Credit Lending

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

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.

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.

Many organizations remain committed to financial inclusion to create better outcomes for underrepresented consumers and small businesses by unlocking barriers to financial well-being and closing the wealth gap. Organizations like credit unions, Community Development Financial Institutions (CDFIs), and Minority Deposit Institutions (MDIs) live by these values. These lenders work hard to ensure these values are reflected in the products and services they offer and in how they attract and interact with customers. While funding from the federal government is being scaled back for many of these community-based financial institutions, Experian is scaling up! We're still here to support CDFIs, Credit Unions, and their members, along their financial inclusion journey. The cross-walk between DEIB and financial inclusion Although Diversity, Equity, Inclusion and Belonging (DEIB) and financial inclusion involve different strategies, there’s an undeniable connection that should ultimately be tied to a business’s overall goal and mission. The communities that are historically underrepresented and underpaid in the workforce – including Black Americans, Hispanic/Latinos, and rural white Americans – also tend to be marginalized by the established financial system. Financial institutions that work to address the inequities within their organizations and promote financial inclusion may find that these efforts complement each other. DEIB policies help promote and support individuals and groups regardless of their backgrounds or differences. While financial inclusion is less specific to a company or organization, instead it describes the strategic approach and efforts that allow people to affordably and readily access financial products, services, and systems. The impact of financial inclusion Lenders can promote financial inclusion in different ways. A bank can change the requirements or fees for one of its accounts to better align with the needs of people who are currently unbanked. Or it can offer a solution to help people who are credit invisible, or unscoreable by conventional credit scoring models, establish their credit files for the first time. Financial institutions also use non-traditional data scoring to lend to applicants that conventional scoring models can’t score. By incorporating alternative credit data[1] (also known as expanded FCRA-regulated data) into their marketing and underwriting, lenders can expand their lending universe without taking on additional risk. Financial inclusion efforts for all Experian is a champion of financial inclusion by supporting both financial institutions and consumers. Through our Inclusion Forward – Experian Empowering Opportunities™ initiative, we work directly with lenders to reach underserved communities and extend greater credit access to consumers. We also offer various tools to help consumers build and understand their credit, and to help financial institutions reach underrepresented communities. We provide individuals with everything from financial inclusion solutions to literacy education to insights about their own financial profile, along with ways to help underrepresented communities improve their financial wellness.* One way that we are doing this is through our consumer programs called Experian Go® and Experian Boost® –that are available for free through the Experian app. These first-of-their-kind programs work together to help consumers improve their credit profile. Experian Go helps individuals establish a credit file, while Experian Boost assists with adding tradelines to an existing credit file. For example, with Experian Boost, individuals can connect positive payments to utility, rent, streaming services, and other accounts to improve their credit scores. Membership with Experian helps consumers monitor their credit, manage their money, and find ways to save money, including shopping for insurance. In fact, consumers saved an average of $828 per year when they switched and saved through Experian Insurance Marketplace.[2] Working together to create financial empowerment There’s no magic solution to undoing the decades of policies and prejudices that have kept certain communities unable to fully access our financial and credit systems. But financial institutions like credit unions, CDFIs and MDIs take steps every day to drive financial inclusion and help underrepresented communities. These values are a part of their business DNA, and Experian is here to help keep their legacy alive. Whether you’ve established your strategy or need help with implementation, we can help you enhance your financial inclusion efforts. Learn more about our helpful solutions. Experian will point you in the right direction to business growth. Visit our website [1] Using Alternative Credit Data for Credit Underwriting. [2] Experian research. *Experian Boost: Results will vary. Not all payments are boost-eligible. Some users may not receive an improved score or approval odds. Not all lenders use Experian credit files, and not all lenders use scores impacted by Experian Boost. Learn more.

The days of managing credit risk, fraud prevention, and compliance in silos are over. As fraud threats evolve, regulatory scrutiny increases, and economic uncertainty persists, businesses need a more unified risk strategy to stay ahead. Our latest e-book, Navigating the intersection of credit, fraud, and compliance, explores why 94% of forward-looking companies expect credit, fraud, and compliance to converge within the next three years — and what that means for your business.1 Key insights include: The line between fraud and credit risk is blurring. Many organizations classify first-party fraud losses as credit losses, distorting the true risk picture. Fear of fraud is costing businesses growth. 68% of organizations say they’re denying too many good customers due to fraud concerns. A unified approach is the future. Integrating risk decisioning across credit, fraud, and compliance leads to stronger fraud detection, smarter credit risk assessments, and improved compliance. Read the full e-book to explore how an integrated risk approach can protect your business and fuel growth. Download e-book 1Research conducted by InsightAvenue on behalf of Experian

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

We are squarely in the holiday shopping season. From the flurry of promotional emails to the endless shopping lists, there are many to-dos and even more opportunities for financial institutions at this time of year. The holiday shopping season is not just a peak period for consumer spending; it’s also a critical time for financial institutions to strategize, innovate, and drive value. According to the National Retail Federation, U.S. holiday retail sales are projected to approach $1 trillion in 2024, , and with an ever-evolving consumer behavior landscape, financial institutions need actionable strategies to stand out, secure loyalty, and drive growth during this period of heightened spending. Download our playbook: "How to prepare for the Holiday Shopping Season" Here’s how financial institutions can capitalize on the holiday shopping season, including key insights, actionable strategies, and data-backed trends. 1. Understand the holiday shopping landscape Key stats to consider: U.S. consumers spent $210 billion online during the 2022 holiday season, according to Adobe Analytics, marking a 3.5% increase from 2021. Experian data reveals that 31% of all holiday purchases in 2022 occurred in October, highlighting the extended shopping season. Cyber Week accounted for just 8% of total holiday spending, according to Experian’s Holiday Spending Trends and Insights Report, emphasizing the importance of a broad, season-long strategy. What this means for financial institutions: Timing is crucial. Your campaigns are already underway if you get an early start, and it’s critical to sustain them through December. Focus beyond Cyber Week. Develop long-term engagement strategies to capture spending throughout the season. 2. Leverage Gen Z’s growing spending power With an estimated $360 billion in disposable income, according to Bloomberg, Gen Z is a powerful force in the holiday market​. This generation values personalized, seamless experiences and is highly active online. Strategies to capture Gen Z: Offer digital-first solutions that enhance the holiday shopping journey, such as interactive portals or AI-powered customer support. Provide loyalty incentives tailored to this demographic, like cash-back rewards or exclusive access to services. Learn more about Gen Z in our State of Gen Z Report. To learn more about all generations' projected consumer spending, read new insights from Experian here, including 45% of Gen X and 52% of Boomers expect their spending to remain consistent with last year. 3. Optimize pre-holiday strategies Portfolio Review: Assess consumer behavior trends and adjust risk models to align with changing economic conditions. Identify opportunities to engage dormant accounts or offer tailored credit lines to existing customers. Actionable tactics: Expand offerings. Position your products and services with promotional campaigns targeting high-value segments. Personalize experiences. Use advanced analytics to segment clients and craft offers that resonate with their holiday needs or anticipate their possible post-holiday needs. 4. Ensure top-of-mind awareness During the holiday shopping season, competition to be the “top of wallet” is fierce. Experian’s data shows that 58% of high spenders shop evenly across the season, while 31% of average spenders do most of their shopping in December​. Strategies for success: Early engagement: Launch educational campaigns to empower credit education and identity protection during this period of increased transactions. Loyalty programs: Offer incentives, such as discounts or rewards, that encourage repeat engagement during the season. Omnichannel presence: Utilize digital, email, and event marketing to maintain visibility across platforms. 5. Combat fraud with multi-layered strategies The holiday shopping season sees an increase in fraud, with card testing being the number one attack vector in the U.S. according to Experian’s 2024 Identity and Fraud Study. Fraudulent activity such as identity theft and synthetic IDs can also escalate​. Fight tomorrow’s fraud today: Identity verification: Use advanced fraud detection tools, like Experian’s Ascend Fraud Sandbox, to validate accounts in real-time. Monitor dormant accounts: Watch these accounts with caution and assess for potential fraud risk. Strengthen cybersecurity: Implement multi-layered strategies, including behavioral analytics and artificial intelligence (AI), to reduce vulnerabilities. 6. Post-holiday follow-up: retain and manage risk Once the holiday rush is over, the focus shifts to managing potential payment stress and fostering long-term relationships. Post-holiday strategies: Debt monitoring: Keep an eye on debt-to-income and debt-to-limit ratios to identify clients at risk of defaulting. Customer support: Offer tailored assistance programs for clients showing signs of financial stress, preserving goodwill and loyalty. Fraud checks: Watch for first-party fraud and unusual return patterns, which can spike in January. 7. Anticipate consumer trends in the New Year The aftermath of the holidays often reveals deeper insights into consumer health: Rising credit balances: January often sees an uptick in outstanding balances, highlighting the need for proactive credit management. Shifts in spending behavior: According to McKinsey, consumers are increasingly cautious post-holiday, favoring savings and value-based spending. What this means for financial institutions: Align with clients’ needs for financial flexibility. The holiday shopping season is a time that demands precise planning and execution. Financial institutions can maximize their impact during this critical period by starting early, leveraging advanced analytics, and maintaining a strong focus on fraud prevention. And remember, success in the holiday season extends beyond December. Building strong relationships and managing risk ensures a smooth transition into the new year, setting the stage for continued growth. Ready to optimize your strategy? Contact us for tailored recommendations during the holiday season and beyond. Download the Holiday Shopping Season Playbook

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.

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

In this article...What is credit card fraud?Types of credit card fraudWhat is credit card fraud prevention and detection?How Experian® can help with card fraud prevention and detection With debit and credit card transactions becoming more prevalent than cash payments in today’s digital-first world, card fraud has become a significant concern for organizations. Widespread usage has created ample opportunities for cybercriminals to engage in credit card fraud. As a result, millions of Americans fall victim to credit card fraud annually, with 52 million cases reported last year alone.1 Preventing and detecting credit card fraud can save organizations from costly losses and protect their customers and reputations. This article provides an overview of credit card fraud detection, focusing on the current trends, types of fraud, and detection and prevention solutions. What is credit card fraud? Credit card fraud involves the unauthorized use of a credit card to obtain goods, services or funds. It's a crime that affects individuals and businesses alike, leading to financial losses and compromised personal information. Understanding the various forms of credit card fraud is essential for developing effective prevention strategies. Types of credit card fraud Understanding the different types of credit card fraud can help in developing targeted prevention strategies. Common types of credit card fraud include: Card not present fraud occurs when the physical card is not present during the transaction, commonly seen in online or over-the-phone purchases. In 2023, card not present fraud was estimated to account for $9.49 billion in losses.2 Account takeover fraud involves fraudsters gaining access to a victim's account to make unauthorized transactions. In 2023, account takeover attacks increased 354% year-over-year, resulting in almost $13 billion in losses.3,4 Card skimming, which is estimated to cost consumers and financial institutions over $1 billion per year, occurs when fraudsters use devices to capture card information from ATMs or point-of-sale terminals.5 Phishing scams trick victims into providing their card information through fake emails, texts or websites. What is credit card fraud prevention and detection? To combat the rise in credit card fraud effectively, organizations must implement credit card fraud prevention strategies that involve a combination of solutions and technologies designed to identify and stop fraudulent activities. Effective fraud prevention solutions can help businesses minimize losses and protect their customers' information. Common credit card fraud prevention and detection methods include: Fraud monitoring systems: Banks and financial institutions employ sophisticated algorithms and artificial intelligence to monitor transactions in real time. These systems analyze spending patterns, locations, transaction amounts, and other variables to detect suspicious activity. EMV chip technology: EMV (Europay, Mastercard, and Visa) chip cards contain embedded microchips that generate unique transaction codes for each purchase. This makes it more difficult for fraudsters to create counterfeit cards. Tokenization: Tokenization replaces sensitive card information with a unique identifier or token. This token can be used for transactions without exposing actual card details, reducing the risk of fraud if data is intercepted. Multifactor authentication (MFA): Adding an extra layer of security beyond the card number and PIN, MFA requires additional verification such as a one-time code sent to a mobile device, knowledge-based authentication or biometric/document confirmation. Transaction alerts: Many banks offer alerts via SMS or email for every credit card transaction. This allows cardholders to spot unauthorized transactions quickly and report them to their bank. Card verification value (CVV): CVV codes, typically three-digit numbers printed on the back of cards (four digits for American Express), are used to verify that the person making an online or telephone purchase physically possesses the card. Machine learning and AI: Advanced algorithms can analyze large datasets to detect unusual patterns that may indicate fraud, such as sudden large transactions or purchases made in different geographic locations within a short time frame. Advanced algorithms can analyze large datasets to detect unusual patterns that may indicate fraud, such as sudden large transactions or purchases made in different geographic locations within a short time frame. Behavioral analytics: Monitoring user behavior to detect anomalies that may indicate fraud. Education and awareness: Educating consumers about phishing scams, identity theft, and safe online shopping practices can help reduce the likelihood of falling victim to credit card fraud. Fraud investigation units: Financial institutions have teams dedicated to investigating suspicious transactions reported by customers. These units work to confirm fraud, mitigate losses, and prevent future incidents. How Experian® can help with card fraud prevention and detection Credit card fraud detection is essential for protecting businesses and customers. By implementing advanced detection technologies, businesses can create a robust defense against fraudsters. Experian® offers advanced fraud management solutions that leverage identity protection, machine learning, and advanced analytics. Partnering with Experian can provide your business with: Comprehensive fraud management solutions: Experian’s fraud management solutions provide a robust suite of tools to prevent, detect and manage fraud risk and identity verification effectively.  Account takeover prevention: Experian uses sophisticated analytics and enhanced decision-making capabilities to help businesses drive successful transactions by monitoring identity and flagging unusual activities. Identifying card not present fraud: Experian offers tools specifically designed to detect and prevent card not present fraud, ensuring secure online transactions.  Take your fraud prevention strategies to the next level with Experian's comprehensive solutions. Explore more about how Experian can help. Learn More Sources 1 https://www.security.org/digital-safety/credit-card-fraud-report/ 2 https://www.emarketer.com/chart/258923/us-total-card-not-present-cnp-fraud-loss-2019-2024-billions-change-of-total-card-payment-fraud-loss 3 https://pages.sift.com/rs/526-PCC-974/images/Sift-2023-Q3-Index-Report_ATO.pdf 4 https://www.aarp.org/money/scams-fraud/info-2024/identity-fraud-report.html 5 https://www.fbi.gov/how-we-can-help-you/scams-and-safety/common-scams-and-crimes/skimming This article includes content created by an AI language model and is intended to provide general information. 

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.

In this article...What is fair lending?Understanding machine learning modelsThe pitfalls: bias and fairness in ML modelsFairness metricsRegulatory frameworks and complianceHow Experian® can help As the financial sector continues to embrace technological innovations, machine learning models are becoming indispensable tools for credit decisioning. These models offer enhanced efficiency and predictive power, but they also introduce new challenges. These challenges particularly concern fairness and bias, as complex machine learning models can be difficult to explain. Understanding how to ensure fair lending practices while leveraging machine learning models is crucial for organizations committed to ethical and compliant operations. What is fair lending? Fair lending is a cornerstone of ethical financial practices, prohibiting discrimination based on race, color, national origin, religion, sex, familial status, age, disability, or public assistance status during the lending process. This principle is enshrined in regulations such as the Equal Credit Opportunity Act (ECOA) and the Fair Housing Act (FHA). Overall, fair lending is essential for promoting economic opportunity, preventing discrimination, and fostering financial inclusion. Key components of fair lending include: Equal treatment: Lenders must treat all applicants fairly and consistently throughout the lending process, regardless of their personal characteristics. This means evaluating applicants based on their creditworthiness and financial qualifications rather than discriminatory factors. Non-discrimination: Lenders are prohibited from discriminating against individuals or businesses on the basis of race, color, religion, national origin, sex, marital status, age, or other protected characteristics. Discriminatory practices include redlining (denying credit to applicants based on their location) and steering (channeling applicants into less favorable loan products based on discriminatory factors). Fair credit practices: Lenders must adhere to fair and transparent credit practices, such as providing clear information about loan terms and conditions, offering reasonable interest rates, and ensuring that borrowers have the ability to repay their loans. Compliance: Financial institutions are required to comply with fair lending laws and regulations, which are enforced by government agencies such as the Consumer Financial Protection Bureau (CFPB) in the United States. Compliance efforts include conducting fair lending risk assessments, monitoring lending practices for potential discrimination, and implementing policies and procedures to prevent unfair treatment. Model governance: Financial institutions should establish robust governance frameworks to oversee the development, implementation and monitoring of lending models and algorithms. This includes ensuring that models are fair, transparent, and free from biases that could lead to discriminatory outcomes. Data integrity and privacy: Lenders must ensure the accuracy, completeness, and integrity of the data used in lending decisions, including traditional credit and alternative credit data. They should also uphold borrowers’ privacy rights and adhere to data protection regulations when collecting, storing, and using personal information. Understanding machine learning models and their application in lending Machine learning in lending has revolutionized how financial institutions assess creditworthiness and manage risk. By analyzing vast amounts of data, machine learning models can identify patterns and trends that traditional methods might overlook, thereby enabling more accurate and efficient lending decisions. However, with these advancements come new challenges, particularly in the realms of model risk management and financial regulatory compliance. The complexity of machine learning models requires rigorous evaluation to ensure fair lending. Let’s explore why. The pitfalls: bias and fairness in machine learning lending models Despite their advantages, machine learning models can inadvertently introduce or perpetuate biases, especially when trained on historical data that reflects past prejudices. One of the primary concerns with machine learning models is their potential lack of transparency, often referred to as the "black box" problem. Model explainability aims to address this by providing clear and understandable explanations of how models make decisions. This transparency is crucial for building trust with consumers and regulators and for ensuring that lending practices are fair and non-discriminatory. Fairness metrics Key metrics used to evaluate fairness in models can include standardized mean difference (SMD), information value (IV), and disparate impact (DI). Each of these metrics offers insights into potential biases but also has limitations. Standardized mean difference (SMD). SMD quantifies the difference between two groups' score averages, divided by the pooled standard deviation. However, this metric may not fully capture the nuances of fairness when used in isolation. Information value (IV). IV compares distributions between control and protected groups across score bins. While useful, IV can sometimes mask deeper biases present in the data. Disparate impact (DI). DI, or the adverse impact ratio (AIR), measures the ratio of approval rates between protected and control classes. Although DI is widely used, it can oversimplify the complex interplay of factors influencing credit decisions. Regulatory frameworks and compliance in fair lending Ensuring compliance with fair lending regulations involves more than just implementing fairness metrics. It requires a comprehensive end-to-end approach, including regular audits, transparent reporting, and continuous monitoring and governance of machine learning models. Financial institutions must be vigilant in aligning their practices with regulatory standards to avoid legal repercussions and maintain ethical standards. Read more: Journey of a machine learning model How Experian® can help By remaining committed to regulatory compliance and fair lending practices, organizations can balance technological advancements with ethical responsibility. Partnering with Experian gives organizations a unique advantage in the rapidly evolving landscape of AI and machine learning in lending. As an industry leader, Experian offers state-of-the-art analytics and machine learning solutions that are designed to drive efficiency and accuracy in lending decisions while ensuring compliance with regulatory standards. Our expertise in model risk management and machine learning model governance empowers lenders to deploy robust and transparent models, mitigating potential biases and aligning with fair lending practices. When it comes to machine learning model explainability, Experian’s clear and proven methodology assesses the relative contribution and level of influence of each variable to the overall score — enabling organizations to demonstrate transparency and fair treatment to auditors, regulators, and customers. Interested in learning more about ensuring fair lending practices in your machine learning models?    Learn More This article includes content created by an AI language model and is intended to provide general information.