on Model Risk Management states model validations should be performed at least annually to help reduce risk. The validation process should be comprehensive and produce proper documentation. While some organizations perform their own validations, those with fewer resources and access to historical data may not be able to validate and meet the guidance recommendations. Regular validations support compliance and can also give you confidence that your lending strategies are built on solid, current data that drive better outcomes. Good model validation practices are critical if lenders are to continue to make data-driven decisions that promote fairness for consumers and financial soundness for the institution. Make better lending decisions If you’re a credit risk manager responsible for the models driving your lending policies, there are several things you can do to ensure that your organization continues to make fair and sound lending decisions: Assess your model inventory. Ensure you have comprehensive documentation showing when each model was developed and when it was last validated. Validate the scores you are using on your data, along with those you are considering, to compare how well each model performs and determine if you are using the most effective model for your needs. Produce validation documentation, including performance (odds) charts and key performance metrics, which can be shared with regulators. Utilize the performance charts produced from the validation to analyze bad rates/approval rates and adjust cutoff scores as needed. Explore alternative credit scoring models to potentially enhance your scoring process. As market conditions and regulations continue to evolve, model validations will remain an essential tool for staying competitive and making sound lending decisions. Ready to ensure your lending decisions are based on the latest data? Learn more about Experian’s flexible validation services and how we can support your ongoing success. Contact us today to schedule a consultation. Learn more

In today's data-driven business landscape, leveraging advanced targeting techniques is crucial for effective consumer engagement, particularly in the financial services sector. Prescreen targeting solutions have evolved significantly, offering a competitive edge through more precise and impactful outreach strategies. The power of data analytics and predictive modeling At the heart of modern prescreen targeting solutions lies the integration of extensive data analytics and predictive modeling. These systems combine detailed consumer information, including purchasing behaviors and credit scores, with sophisticated algorithms to identify potential customers most likely to respond positively to specific promotional campaigns. This approach not only streamlines campaign efforts but also enhances the tactical effectiveness of each interaction. Direct mail: a proven channel for financial services In the competitive North American financial services market, direct mail has demonstrated distinct advantages as a targeting channel. Its tangible nature helps cut through digital noise, capturing consumer attention effectively. For credit products, direct mail typically achieves engagement rates of 0.2-2% for prime consumers and 1-3% for near-prime and subprime consumers[1]. Key advantages of prescreen targeting solutions Enhanced response rates Custom response models can significantly boost prospect response rates by targeting a well-defined, high-propensity audience. These models have the potential to improve average response rates of prescreen direct mail campaigns by 10-25%. Risk mitigation By focusing on well-defined, high-propensity audiences, prescreen targeting via direct mail aims to attract the right prospects, minimizing fraud and delinquency risks. This targeted approach can lead to substantial savings on underwriting costs. Improved customer engagement and retention Personalized direct mail strengthens customer relationships by making recipients feel valued, leading to higher engagement and loyalty – crucial factors for long-term business success. Regulatory compliance and security Prescreen solutions come equipped with compliance safeguards, simplifying adherence to industry regulations and consumer privacy standards. This is particularly critical in the highly regulated financial sector. The future of targeting and enhancement As markets continue to evolve, the strategic importance of precise and efficient marketing techniques will only grow. Financial institutions leveraging optimized prescreen targeting and enhancement solutions can gain a significant competitive advantage, achieving higher immediate returns and fostering long-term customer loyalty and brand strength. Future advancements in AI and machine learning are expected to further refine prescreen targeting capabilities, offering even more sophisticated tools for marketers to engage effectively with their target audiences. Ascend Intelligence Services™ Target Ascend Intelligence Services Target is a sophisticated prescreening solution that boosts direct mail response rates. It uses comprehensive trended and alternative data, capturing credit and behavior patterns to iterate through direct mail response models and mathematical optimization. This enhances the target strategy and maximizes campaign response, take-up rates, and ROI within business constraints. Visit our website to learn more [1] Experian Research, Data Science Team, July 2024 

In this article...What is reject inference? How can reject inference enhance underwriting? Techniques in reject inference Enhancing reject inference design for better classification How Experian can assist with reject inference  In the lending world, making precise underwriting decisions is key to minimizing risks and optimizing returns. One valuable yet often overlooked technique that can significantly enhance your credit underwriting process is reject inferencing. This blog post offers insights into what reject inference is, how it can improve underwriting, and various reject inference methods.  What is reject inference?  Reject inference is a statistical method used to predict the potential performance of applicants who were rejected for a loan or credit — or approved but did not book. In essence, it helps lenders and financial institutions gauge how rejected or non-booked applicants might have performed had they been accepted or booked. By incorporating reject inference, you gain a more comprehensive view of the applicant pool, which leads to more informed underwriting decisions.  Utilizing reject inference helps reduce biases in your models, as decisions are based on a complete set of data, including those who were initially rejected. This technique is crucial for refining credit risk models, leading to more accurate predictions and improved financial outcomes.  How can reject inference enhance underwriting?  Incorporating reject inference into your underwriting process offers several advantages:  Identifying high-potential customers: By understanding the potential behavior of rejected applicants, you can uncover high-potential customers who might have been overlooked before.  Improved risk assessment: Considering the full spectrum of applicants provides a clearer picture of the overall risk landscape, allowing for more informed lending decisions. This can help reduce default rates and enhance portfolio performance.  Optimizing credit decisioning models: Including inferred data from rejected and non-booked applicants makes your credit scoring models more representative of the entire applicant population. This results in more robust and reliable predictions.  Techniques in reject inference  Several techniques are employed in reject inference, each with unique strengths and applications. Understanding these techniques is crucial for effectively implementing reject inference in your underwriting process. Let's discuss three commonly used techniques:  Parceling: This technique involves segmenting rejected applicants based on their characteristics and behaviors, creating a more detailed view of the applicant pool for more precise predictions.  Augmentation: This method adds inferred data to the dataset of approved applicants, producing a more comprehensive model that includes both approved and inferred rejected applicants, leading to better predictions.  Reweighting: This technique adjusts the weights of approved applicants to reflect the characteristics of rejected applicants, minimizing bias towards the approved applicants and improving prediction accuracy.  Pre-diction method  The pre-diction method is a common approach in reject inference that uses data collected at the time of application to predict the performance of rejected applicants. The advantage of this method is its reliance on real-time data, making it highly relevant and current.  For example, pre-diction data can include credit bureau attributes from the time of application. This method helps develop a model that predicts the outcomes of rejected applicants based on performance data from approved applicants. However, it may not capture long-term trends and could be less effective for applicants with unique characteristics.  Post-diction method  The post-diction method uses data collected after the performance window to predict the performance of rejected applicants. Leveraging historical data, this method is ideal for capturing long-term trends and behaviors.  Post-diction data may include credit bureau attributes from the end of the performance window. This method helps develop a model based on historical performance data, which is beneficial for applicants with unique characteristics and can lead to higher performance metrics. However, it may be less timely and require more complex data processing compared to pre-diction.  Enhancing reject inference design for better classification  To optimize your reject inference design, focus on creating a model that accurately classifies the performance of rejected and non-booked applicants. Utilize a combination of pre-diction and post-diction data to capture both real-time and historical trends.  Start by developing a parceling model using pre-diction data, such as credit bureau attributes from the time of application, to predict rejected applicants' outcomes. Regularly update your model with the latest data to maintain its relevance.  Next, incorporate post-diction data, including attributes from the end of the performance window, to capture long-term trends. Combining both data types will result in a more comprehensive model.  Consider leveraging advanced analytics techniques like machine learning and artificial intelligence to refine your model further, identifying hidden patterns and relationships for more accurate predictions. How Experian can assist with reject inference  Reject inference is a powerful tool for enhancing your underwriting process. By predicting the potential performance of rejected and non-booked applicants, you can make more inclusive and accurate decisions, leading to improved risk assessment and optimized credit scoring models.  Experian offers various services and solutions to help financial institutions and lenders effectively implement reject inference into their decisioning strategy. Our solutions include comprehensive and high-quality datasets, which empower you to build models that are more representative of the entire applicant population. Additionally, our advanced analytics tools simplify data analysis and model development, enabling you to implement reject inference efficiently without extensive technical expertise.  Ready to elevate your underwriting process? Contact us today to learn more about our suite of advanced analytics solutions or hear what our experts have to say in this webinar.  Watch Webinar Learn More This article includes content created by an AI language model and is intended to provide general information. 

Voter registration lists are the backbone of our democratic process. However, maintaining accurate and up-to-date lists is a challenge that election agencies constantly face. With several regulations related to voting and election integrity that have been enacted or proposed in the last two years, maintaining a quality voter list is more important now than ever before. Election officials now have a powerful tool at their disposal: commercially available data to enhance voter list maintenance and boost voter confidence.  The importance of maintaining voter lists  Audit teams within election agencies are tasked with ensuring election integrity through voter list maintenance. These teams need reliable tools to:  Verify and correct voter registration data  Identify and update contact information  Provide a cost-effective method for record updates  Reduce election costs for taxpayers  Success stories in voter list management  Case study: West Virginia Secretary of State   The West Virginia Secretary of State (WVSOS) uses Experian’s TrueTrace™ solution to enhance voter roll maintenance. Traditionally a skip-tracing solution for debt collectors, TrueTrace leverages unique data sources to ensure voters receive correct information, reducing wasted resources and improving election efficiency.  WVSOS was challenged with enhancing their existing processes to a more robust 50-state comparison for cross-state movers.  After WVSOS’s first data pull using TrueTrace, nearly 16,000 individuals were identified with a potential new "best" address that were also not flagged by other data comparison programs used by the state.  The results? Of the almost 16,000 mailings sent, about 25% returned were undeliverable, confirming those individuals had moved.   Access the full case study to discover best practices for maintaining voter rolls and conducting cost-effective elections.  Webinar: El Paso County Clerk & Recorder's Office   The El Paso County Clerk & Recorder’s Office was looking to bring transparency, efficiency, and increased voter confidence to the elections in El Paso County, Texas. To achieve this, they required verified enriched data for registered voters. By partnering with Experian, voter data was enriched using our TrueTrace solution. This partnership has enabled the Office to verify and append the most up-to-date voter address, leading to significant improvements in list maintenance.   To date, the El Paso County Clerk & Recorder’s Office has seen a reduction of more than $39,860 in undeliverable ballot costs. Their ROI to date is a positive $4,537 back to the citizens of El Paso County.  Listen to our on-demand webinar to hear more about this collaboration.  Visit us online to learn more about our public sector solutions. Learn more

This series will dive into our monthly State of the Economy report, providing a snapshot of the top monthly economic and credit data for those in financial services to proactively shape their business strategies. The labor market has been a source of strength for the U.S. economy coming out of the pandemic, providing workers with stable employment and solid wages. However, the labor market has slowed in recent months, with lower-than-expected job creation and rising unemployment, causing weakening sentiment in the broader market. This has resulted in increased pressure on the Federal Reserve to begin cutting rates and places more importance on the incoming data between now and the September FOMC meeting. Data highlights from this month’s report include: Job creation declined in July, falling short of economists’ expectations. Unemployment increased from 4.1% to 4.3%. Inflation cooled again in July, with annual headline inflation easing from 3.0% to 2.9%. GDP picked up in Q2 to 2.8%, primarily driven by strong consumer spending. Check out our report for a deep dive into the rest of this month’s data, including the latest trends in originations, retail sales, and the new housing market. Download August's report To have a holistic view of our current environment, it’s important to view the economy from different angles and through different lenses. Download our latest macroeconomic forecasting report for our views on what's to come in the U.S. economy and listen to our latest Econ to Action podcast. For more economic trends and market insights, visit Experian Edge.

Gen Z, or "Zoomers," born from 1997 to 2012, are molded by modern transformations. They have witnessed events from post-9/11 impacts to the rise of the internet and the COVID-19 crisis. As early adopters of technology, their lives are intertwined with smartphones, online shopping, social platforms, cloud services, emerging fintech, and artificial intelligence. They are called “digital natives” as they are the first generation to grow up with internet as part of their daily life. Research generally indicates that this post-millennial generation values practicality, favoring financial stability over entrepreneurial pursuits. They appreciate communication tailored to them and often employ social media to cultivate their personal brands. As a generation growing up immersed in technology, they tend to choose digital interactions, seeking to forge robust, secure, genuine, and unconstrained digital experiences. The challenge of identity verification Identity verification presents a considerable challenge for Generation Z. According to a Fortune survey, close to 50% of this demographic regrets not opening financial accounts earlier, citing a lack of readiness to join the financial ecosystem by the age of 18. Consequently, this has given rise to "digital ghosts"—people with minimal or nonexistent financial histories who face challenges when trying to utilize financial services. The 2009 Credit Card Accountability Responsibility and Disclosure Act mandates that individuals under 21 need a cosigner or show income proof to get a credit card, hindering their early financial involvement. Moreover, conventional identity checks are becoming less reliable due to the surge in identity theft. Innovative solutions for verifying Gen Z Verifying identities and preventing fraud among Gen Z presents unique challenges due to their digital-native status and limited credit histories. Here are some effective strategies and approaches that financial institutions can adopt to address these challenges: Leveraging alternative data sources Academic records leverage information from higher learning institutions such as universities, colleges, and vocational schools. This data can be vital for authenticating the identities of younger individuals who may lack a substantial credit history. Employment verification retrieve data confirming the identity and employment status, especially focusing on Gen Z who are new to the job market. Utility and telecom records leverage payment histories for utilities, phone bills, and other recurring services, which can provide additional layers of identity verification. Alternative financial data includes online small dollar lenders, online installment lenders, single payment, line of credit, storefront small dollar lenders, auto title and rent-to-own. Phone-Centric ID Phone-Centric Identity refers to technology that leverages and analyzes mobile, telecom, and other signals for the purposes of identity verification, identity authentication, and fraud prevention. Phone-Centric Identity relies on billions of signals from authoritative sources pulled in real time, making it a powerful proxy for digital identity and trust. Advance authentication technologies Behavioral biometrics analyze user behaviors such as typing patterns, navigation habits, and device usage. These subtle behaviors can help create a unique profile for each user, making it difficult for fraudsters to impersonate them. Adaptive risk-based authentication that adjusts the level of security based on the user's behavior, location, device, and other factors. For example, a higher level of verification might be required for transactions that are deemed unusual or high-risk. Real-time fraud detection AI and machine learning: Deploy AI and machine learning algorithms to analyze transaction patterns and detect anomalies in real-time. These technologies can identify suspicious activities and flag potential fraud. Fraud analytics: Use predictive analytics to assess the likelihood of fraud based on historical data and current behavior. This approach helps in proactively identifying and mitigating fraudulent activities. Secure digital onboarding Digital identity verification: Implement digital onboarding processes that include online identity verification with real-time document verification. Users can upload government-issued IDs and take selfies to confirm their identity. Video KYC (Know Your Customer): Use video calls to conduct KYC processes, allowing bank representatives to verify identities and documents remotely via automated identity verification. This method is secure and convenient for tech-savvy Gen Z customers. Make identity verification easy To authenticate identities and combat fraud within the Gen Z population, financial organizations need to implement a comprehensive strategy utilizing innovative technologies, non-traditional data, and strong protective protocols. Such actions will enable the creation of a trustworthy and frictionless banking environment that appeals to a generation adept in digital interactions, thereby establishing trust and encouraging enduring connections. To learn more about Experian’s automated identity verification solutions, visit our website. Learn more 

In this article...Recent trends in credit card debtThe rising tide of delinquenciesWhat is credit limit optimization?Benefits of credit limit optimizationEconomic indicators and CLO ImpactEnhanced profitability and risk mitigation This post was originally published on our Global Insights Blog. As credit card issuers grow, the size of their customer base expands, bringing both opportunities and challenges. One of the most critical challenges is managing growth while controlling default rates. Credit limit optimization (CLO) has emerged as a vital tool for banks and credit lenders to achieve this balance. By leveraging machine learning models and mathematical optimization, CLO enables lenders to tailor credit limits to individual customers, enhancing profitability while mitigating risk. Recent trends in credit card debt To understand the significance of CLO, it is essential to consider the current economic landscape. The first quarter of 2024 saw total household debt in the U.S. rise by $184 billion, reaching $17.69 trillion. While credit card balances declined slightly (a reflection of seasonal factors and consumer spending patterns), they remain a substantial component of household liabilities, with total credit card debt standing at approximately $1.26 trillion in early 2024. On average, American households hold around $10,479 in credit card debt, which is down from previous years but still significant. The average APR for credit cards in the first quarter of 2024 was 21.59%.* The rising tide of delinquencies In the first quarter of 2024, about 8.9% (annualized) of credit card balances transitioned into delinquency. This trend underscores the need for credit card issuers to adopt more sophisticated methods to assess credit risk and adjust credit limits accordingly. The rising rate of credit card delinquencies is a key driver behind the adoption of CLO strategies. What is credit limit optimization? Credit limit optimization uses advanced analytics to assess individual customers’ creditworthiness. By analyzing various data points, including payment history, income levels, spending patterns, and economic indicators, these tools can recommend optimal credit limits that maximize customer spending potential while minimizing the risk of default, all within the constraints set by the business in terms of its appetite for risk and capacity. For instance, a customer with a strong payment history and stable income might receive a higher credit limit, encouraging more spending and enhancing the lender’s revenue through interest and interchange fees. Conversely, customers showing signs of financial stress might see their credit limit reduced to prevent them from accumulating unmanageable debt. Benefits of credit limit optimization Improved profitability – By setting credit limits reflecting customers’ credit risk and spending potential, lenders can increase their revenue through higher interest and fee income. Reduced default rates – Lenders can significantly reduce the incidence of bad debt by identifying customers at risk of default and adjusting their credit limits accordingly. Improved customer satisfaction – Personalized credit limits can improve customer satisfaction, as customers are more likely to receive credit that matches their needs and financial situation. Regulatory compliance – CLO can help lenders comply with regulatory requirements by ensuring that credit limits are set based on objective, data-driven criteria. Economic indicators and CLO Impact Several economic indicators provide context for the importance of CLO in the current market. For instance, the Federal Reserve reported that in 2023, fewer than half of adult credit cardholders carried a balance on their cards, down from previous years. This indicates a more cautious approach to credit use among consumers, likely influenced by economic uncertainty and rising interest rates. Moreover, the disparity in credit card debt across different states highlights the varying economic conditions and the need for tailored credit strategies. States like New Jersey have some of the highest average credit card debts, while states like Mississippi have the lowest. This regional variation underscores lenders’ need to adopt flexible, data-driven approaches to credit limit setting. Enhanced profitability and risk mitigation Credit limit optimization is critical for credit card issuers aiming to balance growth and risk management. As economic conditions evolve and consumer behaviors shift, the ability to set personalized credit limits will become increasingly important. By leveraging advanced analytics and machine learning, CLO enhances profitability and contributes to a more stable and resilient financial system. One such solution is Experian’s Ascend Intelligence Services™ Limit, which provides an optimized strategy designed to enhance the precision and effectiveness of credit limit assignments. Ascend Intelligence Services™ Limit combines best-in-class bureau data with machine learning to simulate the impact of different credit limits in real time. This capability allows lenders to quickly test and refine their credit limit strategies without the lengthy trial-and-error period traditionally required. Ascend Intelligence Services Limit enables lenders to set credit limits that align with their business objectives and risk tolerance. By providing insights into the likelihood of default and potential revenue for each credit limit scenario, Ascend Intelligence Services Limit helps design optimal limit strategies. This not only maximizes revenue but also minimizes the risk of defaults by ensuring credit limits are appropriate for each customer’s financial situation. In a landscape marked by rising delinquencies and varying regional debt levels, the strategic use of CLO like Ascend Intelligence Services Limit represents a forward-thinking approach to credit management, benefiting both lenders and consumers. Learn More * HOUSEHOLD DEBT AND CREDIT REPORT (Q1 2024) – Federal Reserve Bank of New York

Getting customers to respond to your credit offers can be difficult. With the advent of artificial intelligence (AI) and machine learning (ML), optimizing credit prescreen campaigns has never been easier or more efficient. In this post, we'll explore the basics of prescreen and how AI and ML can enhance your strategy.  What is prescreen?  Prescreen involves evaluating potential customers to determine their eligibility for credit offers. This process takes place without the consumer’s knowledge and without any negative impact on their credit score.  Why optimize your prescreen strategy?  In today's financial landscape, having an optimized prescreen strategy is crucial. Some reasons include:  Increased competition: Financial institutions face stiff competition in acquiring new customers. An optimized prescreen strategy helps you stand out by targeting the right individuals with tailored offers, increasing the chances of conversion.  Customer expectations: Modern customers expect personalized and relevant offers. An effective prescreen strategy ensures that your offers resonate with the specific needs and preferences of potential customers.  Strict budgets: Organizations today are faced with a limited marketing budget. By determining the right consumers for your offers, you can minimize prescreen costs and maximize the ROI of your campaigns.  Regulatory compliance: Compliance with regulations such as the Fair Credit Reporting Act (FCRA) is essential. An optimized prescreen strategy helps you stay compliant by ensuring that only eligible individuals are targeted for credit offers.  Financial inclusion: 49 million American adults don’t have conventional credit scores. An optimized prescreen strategy allows you to send offers to creditworthy consumers who you may have missed due to a lack of traditional credit history.  How AI and ML can enhance your strategy  AI and ML can revolutionize your prescreen strategy by offering advanced analytics and custom response modeling capabilities.  AI-driven data analytics  AI analytics allow financial institutions to analyze vast amounts of data quickly and accurately. This enables you to identify patterns and trends that may not be apparent through traditional analysis. By leveraging data-centric AI, you can gain deeper insights into customer behavior and preferences, allowing for more precise targeting and increased response rates.  LEARN MORE: Explore the benefits of AI for credit unions.  Custom response modeling  Custom response models enable you to better identify individuals who fall within your credit criteria and are more likely to respond to your credit offers. These models consider various factors such as credit history, spending habits, and demographic information to predict future behavior. By incorporating custom response models into your prescreen strategy, you can select the best consumers to engage, including those you may have previously overlooked.  LEARN MORE: AI can be leveraged for numerous business needs. Learn about generative AI fraud detection.   Get started today  Incorporating AI and ML into your prescreen campaigns can significantly enhance their effectiveness and efficiency. By leveraging Experian's Ascend Intelligence Services™ Target, you can better target potential customers and maximize your marketing spend.   Our optimized prescreen solution leverages:  Full-file credit bureau data on over 245 million consumers and over 2,100 industry-leading credit attributes.  Exclusive access to the industry's largest alternative datasets from nontraditional lenders, rental data inputs, full-file public records, and more.  24 months of trended data showing payment patterns over time and over 2,000 attributes that help determine your next best action.  When it comes to compliance, Experian leverages decades of regulatory experience to provide the documentation needed to explain lending practices to regulators. We use patent-pending ML explainability to understand what contributed most to a decision and generate adverse action codes directly from the model.  For more insights into Ascend Intelligence Services Target, view our infographic or contact us at 855 339 3990. View infographic This article includes content created by an AI language model and is intended to provide general information. 

This series will dive into our monthly State of the Economy report, providing a snapshot of the top monthly economic and credit data for those in financial services to proactively shape their business strategies. While much of the economic data released this month remained steady, including continued downward progress in inflation and resilience in inflation-adjusted spending, June was a pivotal month for the labor market. With downward revisions to job creation over the past few months to an up-tick in unemployment, the potential for a sooner-than-expected rate cut increased. Data highlights from this month’s report include: While above economists’ expectations in June, job creation was 111,000 jobs shy of what was recorded in April and May, signaling some slowdown in the labor market. Inflation-adjusted spending and incomes increased in May, by 0.3% and 0.5%, respectively. Inflation eased more than economists expected, with annual headline inflation cooling from 3.3% to 3.0%. Check out our report for a deep dive into the rest of this month’s data, including the latest trends in job openings, new business survival rates, and bankcard delinquency rates. Download July's report  To have a holistic view of our current environment, it’s important to view the economy from different angles and through different lenses. Watch our experts discuss the latest economic and credit trends in the next macroeconomic forecasting webinar and listen to our latest Econ to Action podcast. For more economic trends and market insights, visit Experian Edge.

Open banking has been leveraged for years in the U.S. The anticipated U.S. regulation under section 1033 of the Dodd-Frank Act, combined with the desire to expand lending universes, has increased interest and urgency among financial institutions to incorporate open banking flows into their workstreams.  With technological improvements, increased data availability, and increasing consumer awareness around the benefits of data value exchange, financial service providers can use consumer-permissioned data to gain new insights. For example, access to bank account transactional data, permissioned appropriately, provides important attributes into risk, spend and income behaviors, and financial health, while equipping institutions with intelligence they can harness to help meet various business objectives.  Current state of open banking Open Banking use cases are extensive and will continue to expand as access to permissioned data becomes more common. Second chance underwriting, where a lender retrieves additional insights to potentially reverse the primary declination, is the most prevalent use case in the market today.  Where a consumer may have limited or no credit history, this application of cashflow attributes and scores in a decisioning flow can help many consumers access financial services where they cannot be fully underwritten on credit data alone.  And it is not just consumer behavior and willingness to permission their data that will accelerate open banking in financial services. The technology enabling access, security, standardization, and categorization is equally critical. New and existing players across the ecosystem are rolling out new solutions to drive results for financial institutions.  The benefits of open banking are vast as highlighted recently by Craig Focardi, Principal Analyst at Celent: “The final adoption of the CFPB’s proposed rule under Section 1033 will accelerate open banking in the US,” said Focardi. “Although open banking is operating effectively under existing consumer protection/privacy and related laws and regulations, this modern opening banking rule will enhance consumer control over their data for privacy and security, help consumers better manage their finances, and help them find the best products and banking relationships. For financial institutions, it will level the competitive playing field for smaller financial institutions, increase competition for customer relationships, and incentivize all financial institutions to invest in technology, data, and analytics to adopt open banking more quickly.” Despite the wealth of information that open banking can offer, institutions are at varying stages of maturity when it comes to using this data in production, with fintechs and challenger banks leading the way. However, most banks are researching and planning to take advantage of the insights unlocked through open banking – particularly cashflow data.  But why is there not wider adoption when this ‘new’ data can offer such rich and actionable insights?  The answer varies, but it is top of mind for risk officers, analysts and marketers. Some financial institutions are worried about application drop-off as consumers move through a data consent journey. Others are taking a wait-and-see approach as they are concerned about incorporating open banking flows only to see regulation upend the application of permissioned data.   Regardless of readiness, most organizations are in various stages of testing new permissioned data sources to understand the implications. Experian has helped many financial institutions understand the power of consumer-permissioned data through analytics and specific tests leveraging client transactional data and our cashflow models. On aggregate, we see cashflow data perform well on its own in determining a consumer’s likelihood of going 60 days past due over 12 months; however, it is best used in combination with traditional and alternative credit data to achieve optimal performance of underwriting models.   But what about consent? Will consumers be open to permissioning their data?  From our research, we see that consumers are willing to give permission if the benefits are explained and they understand how their data will be used. In fact, 70% of consumers report they are likely to share banking data for better loan rates, financial tools, or personalized spending insights.1 Experian reveals new solutions for open banking We at Experian are excited about the benefits open banking can provide, including: Giving more control to consumers: Consumers are hungry for more control over their data. We have seen this ourselves with Experian Boost®. When the benefits of data sharing are properly explained, and consumers can control when and how that data is used, it is empowering and allows consumers the potential to unlock new financial opportunities.   Improving risk assessment: As mentioned above, analysis shows that cash flow data (transactional open banking data) is very predictive on its own. Adding our credit data delivers even greater predictability, enabling lenders to score more consumers and offer the right products, services, and pricing.   Augmenting existing strategies: Open banking is not a new strategy; it augments and improves many existing processes. Institutions do not need to start something from scratch; they can layer incremental data into existing processes for an improved risk assessment, deeper insights, and a better customer experience.  Open banking is not a new strategy; it augments and improves many existing processes. Institutions do not need to start something from scratch; rather, they can layer incremental data into existing processes for an improved risk assessment, deeper insights, and a better customer experience.  We’re helping institutions unlock the power of open banking data by transforming transaction data into precise categories, a foundational component of cashflow analytics that feeds into the calculation of attributes and scores. These new Cashflow Attributes can be easily plugged into existing underwriting, analytic, and account management use cases. Early indicators show that Cashflow Attributes can boost predictive accuracy by up to 20%, allowing lenders to drive revenue growth while mitigating risk.2 Open banking is emerging in the industry across various use cases. Many are only just realizing the potential insights and benefits this can have to consumers and their organizations. How will you leverage open banking? Learn more about how we're helping address open banking 1Atomik Research survey of 2,005 U.S. adults online, matching national demographics. Fieldwork: March 17-21, 2024. 2Experian analysis based on GINI predictability. GINI coefficient measures income or wealth inequality within a population, with 0 indicating perfect equality and 1 indicating perfect inequality, reflecting predictive capability.

Finding a balance between providing secure financial services and user-friendly experiences is no easy task. One of the biggest hurdles? Ensuring identity authentication is robust and reliable. Let's walk through the essentials of identity authentication, its importance, and what effective solutions look like. What is identity authentication? Identity authentication is the process of proving that an individual is who they claim to be. Unlike identity verification, which simply confirms that the provided identity information is valid, identity authentication goes a step further by ensuring that the person presenting the information is indeed its rightful owner. At its core, identity authentication relies on various methods to verify identities. These methods can range from simple password checks to more sophisticated technologies like biometrics and adaptive authentication. The goal is to create multiple layers of security that make it difficult for unauthorized users to gain access. Types of authentication methods Several types of identity authentication methods are used today. Passwords and PINs are the most basic forms, but they are increasingly being supplemented or replaced by more advanced solutions like multi-factor authentication (MFA) , biometric scans, and knowledge-based authentication (KBA). Each method has its advantages and limitations, making it crucial for financial institutions to choose the right mix. Authentication vs. verification While often used interchangeably, identity verification and identity authentication serve different purposes. Identity verification solutions confirm that the provided identity information matches public records, whereas identity authentication solutions ensure that the person presenting the information is its true owner. Identity verification is typically a one-time process conducted at the beginning of a relationship, such as when opening a new bank account. On the other hand, identity authentication is an ongoing process, ensuring that each login or transaction is carried out by a legitimate user. Though different, these processes are crucial for financial institutions. They work together to provide a robust security framework that minimizes the risk of fraud while offering a seamless user experience. READ: Learn how to overcome online identity verification challenges. Why it's important for financial institutions The importance of identity authentication for financial institutions cannot be overstated. With the rise of cyber threats and sophisticated fraud schemes like synthetic identity fraud, robust identity authentication measures are more critical than ever. Enhancing security. Effective authentication significantly enhances the security of financial transactions. By preventing unauthorized access, sensitive information and financial assets are safeguarded. Advanced solutions like multi-factor authentication solutions add extra layers of protection. Building trust with customers. Robust authentication also helps build trust with customers. When users feel confident that their accounts and personal information are secure, they are more likely to engage with the institution and utilize its services. Regulatory compliance. For financial institutions, compliance with regulatory standards is paramount. Many regulations now mandate strong identity authentication measures to protect against fraud and ensure the security of financial transactions. What to look for in an identity authentication solution The ideal solution should offer a balance between security, user experience, and cost-effectiveness. Adaptive authentication solutions use machine learning algorithms to assess the risk level of each transaction. This allows for a dynamic approach to authentication, where additional checks are only required when necessary. Multi-factor authentication (MFA) solutions add an extra layer of security by requiring users to provide multiple forms of identification. This could include something they know (password), something they have (smartphone), and something they are (biometric data). Knowledge-based authentication (KBA) solutions ask users to answer questions based on their personal information. This method is particularly useful for verifying identities during online transactions and account recoveries. Experian’s Knowledge IQSM offers KBA with over 70 credit- and noncredit-based questions to help you authenticate consumers by asking noninvasive questions that can be answered quickly by the true consumer. Comprehensive identity solutions take a holistic approach by integrating various methods and technologies. Experian’s identity solutions offer a range of services, from risk-based authentication to automated identity verification, ensuring comprehensive protection. Importance of user experience. While security is paramount, user experience should not be overlooked. The ideal identity authentication solution should be seamless and user-friendly, minimizing friction during the authentication process. READ: By adopting a consumer-centric approach to digital identity, organizations can offer customers a better experience while minimizing risk. How Experian can help Identity authentication is a critical component of modern financial institutions. By implementing robust and user-friendly solutions, organizations can enhance security, build customer trust, and comply with regulatory standards. Whether it's through adaptive authentication, multi-factor authentication, or knowledge-based authentication, the goal is to create a secure and seamless experience for users. Ready to take your identity strategy to the next level? Explore Experian’s identity solutions today and discover how they can help your institution achieve its security and user experience goals. Learn more This article includes content created by an AI language model and is intended to provide general information.

Rising balances and delinquency rates are causing lenders to proactively minimize credit risk through pre-delinquency treatments. However, the success of these types of account management strategies depends on timely and predictive data. Credit attributes summarize credit data into specific characteristics or variables to provide a more granular view of a consumer’s behavior. Credit attributes give context about a consumer’s behavior at a specific point in time, such as their current revolving credit utilization ratio or their total available credit. Trended credit attributes analyze credit history data for consumer behavior patterns over time, including changes in utilization rates or how often a balance exceeded an account’s credit limit during the previous 12 months. In a recent analysis, we found that credit attributes related to utilization were highly predictive of future delinquencies in bankcard accounts, with many lenders better managing their credit risk when incorporating these attributes into their account management processes. READ: Find out how custom attributes and models can help you stay ahead of your competitors in the "Build a profitable portfolio with credit attributes" e-book. Using attributes to manage credit risk An enhanced understanding of credit attributes can be leveraged to manage risk throughout the customer lifecycle. They can be important when you want to: Improve credit strategies and efficiencies: Overlay attributes and incorporate them into credit policy rules, such as knockout criteria, to expand your lending population and increase automation without taking on more credit risk. Better understand customers' credit trends: Experian’s wide range of credit data, including trended credit attributes, can help you quickly understand how consumers are faring off-book for visibility into other lending relationships and if they’ll likely experience financial stress in the future. Credit attributes can also help precisely segment populations. For example, attributes can help you distinguish between two people who have similar credit risk scores — but very different trajectories — and will better determine who's the least risky customer. Predicting 60+ day delinquencies with credit attributes To evaluate the effectiveness of credit attributes during account review, we looked at 2.9 million open and active bankcard accounts to see which attributes best predicted the likelihood of an account reaching 60 days past due. For this analysis, we used snapshots of bankcard accounts that were reported in October 2022 and April 2023. Additionally, we analyzed the predictive power of over 4,000 attributes from Experian Premier AttributesSM and Trended 3DTM. Key findings Nine of the top 20 most predictive credit attributes were related to credit utilization rates. Delinquency-related attributes were predictive but weren’t part of the top 10. Three of the top 10 attributes were related to available credit. Turning insight into action While we analyzed credit attributes for account review, determining attribute effectiveness for other use cases will depend on your own portfolio and goals. However, you can use a similar approach to finding the predictive power of attributes. Once you identify the most predictive credit attributes for your population, you can also create an account review program to track these metrics, such as changes in utilization rates or available credit balances. Using Experian’s Risk and Retention Triggers℠ can immediately notify you of customers' daily credit activity to monitor those changes. Ongoing monitoring of attributes and triggers can help you identify customers who are facing financial stress and are headed toward delinquency. You can then proactively take steps to reduce your risk exposure, prioritize accounts, and modify pre-collections strategy based on triggering events. Experian offers credit attributes and the tools to use them Creating and managing credit attributes can be a complex and never-ending task. You need to regularly monitor attributes for performance drift and to address changing regulatory requirements. You may also want to develop new attributes based on expanding data sources and industry trends. Many organizations don’t have the resources to create, manage, and update credit attributes on their own. That’s where Experian’s 4,500+ attributes and tools can help to save time and money. Premier Attributes includes our core attributes and subsets for over 50 industries. Trended 3D attributes can help you better understand changes in consumer behavior and creditworthiness. Clear View AttributesTM offers insights from expanded FCRA data* that generally isn’t reported to consumer credit bureaus. You can easily review and manage your portfolios with Experian’s Ascend Quest™ platform. The always-on access allows you to request thousands of data elements, including credit attributes, risk scores, income models, segmentation data, and payment history, at any time. Use insights from the data and leverage Ascend Quest to quickly identify accounts that may be experiencing financial stress to limit your credit risk — and target others with retention and up-selling opportunities. Watch the Ascend Quest demo to see it in action, or contact us to learn more about Experian’s credit attributes and account review solutions. Watch demo Contact us