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of the printing and typesetting industry. Lorem Ipsum has been the industry’s standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum
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Explore credit card marketing trends and strategies to help you identify, engage, and acquire the right customers.
"Grandma, it’s me, Mike.” Imagine hearing the voice of a loved one (or what sounds like it) informing you they were arrested and in need of bail money. Panicked, a desperate family member may follow instructions to withdraw a large sum of money to provide to a courier. Suspicious, they even make a video call to which they see a blurry image on the other end, but the same voice. When the fight or flight feeling settles, reality hits. Sadly, this is not the scenario of an upcoming Netflix movie. This is fraud – an example of a new grandparent scam/family emergency scam happening at scale across the U.S. While generative AI is driving efficiencies, personalization and improvements in multiple areas, it’s also a technology being adopted by fraudsters. Generative AI can be used to create highly personalized and convincing messages that are tailored to a specific victim. By analyzing publicly available social media profiles and other personal information, scammers can use generative AI to create fake accounts, emails, or phone calls that mimic the voice and mannerisms of a grandchild or family member in distress. The use of this technology can make it particularly difficult to distinguish between real and fake communication, leading to increased vulnerability and susceptibility to fraud. Furthermore, generative AI can also be used to create deepfake videos or audio recordings that show the supposed family member in distress or reinforce the scammer's story. These deepfakes can be incredibly realistic, making it even harder for victims to identify fraudulent activity. What is Generative AI? Generative artificial intelligence (GenAI) describes algorithms that can be used to create new content, including audio, code, images, text, simulations, and videos. Generative AI has the potential to revolutionize many industries by creating new and innovative content, but it also presents a significant risk for financial institutions. Cyber attackers can use generative AI to produce sophisticated malware, phishing schemes, and other fraudulent activities that can cause data breaches, financial losses, and reputational damage. This poses a challenge for financial organizations, as human error remains one of the weakest links in cybersecurity. Fraudsters capitalizing on emotions such as fear, stress, desperation, or inattention can make it difficult to protect against malicious content generated by generative AI, which could be used as a tactic to defraud financial institutions. Four types of Generative AI used for Fraud: Fraud automation at scale Fraudulent activities often involve multiple steps which can be complex and time-consuming. However, GenAI may enable fraudsters to automate each of these steps, thereby establishing a comprehensive framework for fraudulent attacks. The modus operandi of GenAI involves the generation of scripts or code that facilitates the creation of programs capable of autonomously pilfering personal data and breaching accounts. Previously, the development of such codes and programs necessitated the expertise of seasoned programmers, with each stage of the process requiring separate and fragmented development. Nevertheless, with the advent of GenAI, any fraudster can now access an all-encompassing program without the need for specialized knowledge, amplifying the inherent danger it poses. It can be used to accelerate fraudsters techniques such as credential stuffing, card testing and brute force attacks. Text content generation In the past, one could often rely on spotting typos or errors as a means of detecting such fraudulent schemes. However, the emergence of GenAI has introduced a new challenge, as it generates impeccably written scripts that possess an uncanny authenticity, rendering the identification of deceit activities considerably more difficult. But now, GenAI can produce realistic text that sounds as if it were from a familiar person, organization, or business by simply feeding GenAI prompts or content to replicate. Furthermore, the utilization of innovative Language Learning Model (LLM) tools enables scammers to engage in text-based conversations with multiple victims, skillfully manipulating them into carrying out actions that ultimately serve the perpetrators' interests. Image and video manipulation In a matter of seconds, fraudsters, regardless of their level of expertise, are now capable of producing highly authentic videos or images powered by GenAI. This innovative technology leverages deep learning techniques, using vast amounts of collected datasets to train artificial intelligence models. Once these models are trained, they possess the ability to generate visuals that closely resemble the desired target. By seamlessly blending or superimposing these generated images onto specific frames, the original content can be replaced with manipulated visuals. Furthermore, the utilization of AI text-to-image generators, powered by artificial neural networks, allows fraudsters to input prompts in the form of words. These prompts are then processed by the system, resulting in the generation of corresponding images, further enhancing the deceptive capabilities at their disposal. Human voice generation The emergence of AI-generated voices that mimic real people has created new vulnerabilities in voice verification systems. Firms that rely heavily on these systems, such as investment firms, must take extra precautions to ensure the security of their clients' assets. Criminals can also use AI chatbots to build relationships with victims and exploit their emotions to convince them to invest money or share personal information. Pig butchering scams and romance scams are examples of these types of frauds where AI chatbots can be highly effective, as they are friendly, convincing, and can easily follow a script. In particular, synthetic identity fraud has become an increasingly common tactic among cybercriminals. By creating fake personas with plausible social profiles, hackers can avoid detection while conducting financial crimes. It is essential for organizations to remain vigilant and verify the identities of any new contacts or suppliers before engaging with them. Failure to do so could result in significant monetary loss and reputational damage. Leverage AI to fight bad actors In today's digital landscape, businesses face increased fraud risks from advanced chatbots and generative technology. To combat this, businesses must use the same weapons than criminals, and train AI-based tools to detect and prevent fraudulent activities. Fraud prediction: Generative AI can analyze historical data to predict future fraudulent activities. By analyzing patterns in data and identifying potential risk factors, generative AI can help fraud examiners anticipate and prevent fraudulent behavior. Machine learning algorithms can analyze patterns in data to identify suspicious behavior and flag it for further investigation. Fraud Investigation: In addition to preventing fraud, generative AI can assist fraud examiners in investigating suspicious activities by generating scenarios and identifying potential suspects. By analyzing email communications and social media activity, generative AI can uncover hidden connections between suspects and identify potential fraudsters. To confirm the authenticity of users, financial institutions should adopt sophisticated identity verification methods that include liveness detection algorithms and document-centric identity proofing, and predictive analytics models. These measures can help prevent bots from infiltrating their systems and spreading disinformation, while also protecting against scams and cyberattacks. In conclusion, financial institutions must stay vigilant and deploy new tools and technologies to protect against the evolving threat landscape. By adopting advanced identity verification solutions, organizations can safeguard themselves and their customers from potential risks. To learn more about how Experian can help you leverage fraud prevention solutions, visit us online or request a call
Learn what identity verification is and how online identity verification services and methods help organizations address fraud.
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With the National Automobile Dealers Association (NADA) Show set to kickoff later this week, it seemed fitting to explore how the shifting dynamics of the used vehicle market might impact dealers and buyers over the coming year. Shedding light on some of the registration and finance trends, as well as purchasing behaviors, can help dealers and manufacturers stay ahead of the curve. And just like that, the Special Report: Automotive Consumer Trends Report was born. As I was sifting through the data, one of the trends that stood out to me was the neck-and-neck race between Millennials and Gen X for supremacy in the used vehicle market. Five years ago, in 2019, Millennials were responsible for 33.3% of used retail registrations, followed by Gen X (29.5%) and Baby Boomers (26.8%). Since then, Baby Boomers have gradually fallen off, and Gen X continues to close the already minuscule gap. Through October 2024, Millennials accounted for 31.6%, while Gen X accounted for 30.4%. But trends can turn on a dime if the last year offers any indication. Over the last rolling 12 months (October 2023-October 2024), Gen X (31.4%) accounted for the majority of used vehicle registrations compared to Millennials (30.9%). Of course, the data is still close, and what 2025 holds is anyone’s guess, but understanding even the smallest changes in market share and consumer purchasing behaviors can help dealers and manufacturers adapt and navigate the road ahead. Although there are similarities between Millennials and Gen X, there are drastic differences, including motivations and preferences. Dealers and manufacturers should engage them on a generational level. What are they buying? Some of the data might not come as a surprise but it’s a good reminder that consumers are in different phases of life, meaning priorities change. Over the last rolling 12 months, Millennials over-indexed on used vans, accounting for more than one-third of registrations. Meanwhile, Gen X over-indexed on used trucks, making up nearly one-third of registrations, and Gen Z over-indexed on cars (accounting for 17.1% of used car registrations compared to 14.6% of overall used vehicle registrations). This isn’t surprising. Many Millennials have young families and may need extra space and functionality, while Gen Xers might prefer the versatility of the pickup truck—the ability to use it for work and personal use. On the other hand, Gen Zers are still early in their careers and gravitate towards the affordability and efficiency of smaller cars. Interestingly, although used electric vehicles only make up a small portion of used retail registrations (less than 1%), Millennials made up nearly 40% over the last rolling 12 months, followed by Gen X (32.2%) and Baby Boomers (15.8%). The market at a bird’s eye view Pulling back a bit on the used vehicle landscape, over the last rolling 12 months, CUVs/SUVs (38.9%) and cars (36.6%) accounted for the majority of used retail registrations. And nearly nine-in-ten used registrations were non-luxury vehicles. What’s more, ICE vehicles made up 88.5% of used retail registrations over the same period, while alternative-fuel vehicles (not including BEVs) made up 10.7% and electric vehicles made up 0.8%. At the finance level, we’re seeing the market shift ever so slightly. Since the beginning of the pandemic, one of the constant narratives in the industry has been the rising cost of owning a vehicle, both new and used. And while the average loan amount for a used non-luxury vehicle has gone up over the past five years, we’re seeing a gradual decline since 2022. In 2019, the average loan amount was $22,636 and spiked $29,983 in 2022. In 2024, the average loan amount reached $28,895. Much of the decline in average loan amounts can be attributed to the resurgence of new vehicle inventory, which has resulted in lower used values. With new leasing climbing over the past several quarters, we may see more late-model used inventory hit the market in the next few years, which will most certainly impact used financing. The used market moving forward Relying on historical data and trends can help dealers and manufacturers prepare and navigate the road ahead. Used vehicles will always fit the need for shoppers looking for their next vehicle; understanding some market trends will help ensure dealers and manufacturers can be at the forefront of helping those shoppers. For more information on the Special Report: Automotive Consumer Trends Report, visit Experian booth #627 at the NADA Show in New Orleans, January 23-26.
The automotive industry is constantly changing. Shifting consumer demands and preferences, as well as dynamic economic factors, make the need for data-driven insights more important than ever. As we head into the National Automobile Dealers Association (NADA) Show this week, we wanted to explore some of the trends in the used vehicle market in our Special Report: State of the Automotive Finance Market Report. Packed with valuable insights and the latest trends, we’ll take a deep dive into the multi-faceted used vehicle market and better understand how consumers are financing used vehicles. 9+ model years grow Although late-model vehicles tend to represent much of the used vehicle finance market, we were surprised by the gradual growth of 9+ model year (MY) vehicles. In 2019, 9+MY vehicles accounted for 26.6% of the used vehicle sales. Since then, we’ve seen year-over-year growth, culminating with 9+MY vehicles making up a little more than 30% of used vehicle sales in 2024. Perhaps more interesting though, is who is financing these vehicles. Five years ago, prime and super prime borrowers represented 42.5% of 9+MY vehicles, however, in 2024, those consumers accounted for nearly 54% of 9+MY originations. Among the more popular 9+MY segments, CUVs and SUVs comprised 36.9% of sales in 2024, up from 35.2% in 2023, while cars went from 44.3% to 42.9% year-over-year and pickup trucks decreased from 15.9% to 15.6%. 2024 highlights by used vehicle age group To get a better sense of the overall used market, the segments were broken down into three age groups—9+MY, 4-8MY, and current +3MY—and to no surprise, the finance attributes vary widely. While we’ve seen the return of new vehicle inventory drive used vehicle values lower, it could be a sign that consumers are continuing to seek out affordable options that fit their lifestyle. In fact, the average loan amount for a 9+MY vehicle was $19,376 in 2024, compared to $24,198 for a vehicle between 4-8 years old and $32,381 for +3MY vehicle. Plus, more than 55% of 9+MY vehicles have monthly payments under $400. That’s not an insignificant number for people shopping with the monthly payment in mind. In 2024, the average monthly payment for a used vehicle that falls under current+3MY was $608. Meanwhile, 4-8MY vehicles came in at an average monthly payment of $498, and 9+MY vehicles had a $431 monthly payment. Taking a deeper dive into average loan amounts based on specific vehicle types—as of 2024, current +3MY cars came in at $28,721, followed by CUVs/SUVs ($31,589) and pickup trucks ($40,618). As for 4-8MY vehicles, cars came in with a loan amount of $22,013, CUVs/SUVs were at $23,133, and pickup trucks at $31,114. Used 9+MY cars had a loan amount of $19,506, CUVs/SUVs came in at $17,350, and pickup trucks at $22,369. With interest rates remaining top of mind for most consumers as we’ve seen them increase in recent years, understanding the growth from 2019-2024 can give a holistic picture of how the market has shifted over time. For instance, the average interest rate for a used current+3MY vehicle was 8.0% in 2019 and grew to 10.2% in 2024, the average rate for a 4-8MY vehicle went from 10.3% to 12.9%, and the average rate for a 9+MY vehicle increased from 11.4% to 13.8% in the same time frame. Looking ahead to the used vehicle market It’s important for automotive professionals to understand and leverage the data of the used market as it can provide valuable insights into trending consumer behavior and pricing patterns. While we don’t exactly know where the market will stand in a few years—adapting strategies based on historical data and anticipating shifts can help professionals better prepare for both challenges and opportunities in the future. As used vehicles remain a staple piece of the automotive industry, making informed decisions and optimizing inventory management will ensure agility as the market continues to shift. For more information, visit us at the Experian booth (#627) during the NADA Show in New Orleans from January 23-26.
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typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.