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By: Kennis Wong Several weeks ago, I attended and presented at Experian’s sold-out annual conference, Vision, in Phoenix, Arizona. One of the guest speakers was Malcolm Gladwell, best-selling author of The Tipping Point, Blink, Outliers and What the Dog Saw: And Other Adventures. Since I've read three of his four books, I could be considered a fan. And yes, his hair did look as wild in person as it appears in the pictures on the insides of his book covers. But that was not why I was so impressed by his speech. The real reason was that his topic was so relevant to how Experian Decision Analytics delivers value to our clients. Gladwell spent the whole hour addressing the difference between “puzzle” and “mystery”, providing abundant examples for both. The puzzle-versus-mystery topic was from one of his articles in The New Yorker. To solve a puzzle, one or more pieces of information are needed. The source of the problem is that insufficient data is available to have a conclusive answer to the question. An example would be finding Osama Bin Laden’s whereabouts. We simply do not have enough information to locate him, and we need more intelligence. On the other hand, a mystery is not solved by simply gathering more information. It is a matter of making sense out of a massive amount of data available, using analysis and judgment. Enron’s creative accounting was an example of a mystery. All the information was out in the open. Pages and pages of SEC filings and annual reports were there for anyone who was willing and able to analyze them. All that was needed to solve the mystery was to make sense out of the data. In the Fraud and Identity Solutions team, we satisfy clients’ needs by providing solutions for both puzzles and mysteries to fend off fraudsters. Besides the core credit bureau data, we have demographic data, fraud consortium data, past application data, automotive data and much more. We also have strategic partnerships to deliver demand deposit account, cell phone, and device data. All these data sources ensure that our clients get the data they need to piece the puzzle together. Our consulting and analytics, on the other hand, help clients to solve mysteries. Looking at individual pieces of disparate data is inefficient and provides little or no value. That’s why our numerous scoring solutions combine the available data in a way that is most predictive of various fraud outcomes. For example, our Precise ID Score and Fraud Shield Score Plus predict first- and third-party fraud; our BustOut Score predicts the likelihood of bust outs; our Never Pay score predicts the likelihood of a consumer never making a payment. As more data are available, we incorporate them into existing or new models if it increases the effectiveness of the models. So we have both the puzzle and mystery grounds covered. A note to Malcolm Gladwell: Great job at Vision! If you write a book about this topic, I’ll definitely buy it.
By: Kari Michel Credit risk models are used by almost every lender, and there are many choices to choose from including custom or generic models. With so many choices how do you know what is best for your portfolio? Custom models provide the strongest risk prediction and are developed using an organization’s own data. For many organizations, custom models may not be an option due to the size of the portfolio (may be too small), lack of data including not enough bads, time constraints, and/or lack of resources. If a custom model is not an option for your organization, generic bureau scoring models are a very powerful alternative for predicting risk. But how can you understand if your current scoring model is the best option for you? You may be using a generic model today and you hear about a new generic model, for example the VantageScore® credit score. How do you determine if the new model is more predictive than your current model for your portfolio? The best way to understand if the new model is more predictive is to do a head-to-head comparison – a validation. A validation requires a sample of accounts from your portfolio including performance flags. An archive is pulled from the credit reporting agency and both scores are calculated from the same time period and a performance chart is created to show the comparison. There are two key performance metrics that are used to determine the strength of the model. The KS (Komogorov-Smirnov) is a statistical term that measures the maximum difference between the bad and good cumulative score distribution. The KS range is from 0% to 100%, with the higher the KS the stronger the model. The second measurement uses the bad capture rate in the bottom 5%, 10% or 15% of the score range. A stronger model will provide better risk prediction and allow an organization to make better risk decisions. Overall, when stronger scoring models are used, organizations will be best prepared to decrease their bad rates and have a more profitable portfolio.
With the upcoming changes to overdraft fee policies coming to the banking industry July 1st, courtesy of the Federal Reserve, banks and credit unions are re-examining the revenue growth opportunities through their new account opening process. We frequently hear from our fraud risk and operations client partners that when there is a push for revenue growth, fraud detection gets de-prioritized as a trade off to bringing in more new customers. A DDA-friendly risk based authentication approach may offer some compromise to this seemingly “one for one” exchange. Here are some quick revenue-friendly, risk-averse practices being seen in the branches, call centers, and online channels of Experian clients: • Drive referrals to knowledge based authentication (KBA), negative record checks (account abuse, fraud records) or both off of an upfront fraud score, such as the Precise ID(SM) for Account Opening score. Segmenting based on risk is cost efficient and promotes an improved customer experience. • Bolster the fraud defenses of your online channel by raising the “pass” or “accept” threshold. The lower acquisition costs for this online account opening are tempting but this is also the venue most exploited by fraudsters. Some incremental manual reviews should work out as a small price to pay to catch the higher prevalence of fraud. • Cross sell and up sell with confidence based on more comprehensive authentication. By applying appropriate risk based authentication strategies, more products can be offered and exposure is reduced because you know you are dealing with the true consumer.
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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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