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In today’s digital age, call center fraud is a growing threat that businesses can no longer afford to ignore. As fraudsters become increasingly sophisticated, it’s crucial for companies to implement robust security measures to protect both their operations and their consumers. Various forms of call center fraud can have a significant impact on businesses. To prevent this, companies can use effective strategies including multifactor authentication solutions and account takeover prevention techniques. But first, what is call center fraud? Understanding call center fraud Call center fraud occurs when fraudsters exploit vulnerabilities in customer service operations to gain unauthorized access to sensitive information and commit identity theft. This type of fraud can take many forms, including social engineering, which occurs when a fraudster manipulates a call center agent into providing information or access, and phishing, which occurs when fraudsters use deceptive tactics to obtain confidential details from unsuspecting individuals. One of the most concerning tactics used by fraudsters is impersonation, or pretending to be legitimate consumers to gain access to accounts. Once they have access, they can make unauthorized transactions, change account details, or even take over the account entirely—a scenario known as an account takeover. The impact of these fraudulent activities can be devastating, leading to significant financial losses, damage to brand reputation, and a loss of consumer trust. Key strategies for preventing call center fraud According to recent research, account takeover fraud has increased by 330% in the past two years, projecting to cost $6.24 billion globally.[1] In addition, the number of U.S. consumers who have experienced account takeover has increased from 22% in 2021 to 29% in 2023.[2] To effectively combat call center fraud, businesses must adopt a multi-layered approach that includes advanced technological solutions, comprehensive employee training, and real-time monitoring. Here are some of the most effective strategies: 1. Implementing multifactor authentication (MFA) solutions One of the most effective ways to secure consumer interactions is by implementing multifactor authentication (MFA) solutions. MFA requires users to provide two or more verification factors to gain access to an account or complete a transaction. This adds an extra layer of security, making it significantly more difficult for fraudsters to succeed even if they have obtained some of the consumer’s information. MFA can be integrated into call center operations in several ways. For example, businesses can use voice recognition as a biometric factor, requiring consumers to verify their identity through a unique voiceprint. Other methods include sending a one-time code via text message, which the consumer must provide during the call, or using mobile app verification, where consumers approve transactions directly through their smartphones. 2. Account takeover prevention Account takeover is one of the most serious threats to call centers, as they involve fraudsters gaining control of a consumer’s account, often with disastrous consequences. To prevent account takeover, businesses can employ a combination of technological solutions and best practices. First, understanding what account takeover entails is crucial. It typically begins when a fraudster obtains some of the consumer’s personal information—often through phishing, social engineering, or a data breach. They then use this information to impersonate the consumer and convince call center agents to provide them with access to the account. To combat this, businesses can employ several account takeover prevention techniques. Anomaly detection systems can flag unusual activities, such as login attempts from unfamiliar locations or devices, prompting additional verification steps. Behavioral biometrics is another powerful tool, analyzing patterns in how users interact with their devices to detect inconsistencies that may indicate fraud. Continuous authentication, where the system continuously verifies the user’s identity throughout the session, is also effective in catching fraudsters in the act. 3. Training and awareness Technology alone may not be enough to entirely prevent call center fraud—human factors are equally important. Regular training for call center staff is essential to ensure team members can recognize and respond to potential fraud attempts. Employees should be trained to identify common tactics used by fraudsters, such as social engineering, and to follow strict verification procedures before providing any sensitive information. Awareness campaigns can also play a significant role in preventing fraud. Internally, companies should run regular campaigns to remind employees of the importance of adhering to security protocols. Externally, educating consumers about the risks of fraud and encouraging them to use security features like MFA can help reduce the likelihood of successful attacks. 4. Real-time monitoring and analytics Real-time monitoring is a critical component of an effective fraud prevention strategy. By continuously monitoring calls and transactions, businesses can quickly identify and respond to suspicious activities before they escalate. Advanced analytics tools, including voice analytics and behavior analysis, can provide valuable insights into potential fraud, allowing companies to take proactive measures. Voice analytics, for instance, can detect stress or hesitation in a caller’s voice, which may indicate that they are not who they claim to be. Behavior analysis can track how consumers typically interact with their accounts, flagging deviations from the norm as potential fraud. Continuous improvement is key here—regularly reviewing and updating monitoring protocols ensures that businesses stay ahead of evolving threats. Preventing call center fraud in your business By using a multi-layered fraud approach through a variety of authentication solutions, your business can quickly detect call center fraud without disrupting your consumers’ experience. Identify the risk Identity-based risk detection can pinpoint when a specific identity may be in the hands of fraudsters. Device intelligence solutions can recognize the risk associated with a specific device used to attempt online access. Address the risk Knowledge-based authentication (KBA) can quickly authenticate users by asking questions only they can answer, which can deter fraudsters. MFA services can generate and deliver a one-time password to a consumer’s mobile device to verify their identity in real time. Document verification allows your business to collect and verify images of identity documents uploaded from a consumer’s mobile device. Protect your business and your consumers from call center fraud Call center fraud is a significant threat that requires a proactive and comprehensive approach to prevention. By implementing strategies such as multifactor authentication solutions, account takeover prevention techniques, and robust employee training, businesses can significantly reduce their risk of falling victim to fraud. In today’s fast-paced digital world, staying vigilant and proactive is the key to safeguarding your call center against fraud. Act now to protect your business and maintain the trust of your consumers. Enable your call center to detect risk quickly and effectively with our robust fraud prevention solutions. Get started Download our identity and fraud report This article includes content created by an AI language model and is intended to provide general information. [1] Worldmetrics.org, Account Takeover Statistics: Losses to Reach $6.24 Billion Globally, 2024. [2] Security.org, Account Takeover Incidents are Rising: How to Protect Yourself in 2024.

This series will explore our monthly State of the Economy report, which provides a snapshot of the top monthly economic and credit data for financial service professionals to proactively shape their business strategies. During their September meeting, the Federal Reserve made a highly-anticipated announcement to cut rates for the first time since 2020. Fed officials cut rates by 50bps, while also penciling in an additional 50bps of cuts for 2024 and 100bps of cuts in 2025 in their Summary of Economic Projections. While rates are coming down and inflation continues to cool, there were downward revisions to job creation made in August and declining job openings in July. Data highlights from this month’s report include: The Federal Reserve announced a 50bps rate cut during the September meeting. Annual headline inflation cooled from 2.9% to 2.5%, getting closer to the Fed’s 2% goal. Mortgage originations increased 7.0% in August. Check out our report for a deep dive into the rest of this month’s data, including the latest trends in job creation, retail sales, and consumer sentiment. Download September's report As our economy continues to fluctuate, it's critical to stay updated on the latest developments. Subscribe to our new series, The Macro Moment, for economic commentary from Experian North America's Chief Economist, Joseph Mayans, and download our new Lending Conditions Chartbook for additional insights. For more economic trends and market insights, visit Experian Edge.

As a mortgage lender, understanding the intricacies of the New York housing market is crucial, especially when dealing with first-time homebuyers (FTHBs). While the housing market fluctuates nationwide, New York presents unique challenges and opportunities that require a nuanced approach. Distinguishing NYC from the rest of New York New York City's housing market, along with its suburbs, stands distinct from the rest of the state. With a high cost of living and unique lifestyle, NYC demands a tailored mortgage marketing strategy. This article will highlight key factors affecting affordability in New York, providing valuable insights for mortgage lenders working in this market. Overview of the New York FTHB market According to Experian Housing’s recent report on first-time homebuyers, the state of New York accounts for nearly 4.9% of all first-time homebuyers nationwide.1 More than half of first-time homebuyers are from Generation Y. When combined with Gen Z, these younger buyers make up just over 67% of the state's FTHBs, a figure slightly below the national average of 69%. Affordability metrics: The rent-to-mortgage ratio For many Americans, homeownership represents stability, security, and the future for family, community, and life. However, the decision to buy versus rent often hinges on affordability. Mortgage lenders must understand this dynamic to better assist their clients. Affordability can be defined in various ways. For the purposes of this study, Experian Housing defined affordability by comparing rental and mortgage payments, known as the rent-to-mortgage ratio (RTM ratio). A higher ratio indicates that buying a home is more economically attractive. Again, this metric does not consider incomes and debt levels, but simply housing rental prices and mortgage costs. Based solely on the RTM ratio, the transition from renting to homeownership may be more attractive in New York City, Syracuse, and Oyster Bay, while the transition may be more difficult in Cheektowaga, Amherst, and Hempstead. For mortgage lenders, understanding local markets and buyer profiles is essential. Building trust through personalized service, such as educating buyers on relevant loan programs and showcasing geographic expertise, can set you apart. With this knowledge, you can help buyers make informed decisions about affordability, whether they prefer living in the city or the suburbs. In some areas, the suburbs may offer more affordable options, while in others, the city center might be more cost-effective. Additional factors: Income, debt, and down payments Affordability extends beyond just rent and mortgage payments. Prospective homebuyers must consider their income, monthly expenses, and access to funds for a down payment. Mortgage lenders need to account for these factors when advising first-time homebuyers. Debt-to-income Average DTI across the 14 cities observed was 25.6%. The chart below highlights those at the higher and lower end of the spectrum. Down payments Down payments varied greatly, but the median across the cities observed was 16.5%. The chart below highlights an example at the high, mid, and low point. Sale prices and income Experian Housing analyzed median sales prices and incomes across the U.S., with New York serving as a prime example of the importance of this comparison in assessing affordability. This correlation is crucial; while sales prices may be high, understanding how they align with local incomes helps lenders accurately gauge market dynamics and guide buyers more effectively. In conclusion, having a deep understanding of the New York housing market is invaluable for mortgage lenders aiming to support first-time homebuyers. By leveraging insights into market dynamics, affordability metrics, and borrower profiles, lenders can offer tailored advice that meets the specific needs of their clients. This not only helps buyers navigate the complexities of homeownership but also builds lasting trust and loyalty. Equipped with these insights, you, as a lender, can play a pivotal role in making the dream of homeownership a reality for first-time buyers in New York. Let's continue to empower our clients with the knowledge and guidance they need to make informed and confident decisions in their homebuying journey. For more insights, check out our recent studies on the Florida and Texas markets and download our first-time homebuyer whitepaper. Download white paper Learn more

Effective collection strategies are critical for the financial health of credit unions. Unlike traditional banks, credit unions often emphasize member relationships and community values, making the collection process more tactful. Crafting a strategy that balances the need for financial stability with member-centric values is essential.  Here’s a step-by-step guide on how to create an effective credit union collection strategy. 1. Understand your members The foundation of an effective credit union collection strategy is understanding your members. Credit unions often serve specific communities or groups, and members may face unique financial challenges. By analyzing member demographics, financial behavior, and common reasons for delinquency, you can tailor your approach to be more vigilant and effective. Segment members: Group members based on factors like loan type, payment history, and financial behavior. This allows for targeted communications and outreach strategies. Member communication preferences: Determine how your members prefer to be contacted—whether by phone, email, or in person. This can increase engagement and responsiveness. 2. Prioritize compliance Compliance with regulations is non-negotiable in the collections process. Ensure that your strategy adheres to all relevant laws and guidelines. Fair Debt Collection Practices Act (FDCPA): Ensure that your team is fully trained on the FDCPA and that your practices comply with its requirements. State and local regulations: Be aware of any state or local regulations that may impact your collections process. This could include restrictions on contact methods or times. Internal audits: Regularly conduct internal audits to ensure compliance and identify any areas of risk. 3. Leverage technology for efficiency Technology can streamline the collection process, making it more efficient and a better member experience. Automated reminders: Use automated systems to send reminders before and after payment due dates. This reduces the likelihood of missed payments due to forgetfulness. Data analytics: Use data analytics to identify trends in member behavior, establish a collections prioritization strategy, and predict potential delinquencies. This allows your team to be proactive rather than reactive. Digital communication channels: Implement digital communication options, such as text messages or chatbots to make it easier for members to interact with the credit union. 4. Establish clear communication protocols Early and frequent communication is key to preventing delinquency and managing it when it occurs. Create clear protocols for member communication that prioritize empathy and treatment plans over demands. Early intervention: Reach out to members as soon as they miss a payment. Early intervention can prevent minor issues from escalating. Consistent communication: Ensure that your communication is consistent across all channels. Whether a member receives a call, an email, or a letter, the message should be clear and aligned with the credit union’s values. Human understanding: Train your collections team to use a compassionate tone. Members are more likely to respond positively when they feel understood and respected. 5. Offer flexible payment solutions Flexibility is crucial when working with members who are struggling financially. Offering a range of payment solutions can help members stay on track and reduce the likelihood of default. Customized treatment plans: Offer customizable payment plans that fit the member’s financial situation. This could include lower payments over a longer term or temporary payment deferrals. Loan modifications: In some cases, modifying the terms of the loan—such as extending the repayment period or lowering the interest rate—may be necessary to help the member succeed. Debt consolidation options: If a member has multiple loans, consider offering debt consolidation to simplify their payments and reduce their overall financial burden. 6. Train your collection team Your collection team is the frontline of your strategy. Providing them with the right training and tools is essential for success. Ongoing training: Regularly update your team on the latest regulations, best practices, and communication techniques. This keeps them informed and prepared to handle various situations. Better decision making: Empower your team to make decisions that align with the credit union’s values. This could include offering payment extensions or waiving late fees in certain situations. Regular support: Working in collections can be complex. Provide resources and support to help your team manage stress and maintain a positive attitude. 7. Monitor and adjust your strategy A successful credit union collection strategy is dynamic. Regularly monitor its performance and adjust as needed. Key performance indicators (KPIs): Track KPIs such as delinquency rates, recovery rates, roll-rates and member satisfaction to gauge the effectiveness of your strategy. Member feedback: Survey members who have gone through the collections process. Their insights can help you identify areas for improvement. Continuous improvement: Use data and feedback to continuously refine your strategy. What worked last year may not be as effective today, so staying adaptable is key. Creating an effective credit union collections strategy requires a balance of empathy, effective communication, and compliance. By understanding your members, communicating clearly, offering flexible solutions, leveraging technology, and continuously improving your approach, you can develop a strategy that not only reduces delinquency but also strengthens member relationships. In today’s fiercely competitive landscape, where efficiency and efficacy stand paramount, working with the right partner equipped with innovative credit union solutions can dramatically transform your outcomes. Choosing us for your debt collection needs signifies an investment in premier analytics, advanced debt recovery tools, and unmatched support.  Learn more Watch credit union collection chat This article includes content created by an AI language model and is intended to provide general information.

Fraud-as-a-Service (FaaS) represents an emerging and increasingly sophisticated business model within cybercrime. In this model, malicious actors commercialize their expertise, tools, and infrastructure, enabling others to perpetrate fraud more easily and efficiently. These FaaS offerings are often accessible via dark web marketplaces or underground forums, streamlining and automating fraud processes, such as large-scale phishing campaigns. This enables the creation of convincing counterfeit websites and the distribution of bulk emails, allowing cybercriminals to harvest credentials and personal information en masse.   Organized cybercrime syndicates leverage account creation bots to establish hundreds of fraudulent accounts across various platforms, bypassing standard security protocols and scaling their illicit activities seamlessly. A fraudster no longer requires deep technical skills or detailed knowledge of complex verification techniques, such as liveness detection. Instead, they can acquire turnkey FaaS solutions that, for instance, inject pre-recorded video footage to spoof verification processes, enabling the rapid creation of thousands of fraudulent accounts.  The commoditization of fraud has effectively democratized it, lowering the barriers to entry. Previously accessible to a select few, FaaS has developed sophisticated techniques and is now available to a broader and less technically adept audience. Now, even individuals with basic computer skills can access these services and initiate fraudulent schemes with minimal effort.   Key tools in the FaaS arsenal  Central to the success of fraud-as-a-service is the ability to create fraudulent accounts while evading detection. This process can be alarmingly straightforward, even for companies adhering to industry-recognized best practices. Widely available programs, such as app cloners, enable fraudsters to generate multiple instances of the same application on a single device, modifying its source code to bypass security measures to detect such activities. The generalization of artifical intelligence (AI) and increased access to technology have provided cybercriminals with new tools to launch sophisticated scams, such as Pig Butchering and Authorized Push Payment (APP) scams.   Similarly, image injection tools facilitate the insertion of manipulated images to deceive verification systems, while emulators simulate legitimate device activity at scale, making detection more challenging. Techniques such as location spoofing allow fraudsters to alter the perceived geographical location of a device, thereby evading location-based security checks and allowing their scams to remain undetected.  Once fraudulent accounts are established, cybercriminals focus on monetizing their efforts. Industries like food delivery and ride-hailing are particularly vulnerable to promotional abuse. Fraudsters exploit promotional offers intended for new customers by using cloned apps, injected images, and emulators to create multiple fake accounts, redeem discounts, and resell them for profit. AI-driven automation and advanced communication technologies lower the barriers for these scams, enabling criminals to operate at a larger scale and with greater efficiency. This has made scams more pervasive and difficult for individuals and institutions to detect.  In the ride-hailing industry, these tactics are used to manipulate fare structures and incentives. Fraudsters operate multiple driver or rider accounts on the same device to earn referral bonuses and other promotional rewards. Emulators can simulate rides with fabricated start and end points, while location spoofing tools manipulate GPS data, inflating fares, and earnings. Such fraudulent activities result in significant financial losses for companies and degrade service quality for legitimate users, as resources are diverted from genuine transactions and logistical algorithms are disrupted.  The implications of FaaS for businesses  The commercialization of fraud poses a substantial threat to businesses, not only by democratizing fraud but also by enabling it to rapidly scale. . Fraudsters can experiment with multiple schemes simultaneously, sharing feedback and accelerating their learning curve. A single tool developed by one individual can be deployed by numerous bad actors to perpetrate fraud on a large scale, with remarkable speed. This ease of execution allows fraudsters to overwhelm companies with a barrage of attacks, maximizing their financial gains while exacerbating the challenges of fraud prevention for targeted organizations.  Developing a FaaS-Resilient fraud prevention strategy  To effectively combat fraud-as-a-service, businesses must adopt AI fraud strategies that mirror the operational sophistication of fraudsters. These cybercriminals treat their activities as profitable enterprises, continually optimizing their return on investment through scalable and adaptable tactics. By deeply understanding the methodologies employed by fraudsters, companies can develop more effective fraud prevention measures that disrupt fraudulent operations without inconveniencing legitimate users.  Proactive fraud prevention strategies are essential in countering FaaS tactics. Effective measures rely on robust data collection and analysis. Regular reviews of key performance indicators (KPIs) and velocity checks, which monitor the rate at which users complete transactions, can help identify irregular behaviors.  Passive signals, such as device fingerprinting and location intelligence, are also invaluable in detecting suspicious activities. By scrutinizing data related to app tampering or device emulation, businesses can more accurately determine whether a genuine user is accessing their platform or if a fraudster is attempting to bypass detection.  Given the dynamic nature of FaaS, adaptation is crucial. Fraud prevention strategies must evolve continually to keep pace with emerging threats. Advanced technologies offer nuanced insights into user behavior, enabling businesses to identify and thwart fraud attempts with greater precision. Moreover, cutting-edge risk monitoring tools can help avoid false positives, ensuring that legitimate users are not unduly impacted.  As fraudsters persist in innovating and refining their tactics, organizations must remain vigilant, stay informed about emerging trends, invest in advanced fraud prevention and detection technologies, and cultivate a culture of security and awareness. While it may be tempting to underestimate fraudsters due to the illicit nature of their activities, it is important to recognize that many approach their work with a level of professionalism comparable to legitimate businesses. Understanding this reality offers valuable insights into how companies can effectively counteract fraud and protect their monetary interests.  Learn more This article includes content created by an AI language model and is intended to provide general information.

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. 

Replay attacks may threaten your customers’ online security Today, consumer online security is more important than ever. This year, the FTC has already received nearly six million reports of fraud, and 1.4 million of those cases were specifically identity theft.[1] In addition, a recent study reported that losses due to identity fraud amounted to almost $23 billion in 2023.[2] And consumers aren’t the only ones at risk. According to CyberArk’s global research report, 93% of organizations had two or more identity-related breaches in the past year.[3] This means it’s not only up to consumers to protect themselves against identity theft. It’s also up to businesses to protect themselves and their customers from the threat of fraud. As security technology advances, so do the tactics of hackers attempting to steal information such as usernames, account numbers, and passwords from innocent online users. One method that hackers use to obtain this information is called a replay attack, which can pose a serious threat to your customers’ online security. What is a replay attack? A replay attack is a network-based security hack in which a hacker intercepts legitimate data transmission and then fraudulently repeats it to gain access to a network or system. These attacks are designed to fool the victim into believing the hacker is a genuine user, and they happen in three steps: Eavesdropping: The hacker listens in on secure network communications, such as information sent through a Virtual Private Network (VPN), to learn about the activity happening on that network. Interception: The hacker intercepts legitimate user information – usernames, user activity, computer specs, passwords, etc. Replay: The hacker illegally resends (or “replays”) the valid information they gathered to trick the receiver into thinking that they are a genuine user. Here’s an example: John transfers funds from one online banking account to another. A hacker illegally captures that transaction message (which is often accompanied by a digital signature or token) and “replays” that same transaction message multiple times to trigger additional fund transfers, all without the genuine user’s knowledge or permission. The bank doesn’t recognize a problem because the “replayed” transaction messages includes the legitimate digital signature/token, so the bank approves the additional transfers. Replay attacks aren’t just used for banking transactions. They can be used for various activities, such as: Internet of Things (IoT) device attacks: IoT devices include a multitude of “smart home” devices such as smart plugs, cameras, locks, appliances, speakers, lights, and more. Vulnerabilities in these devices can allow hackers to replicate commands to these devices that seem legitimate, such as turning on cameras, unlocking doors, and disabling security systems.[4] Remote keyless entry systems for vehicles: Most vehicles use a remote key fob to lock and unlock the doors. This key fob usually uses radio waves to send the lock/unlock signal to the car. Hackers can use a device to receive and transmit radio waves near a person’s vehicle that mimic that same lock/unlock signal, and then “replay” that signal to unlock the person’s car themselves.[5] Text-dependent speaker verification: Some people use voice recognition to verify their identity when accessing an account or system. Hackers can record a person’s voice when the person speaks to verify their identity, and then “replay” that voice recording to fraudulently access the account.[6] How to prevent replay attacks Replay attacks are dangerous because they are often unnoticed or overlooked until the damage has already been done. Fortunately, there are ways to stop hackers from using replay attacks to access your customers’ personal information. Device intelligence: By leveraging unique intelligence about the device being used, replay attacks can be thwarted even when fraudsters are using authentic, but stolen, information. Time stamping: By forcing a timestamp on all sent and received messages, you can prevent hackers from sending repeated messages with legitimate information obtained illegally. Geolocation review: By identifying suspicious language and/or time zones, you can compare access routes to confirm customers are authentic and secure. Why it matters for your business Consumers in the U.S. value network security more than ever, with 70% rating security a top priority, even over personalization and convenience.[7] People want to feel safe online, and if they experience a threat of identity theft or fraud, they’ll need to find a reliable resource to keep their personal information secure. Successful replay attacks allow fraudsters to impersonate real users and potentially gain partial or full access to their personal online accounts. If your customers fall victim to these kinds of attacks, the resulting stress may have a negative impact on your relationship with them. With our fraud management solutions, your business can strengthen your customers’ trust and security by leveraging highly trained fraud analysts to help uncover suspicious activity that might not be noticed otherwise. Lower fraud losses and achieve fraud capture rates that exceed industry averages. Protect your customers by using a covert, frictionless solution the reduces false positives. Improve operational efficiency by prioritizing resources across the board. Protect your consumers with powerful fraud management solutions 63% of consumers say it’s important for businesses to be able to recognize them online, and 81% say they are more trusting of businesses that can accomplish easy and accurate identification.[8] While replay attacks can cause consumers stress and anxiety, taking action to prevent them can fortify a strong, trusting relationship between your business and your customers. Protect your customers and prevent replay attacks with our powerful fraud management solutions. Get started [1] IdentityTheft.org, 2024 Identity Theft Facts and Statistics. [2] Javelin, 2024 Identity Fraud Study: Resolving the Shattered Identity Crisis. [3] CyberArk, Report: 93% of Organizations Had Two or More Identity-Related Breaches in the Past Year, May 2024. [4] Hackster.io, IoT Devices May Be Susceptible to Replay Attacks with a Raspberry Pi and RTL-SDR Dongle, 2017. [5] Automotive World, How to mitigate vulnerabilities in keyless entry systems, 2023. [6] Antispoofing, Audio Replay Attacks and Countermeasures Against Them, 2022. [7] 2018 Experian® Global Fraud Report [8] Experian® 2024 Identity and Fraud Report Highlights Evolving Fraud Landscape 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

Housing affordability is a pressing concern across the United States, and Florida is no exception. The affordability issue can be particularly crucial for renters looking to become first-time homebuyers (FTHBs). The desire to live in a sunny location must be measured against the cost of living, particularly housing costs. Experts at Experian Housing carefully examined data from the top 15 Florida cities (by population) to gain insights into the state of housing affordability in Florida.1 Experian examined factors such as mortgage payments, rent prices, income levels, and sales prices to assess affordability. Overview of the Florida FTHB market Experian Housing’s recent report on first-time homebuyers ranked Florida the state with the third highest percentage of FTHBs nationwide, at nearly 7.7% of FTHBs.2 It outranked New York, falling behind Texas and California. In Florida, the younger populations of Generation Y and Z account for 60% of all first-time homebuyers. Nationwide, roughly 70% of FTHBs belong to these populations. Among younger buyers, affordability is often the deciding factor in whether they continue to rent or become homeowners as they balance housing costs with student loan debt and other expenses. Let’s look at some key metrics Comparative monthly mortgage payments and rent prices How this affects affordability: The bottom line for prospective homebuyers often comes down to whether it's more affordable to continue to rent or purchase a home. While the metrics discussed all contribute to the picture of affordability, for this study, Experian Housing defined affordability by calculating the rent-to-mortgage ratio, a comparison of monthly rental payments to monthly mortgage payments. Homebuying becomes more attractive to renters when the rent-to-mortgage ratio is higher because mortgage payments are more economically practical. What we observed: Experian Housing found that Pembroke Pines, Palm Bay, and Cape Coral have the highest rent-to-mortgage ratio in Florida, at nearly 80%. In other words, for example, if the average mortgage payment is $1,000, the average rental payment is ~$800. Compare this to Tallahassee, Hialeah, and Hollywood, where the rent-to-mortgage ratio is <60%. These numbers illustrate the varying home purchase and rental market trends across the state. Debt-to-income How this affects affordability: This metric compares monthly debt responsibilities, including mortgages, car loans, student loans, and minimum credit card payments, to monthly income. A high debt-to-income ratio indicates a significant portion of income is dedicated to paying debt, leaving little room for other essential living costs and discretionary spending. What we observed: Down payments How this affects affordability: A higher down payment can also assist buyers, especially first-time buyers, by increasing attractive financing options. Importantly, a down payment of 20% avoids the need for private mortgage insurance (PMI), which is insurance for the lender, protecting the lender against loss should a foreclosure occur. PMI typically costs between 0.5% and 2% of the loan amount, annually. What we observed: Sale prices and financial hurdles How this affects affordability: In comparing home affordability across Florida, first-time homebuyers should consider home prices in relation to income. While other considerations, including an individual’s debt level and other expenses, contribute to the bottom line, this gives an indication of how much income will be consumed by the home purchase. What we observed: Experian Housing examined the median sales prices and median. Comparison is essential because sales prices may be higher in a given area, but correlation with income helps determine affordability. A Florida housing opportunity, up close: Miami metropolitan area The Miami metropolitan area is an example of an area where mortgage lenders who understand their clients and the area they seek to live may well attract first-time homebuyers, loyal clients, and more word-of-mouth business. The Miami suburb of Pembroke Pines, roughly 20 miles from Miami, offers a more affordable housing market. With Florida sunshine, nearby beaches, and access to three main highways, mortgage lenders whose knowledge base is not limited to the Miami city center may have an opportunity to turn a renter into a homeowner. Florida residents navigate the cost of living in the Sunshine State Analysis from Experian Housing highlights the diversity in housing markets and the opportunities to enhance financial well-being for residents in Florida. These insights are crucial for lenders to identify affordable opportunities for all residents. Experian’s data system offers unique value to lenders given the ability to take a more comprehensive look at a borrower’s financial behavior. Experian uses credit, property, rental, and other alternative data sources to capture the borrower profile. Access to such data also gives Experian a unique ability to conduct research for reports like this one and the recent Texas affordability study. For more information about the lending possibilities for first-time homebuyers, download our white paper and visit us online. Download white paper Learn more 1 The analysis is based on the trade and rental data reported to Experian and considered first-time homebuyers during the period between November 2022 and January 2024. 2 Based on those getting a mortgage.

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.

Experian’s ninth annual report on identity and fraud highlights persistent worries among consumers and businesses about fraud, including growing threats from GenAI. In this report, we explore how the evolving fraud landscape is impacting identity verification, customer experience, and business priorities for the future. Our 2024 U.S. Identity and Fraud Report draws insights from surveys of over 2,000 U.S. consumers and 200 businesses. This year’s report dives into: Evolving consumer sentiment over security and experience Businesses’ investments to tackle growing fraud challenges Effective technology solutions to accurately identify and authenticate consumers The impact of GenAI on the fraud landscape   To keep pace with the evolving landscape, businesses will need to apply a multi-faceted strategy that leverages multiple types of recognition and security to stop all types of fraud while allowing real customers through. To learn more about our findings and perspective, read the full 2024 U.S. Identity and Fraud Report, watch our on-demand webinar, or read the press release. Download Now Watch Webinar Read Press Release

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...Rise of AI in fraudulent activitiesFighting AI with AI Addressing fraud threatsBenefits of leveraging AI fraud detectionFinancial services use caseExperian's AI fraud detection solutions In a world where technology evolves at lightning speed, fraudsters are becoming more sophisticated in their methods, leveraging advancements in artificial intelligence (AI). According to our 2024 U.S. Identity and Fraud Report, 70% of businesses expect AI fraud to be their second-greatest challenge over the next two to three years. To combat emerging fraud threats, organizations are turning to AI fraud detection to stay ahead and protect their businesses and their customers, essentially fighting AI with AI. This blog post explores the evolving AI fraud and AI fraud detection landscape. The rise of AI in fraudulent activities Technology is a double-edged sword. While it brings numerous advancements, it also provides fraudsters with new tools to exploit. AI is no exception. Here are some ways fraudsters are utilizing AI: Automated attacks: Fraudsters employ AI to design automated scripts that launch large-scale attacks on systems. These scripts can perform credential stuffing, where stolen usernames and passwords are automatically tested across multiple sites to gain unauthorized access. Deepfakes and synthetic identities: Deepfake technology and the creation of synthetic identities are becoming more prevalent, as we predicted in our 2024 Future of Fraud Forecast. Fraudsters use AI to manipulate videos and audio, making it possible to impersonate individuals convincingly. Similarly, synthetic identities blend real and fake information to create false personas. Phishing and social engineering: AI-driven phishing attacks are more personalized and convincing than traditional methods. By analyzing social media profiles and other online data, fraudsters craft tailored messages that trick individuals into revealing sensitive information. Watch now: Our 2024 Future of Fraud Forecast: Gen AI and Emerging Trends webinar explores five of our fraud predictions for the year. Fighting AI with AI in fraud detection To combat these sophisticated threats, businesses must adopt equally advanced measures. AI fraud detection offers a robust solution: Machine learning algorithms: Fraud detection machine learning algorithms analyze vast datasets to identify patterns and anomalies that indicate fraudulent behavior. These algorithms can continuously learn and adapt, improving their accuracy over time. Real-time monitoring: AI systems provide real-time monitoring of transactions and activities. This allows businesses to detect and respond to fraud attempts instantly, minimizing potential damage. Predictive analytics: Predictive analytics uses historical data to forecast future fraud trends. By anticipating potential threats, organizations can take proactive measures to safeguard their assets. Addressing fraud threats with AI fraud detection AI's versatility allows it to tackle various types of fraud effectively: Identity theft: 84% of consumers rank identity theft as their top online concern.* AI systems can help safeguard consumers by cross-referencing multiple data points to verify identities. They can spot inconsistencies that indicate identity theft, such as mismatched addresses or unusual login locations. Payment fraud: Coming in second to identity theft, 80% of consumers rank stolen credit card information as their top online concern.* Payment fraud includes unauthorized credit card transactions and chargebacks. AI can be used in payment fraud detection to surface unusual spending patterns and flag suspicious transactions for further investigation. Account takeover: Account takeover fraud, the topmost encountered fraud event reported by U.S. businesses in 2023, occurs when fraudsters gain access to user accounts and conduct unauthorized activities.* AI identifies unusual login behaviors and implements additional security measures to prevent account breaches. Synthetic identity fraud: Synthetic identity fraud involves the creation of fake identities using real and fabricated information. Notably, retail banks cite synthetic identity fraud as the operational challenge putting the most stress on their business.* AI fraud solutions detect these false identities by analyzing data inconsistencies and behavioral patterns. Benefits of leveraging AI fraud detection Implementing AI fraud detection offers numerous advantages: Enhanced accuracy: AI systems are highly accurate in identifying fraudulent activities. Their ability to analyze large datasets and detect subtle anomalies surpasses traditional methods. Cost savings: By preventing fraud losses, AI systems save businesses significant amounts of money. They also reduce the need for manual investigations, freeing up resources for other tasks. Improved customer experience: AI fraud detection minimizes false positives, ensuring genuine customers face minimal friction. This enhances the overall customer experience and builds trust in the organization. Scalability: AI systems can handle large volumes of data, making them suitable for organizations of all sizes. Whether you're a small business or a large enterprise, AI can scale to meet your needs. Financial services use case The financial sector is particularly vulnerable to fraud, making AI an invaluable tool for fraud detection in banking. Protecting transactions: Banks use AI to monitor transactions for signs of fraud. Machine learning algorithms analyze transaction data in real time, flagging suspicious activities for further review. Enhancing security: AI enhances security by implementing multifactor authentication and behavioral analytics. These measures make it more challenging for fraudsters to gain unauthorized access. Reducing fraud losses: By detecting and preventing fraudulent activities, AI helps banks reduce their fraud losses throughout the customer lifecycle. This not only saves money but also protects the institution's reputation. Experian's AI fraud detection solutions AI fraud detection is revolutionizing the way organizations combat fraud. Its ability to analyze vast amounts of data, detect anomalies, and adapt to new threats makes it an essential element of any comprehensive fraud strategy. Experian’s range of AI fraud detection solutions help organizations enhance their security measures, reduce fraud losses, authenticate identity with confidence, and improve the overall customer experience. If you're interested in learning more about how AI can protect your business, explore our fraud management solutions or contact us today. Learn More *Source: Experian. 2024 U.S. Identity and Fraud Report. This article includes content created by an AI language model and is intended to provide general information. 

With the noticeable uptick in delinquencies, credit unions face more significant hurdles in effectively managing overdue accounts. In this challenging financial landscape, it’s imperative that you refine your account management processes to remain competitive, preserve the well-being of your members, assure operational efficiency, and increase profitability.  Implementing efficient collection approaches not only improves loss rates but also helps with member retention, which is the backbone of your success. Grab a cup of coffee and join our experts on August 22 @ 1:00 p.m. ET/ 10:00 a.m. PT, for an engaging conversation on credit union collection trends and successful account management strategies. Highlights include: Current landscape: Gain valuable insight and understanding into the current debt collection environment for credit unions. Navigating challenges: Discover effective tips and strategies to tackle obstacles in your business, improve loss rates, and enhance member retention. Real-time Q&A: Participate in a live Q&A session where our experts will address your questions. Watch on-demand

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