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By: Joel Pruis From a score perspective we have established the high level standards/reporting that will be needed to stay on top of the resulting decisions. But there is a lot of further detail that should be considered and further segmentation that must be developed or maintained. Auto Decisioning A common misperception around auto-decisioning and the use of scorecards is that it is an all or nothing proposition. More specifically, if you use scorecards, you have to make the decision entirely based upon the score. That is simply not the case. I have done consulting after a decisioning strategy based upon this misperception and the results are not pretty. Overall, the highest percentage for auto-decisioning that I have witnessed has been in the 25 – 30% range. The emphasis is on the “segment”. The segments is typically the lower dollar requests, say $50,000 or less, and is not the percentage across the entire application population. This leads into the discussion around the various segments and the decisioning strategy around each segment. One other comment around auto-decisioning. The definition related to this blog is the systematic decision without human intervention. I have heard comments such as “competitors are auto-decisioning up to $1,000,000”. The reality around such comments is that the institution is granting loan authority to an individual to approve an application should it meet the particular financial ratios and other criteria. The human intervention comes from verifying that the information has been captured correctly and that the financial ratios make sense related to the final result. The last statement is the key to the disqualification of “auto-decisioning”. The individual is given the responsibility to ensure data quality and to ensure nothing else is odd or might disqualify the application from approval or declination. Once a human eye is looking at an application, judgment comes into the picture and we introduce the potential for inconsistencies and or extension of time to render the decision. Auto-decisioning is just that “Automatic”. It is a yes/no decision and is based upon objective factors that if met, allow the decision to be made. Other factors, if not included in the decision strategy, are not included. So, my fellow credit professionals, should you hear someone say they are auto-decisioning a high percent of their applications or a high dollar amount for an application, challenge, question and dig deeper. Treat it like the fishing story “I caught a fish THIS BIG”. No financials segment The highest volume of applications and the lowest total dollar production area of any business banking/small business product set. We had discussed the use of financials in the prior blog around application requirements so I will not repeat that discussion here. Our focus will be on the decisioning of these applications. Using score and application characteristics as the primary data source, this segment is the optimal segment for auto-decisioning. Speeds the decision process and provides the greatest amount of consistency in the decisions rendered. Two key areas for this segment are risk premiums and scorecard validations. The risk premium is important as you are going to accept a higher level of losses for the sake of efficiencies in the underwriting/processing of the application. The end result is lower operational costs, relatively higher credit losses but the end yield on this segment meets the required, yet practical, thresholds for return. The one thing that I will repeat from a prior blog is that you may request financials after the initial review but the frequency should be low and should also be monitored. The request of financials should not be the “belt and suspenders” approach. If you know what the financials are likely to show, then don’t request them. They are unnecessary. You are probably right and the collection of the financials will only serve to elongate the response time, frustrate everyone involved in the process and not change the expected results. Financials segment The relatively lower unit volume but the higher dollar volume segment. Likely this segment will have no auto-decisioning as the review of financials typically will mandate the judgmental review. From an operational perspective, these are high dollar and thus the manual review does not push this segment into a losing proposition. From a potential operational lift perspective, the ability to drive a higher volume of applications into auto-decisioning is simply not available as we are talking probably less than 40% (if not fewer) of all applications in this segment. In this segment, the consistency becomes more difficult as the underwriter tends to want to put his/her own approach on the deal. Standardization of the analysis approach (at least initially) is critical for this segment. Consistency in the underwriting and the various criteria allows for greater analysis to determine where issues are developing or where we are realizing the greatest success. My recommended approach is to standardize (via automation in the origination platform) the various calculations in a manner that will generate the most conservative approach. Bluntly put, my approach was to attempt to make the deal as ugly as possible and if it still passed the various criteria, no additional work was needed nor was there any need for detailed explanation around how I justified the deal/request. Only if it did not meet the criteria using the most conservative approach would I need to do any work and only if it was truly going to make a difference. Basic characteristics in this segment include – business cash flow, personal debt to income, global cash flow and leverage. Others may be added but on a case by case basis. What about the score? If I am doing so much judgmental underwriting, why calculate the score in this segment? In a nutshell, to act as the risk rating methodology for the portfolio approach. Even with the judgmental approach, we do not want to fall into the trap thinking we are going to be able to adequately monitor this segment in a proactive fashion to justify the risk rating at any point in time after the loan is booked. We have been focusing on the origination process in this blog series but I need to point out that since we are not going to be doing a significant amount of financial statement monitoring in the small business segment, we need to begin to move away from the 1 – 8 (or 9 or 10 or whatever) risk rating method for the small business segment. We cannot be granular enough with this rating system nor can we constantly stay on top of what may be changing risk levels related to the individual clients. But I am going to save the portfolio management area for a future blog. Regardless of the segment, please keep in mind that we need to be able to access the full detail of the information that is being captured during the origination process along with the subsequent payment performance. As you are capturing the data, keep in mind, the abilities to Access this data for purposes of analysis Connect the data from origination to the payment performance data to effectively validate the scorecard and my underwriting/decisioning strategies Dive into the details to find the root cause of the performance problem or success The topic of decisioning strategies is broad so please let me know if you have any specific topics that you would like addressed or questions that we might be able to post for responses from the industry.
Recently we released a white paper that emphasizes the need for better, more granular indicators of local home-market conditions and borrower home equity, with a very interesting new finding on leading indicators in local-area credit statistics. Click here to download the white paper Home-equity indicators with new credit data methods for improved mortgage risk analytics Experian white paper, April 2012 In the run-up to the U.S. housing downturn and financial crisis, perhaps the greatest single risk-management shortfall was poorly predicted home prices and borrower home equity. This paper describes new improvements in housing market indicators derived from local-area credit and real-estate information. True housing markets are very local, and until recently, local real-estate data have not been systematically available and interpreted for broad use in modeling and analytics. Local-area credit data, similarly, is relatively new, and its potential for new indicators of housing market conditions is studied here in Experian’s Premier Aggregated Credit Statistics.SM Several examples provide insights into home-equity indicators for improved mortgage models, predictions, strategies, and combined LTV measurement. The paper finds that for existing mortgages evaluated with current combined LTV and borrower credit score, local-area credit statistics are an even stronger add-on default predictor than borrower credit attributes. Click here to download the white paper Authors: John Straka and Chuck Robida, Experian Michael Sklarz, Collateral Analytics
As the need for password management increases, consumers’ options leave even the strictest cybersecurity aficionado pleased with the service.
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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.