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By: Joel Pruis Some of you may be thinking finally we get to the meat of the matter. Yes the decision strategies are extremely important when we talk about small business/business banking. Just remember how we got to here though, we had to first define: Who are we going to pursue in this market segment? How are we going to pursue this market segment – part 1 & part 2? What are we going to require of the applicants to request the funds? Without the above, we can create all the decision strategies we want but their ultimate effectiveness will be severely limited as they will not have a foundation based upon a successful execution. First we are going to lay the foundation for how we are going to create the decision strategy. The next blog post (yes, there is one more!) will get into some more specifics. With that said, it is still important that we go through the basics of establishing the decision strategy. These are not the same as investments. Decision strategies based upon scorecards We will not post the same disclosure as do the financial reporting of public corporations or investment solicitations. This is the standard disclosure of “past performance is not an indication of future results”. On the contrary, for scorecards, past performance is an indication of future results. Scorecards are saying that if all conditions remain the same, future results should follow past performance. This is the key. We need to fully understand what the expected results are to be for the portfolio originated using the scorecard. Therefore we need to understand the population of applications used to develop the scorecards, basically the information that we had available to generate the scorecard. This will tie directly with the information that we required of the applications to be submitted. As we understand the type of applications that we are taking from our client base we can start to understand some expected results. By analyzing what we have processed in the past we can start to build about model for the expected results going forward. Learn from the past and try not to repeat the mistakes we made. First we take a look at what we did approve and analyze the resulting performance of the portfolio. It is important to remember that we are not to be looking for the ultimate crystal ball rather a model that can work well to predict performance over the next 12 to 18 months. Those delinquencies and losses that take place 24, 36, 48 months later should not and cannot be tied back to the information that was available at the time we originated the credit. We will talk about how to refresh the score and risk assessment in a later blog on portfolio management. As we see what was approved and demonstrated acceptable performance we can now look back at those applications we processed and see if any applications that fit the acceptable profile were actually declined. If so, what were the reasons for the declinations? Do these reasons conflict with our findings based upon portfolio performance? If so, we may have found some additional volume of acceptable loans. I say "may" because statistics by themselves do not tell the whole story, so be cautious of blindly following the statistical data. My statistics professor in college drilled into us the principle of "correlation does not mean causation". Remember that the next time a study featured on the news. The correlation may be interesting but it does not necessarily mean that those factors "caused" the result. Just as important, challenge the results but don't use outliers to disprove here results or the effectiveness of the models. Once we have created the model and applied it to our typical application population we can now come up with some key metrics that we need to manage our decision strategies: Expected score distributions of the applications Expected approval percentage Expected override percentage Expected performance over the next 12-18 months Expected score distributions We build the models based upon what we expect to be the population of applications we process going forward. While we may target market certain segments we cannot control the walk-in traffic, the referral volume or the businesses that will ultimately respond to our marketing efforts. Therefore we consider the normal application distribution and its characteristics such as 1) score; 2) industry; 3) length of time in business; 4) sales size; etc. The importance of understanding and measuring the application/score distributions is demonstrated in the next few items. Expected approval percentages First we need to consider the approval percentages as an indication of what percent of the business market to which we are extending credit. Assuming we have a good representative sample of the business population in the applications we are processing we need to determine what percentile of businesses will be our targeted market. Did our analysis show that we can accept the top 40%? 50%? Whatever the percentage, it is important that we continue to monitor our approval percentage to determine if we are starting to get too conservative or too liberal in our decisioning. I typically counsel my client that “just because your approval percentage is going up is not necessarily an improvement!” By itself an increase in approval percentage is not good. I'm not saying that it is bad just that when it goes up (or down!) you need to explain why. Was there a targeted marketing effort? Did you run into a short term lucky streak? OR is it time to reassess the decision model and tighten up a bit? Think about what happens in an economic expansion. More businesses are surviving (note I said surviving not succeeding). Are more businesses meeting your minimum criteria? Has the overall population shifted up? If more businesses are qualifying but there has been no change in the industries targeted, we may need to increase our thresholds to maintain our targeted 50% of the market. Just because they met the standard criteria in the expansion does not mean they will survive in a recession. "But Joel, the recession might be more than 18 months away so we have a good client for at least 18 months, don't we?". I agree but we have to remember that we built the model assuming all things remain constant. Therefore if we are confident that the expansion will continue at the same pace infinitum, then go ahead and live with the increased approval percentage. I will challenge you that it is those applicants that "squeaked by" during the expansion that will be the largest portion of the losses when the recession comes. I will also look to investigate the approval percentages when they go down. Yes you can make the same claim that the scorecard is saying that the risk is too great over the next 12-18 months but again I will challenge that if we continue to provide credit to the top 40-50% of all businesses we are likely doing business with those clients that will survive and succeed when the expansion returns. Again, do the analysis of “why” the approval percentage declined/dropped. Expected override percentage While the approval percentage may fluctuate or stay the same, another area to be reviewed is that of the override. Overrides can be score overrides or a decision override. Score override would be contradicting the decision that was recommended based upon the score and/or overall decision strategy. Decision override would be when the market/field has approval authority and overturns the decision made by the central underwriting group. Consequently you can have a score override, a decision override or both. Overrides can be an explanation for the change in approval percentages. While we anticipate a certain degree of overrides (say around 5%), should the overrides become too significant we start to lose control of the expected outcomes of the portfolio performance. As such we need to determine why the overrides have increase (or potentially decrease) and the overrides impact on the approval percentage. We will address some specifics around override management in a later blog. Suffice to say, overrides will always be present but we need to keep the amount of overrides within tolerances to be sure we can accurate assess future performance. Expected performance over next 12-18 months The measure of expected performance is at minimum the expected probability/propensity of repayment. This may be labeled as the bad rate or the probability of default (PD). In a nutshell it is the probability that the credit facility will be a certain level of delinquency over the next 12-18 months. What the base level expected performance based upon score is not the expected “loss” on the account. That is a combination of the probability of default combined with the expected loss given event of default. For the purpose of this post we are talking about the probability of default and not the loss given event of default. For reinforcement we are simply talking about the percentage of accounts that go 30 or 60 or 90 days past due during the 12 – 18 months after origination. So bottom line, if we maintain a score distribution of the applications processed by the financial institution, maintain the approval percentage as well as the override percentages we should be able to accurately assess the future performance of the newly originated portfolio. Coming up next… A more tactical discussion of the decision strategy
Lenders continued to increase their appetite for risk in Q2 2011, with new vehicle loans for customers with credit outside of prime increasing by 22.4 percent compared with the previous year. In Q2 2011, 22.29 percent of all new vehicle loans went to customers in the nonprime, subprime and deep-subprime categories, increasing from 18.21 percent in Q2 2010. The largest percentage increase in new car loans was in the category with the highest risk: deep subprime, which jumped 44.1 percent, moving from 1.48 percent of all new vehicle loans in Q2 2010 to 2.13 percent in Q2 2011. For more information on Experian Automotive's AutoCount® Risk Report, visit www.autocount.com Source: Automotive quarterly credit trends
Experian® QAS®, a leading provider of address verification software and services, recently released a new benchmark report on the data quality practices of top online retailers. The report revealed that 72 percent of the top 100 retailers are using some form of address verification during online checkout. This third annual benchmark report enables retailers to compare their online verification practices to those of industry leaders and provides tips for accurately capturing email addresses, a continuously growing data point for retailers. To find out how online retailers are utilizing contact data verification, download the complimentary report 2012 Address Verification Benchmark Report: The Top 100 Online Retailers. Source: Press release: Experian QAS Study Reveals Prevalence of Real-Time Address Verification Increasing Among Top Online Retailers.
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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.