Topics

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

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.

  For car dealers, the holy grail isn't a flashy sports car or a top-selling SUV. It's a simple whisper: "I'm thinking about getting a new car." Imagine if you could hear that murmur from every potential customer walking through your doors, online, or even driving down the street. That's the power of knowing who's in the market for a new car, and it's a game-changer for dealerships. Go beyond the cookie and website tracking and leverage the power of psychographic data and predictive analytics to know who is coming into the market in the next 30, 60, 90 days with the Experian Marketing Engine’s Affinity AutoAudiences. Boost Efficiency and ROI: Targeted Sales: No more shotgun blasts of marketing campaigns! Precisely target consumers considering a new car with personalized offers and incentives. Imagine tailoring financing proposals based on their budget and desired features, not guesswork. Inventory Optimization: Say goodbye to dusty lots filled with unsold models. Knowing market trends and individual preferences allows you to stock in-demand vehicles, maximizing sales and minimizing depreciation costs. Streamlined Sales Process: When a customer walks in already open to buying, the entire process becomes smoother. Focus on addressing their specific needs and preferences, leading to quicker deals and happier customers. Build Stronger Customer Relationships: Proactive Engagement: Instead of waiting for leads, reach out at the perfect moment. A friendly call or email during their research phase demonstrates attentiveness and builds trust, setting you apart from the competition. Personalized Recommendations: Forget one-size-fits-all pitches. Recommend models based on their lifestyle, budget, and driving habits. This shows genuine interest and builds rapport, increasing the likelihood of conversion. Enhanced Customer Experience: Cater to their specific needs before they even step onto the lot. Offer virtual test drives, online financing options, and even home delivery – all tailored to their preferences. This level of personalized service fosters loyalty and repeat business. Leverage the Power of Data: Knowing when a consumer is in the market for a new car isn't just about a head start, it's about building trust, offering convenience, and tailoring the entire experience to their needs. In a competitive market, this inside knowledge is the key to unlocking increased sales, stronger customer relationships, and ultimately, a thriving dealership. So, what are you waiting for? Start listening and turn those whispers into deals! Or