Auto loan delinquencies extending beyond subprime consumers

by Kelly Kent 4 min read February 2, 2017

auto trade data

There has been a lot of discussion around the auto loan market regarding delinquency rates in the past year. It is a topic Experian is asked about frequently from clients in regard to what particular economic market behaviors mean for the overall consumer lending. To understand this issue more clearly, I ran a deeper dive on the data from our Q3 Experian-Oliver Wyman Market Intelligence report. There are some interesting, and perhaps concerning, trends in the data for automotive loans and leases.

Want Insights on the latest consumer credit trends?

Register for our 2016 year-end review webinar.

Register now

Auto loan delinquency rates are at their highest mark since 2008

The findings indicate that the performance of the most recent loans opened from Q4 2015 are now performing as poorly as the loans from the credit crisis back in 2008. In fact, you have to go back to 2008, and in some cases, 2007, to see loan default rates as poorly as the Q4 2015 auto loans originated in the last year.

Below we have the auto loan vintage performance for loans originated in Q4 of the last 8 years — going back to 2008. The lines on the chart each represent 60 days late or more (60+) delinquency rates over specific time period grades. For these charts, I analyzed the first three, six, and nine months from the loan origination date. As you can see, the rates of delinquency have steadily increased in recent years, with the increase in the Q4 2015 loans opened equaling or even surpassing 2008 levels.

The above chart reflects all credit grades, so one might think that this change is a result of the change in the credit origination mix. By digging a little deeper into the data, we can control for the VantageScore® credit score at the loan opening, or origination date, and review performance by looking at two different score segments separately.

Is there concern for Superprime and Prime consumers auto loans?

In the chart immediately below, the same analysis as above has been conducted, but only for trades originated by Superprime and Prime consumers at the time of origination. You can see that although the trend is not as pronounced as when all grades are considered, even these tiers of consumers are showing significant increases in their 60+ days past due (DPD) rates in recent vintages.

Separately, looking at the Subprime and Deep Subprime segments, you can really see the dramatic changes that have occurred in the performance of recent auto vintages. Holding score segments constant, the data indicates a rate of credit deterioration in the Subprime and Deep Subprime segments that we have not observed since at least 2008 — back to when we started tracking this data. What’s concerning here is not only the absolute values of the vintage delinquencies but also the trend, which is moving upward for all three time periods.

Where does the risk fall?

Now that we see the evidence of the deterioration of credit performance across the credit spectrum, one might ask – who is bearing the risk in these recent vintages? Taking a closer look at the chart below, you can see the significant increase in the volumes of loans across lender type, but particularly interesting to me is the increase in 2016 for the Captive Auto lenders and Credit Unions, who are hitting highs in their lending volumes in recent quarters. If the above trend holds and the trajectory continues, this suggests exposure issues for those lenders with higher volumes in recent months.

What does this mean for your business?

Speak to Experian’s global consulting practice to learn more.

Learn more

Just to be thorough, let’s continue and look at the relative amounts of loans going to the different score segments by each of the lender types. Comparing the lender type and the score segments (below) reveals that finance lenders have a greater than average exposure to the Subprime and Deep Subprime segments.

To summarize, although auto lending has recently been viewed as a segment where loan performance is good, relative to historical levels, I believe, the above data signals a striking change in that perspective. Recent loan performance has weakened to a point where comparing the 2008 vintage with 2015 vintage, one might not be able to distinguish between the two.

// <![CDATA[
var elems={'winWidth':window.innerWidth,'winTol':600,'rotTol':800,'hgtTol':1500},
updRes=function(){var xAxislabelSize=function(){if(elems.winWidth<elems.winTol){return'12px'}else{return'14px'}},xAxislabelRotation=function(){if(elems.winWidth<elems.rotTol){return-90}else{return 0}},seriesLabelSize=function(){if(elems.winWidth<elems.winTol){return'12px'}else{return'16px'}},legenLabelSize=function(){if(elems.winWidth<elems.winTol){return'12px'}else{return'16px'}},chartHeight=function(){if(elems.winWidth<elems.rotTol){return 600}else{return 400}},labelInside=function(){if(elems.winWidth<elems.rotTol){return false}else{return true}},chartStack=function(){if(elems.winWidth<elems.rotTol){return null}else{return'normal'}};this.sourceRef=function(){return['Source: Experian.com']};this.seriesColor=function(){return['#982881','#0d6eb6','#26478D','#d72b80','#575756','#b02383']};this.chartFontFamily=function(){return'"Roboto",Helvetica,Arial,sans-serif'};this.xAxislabelSize=function(){return xAxislabelSize()};this.xAxislabelOverflow=function(){return'none'};this.xAxislabelRotation=function(){return xAxislabelRotation()};this.seriesLabelSize=function(){return seriesLabelSize()};this.legenLabelSize=function(){return legenLabelSize()};this.chartHeight=function(){return chartHeight()};this.labelInside=function(){return labelInside()};this.chartStack=function(){return chartStack()}}(),
updY=function(chart){var points=chart.series[0].points;for(var i=0;i
elems.rotTol){if(thisWidth<20){var y=points[i].dataLabel.y;y-=10;points[i].dataLabel.css({color:'#575756'}).attr({y:y-thisWidth})}}}},updX=function(chart){var points=chart.series[0].points;for(var i=0;i
elems.rotTol){if(thisWidth

Related Posts

Expanding the Prescreen View with Alternative Credit Data

Start with a simple question Credit prescreen is an important tool in many lenders’ growth strategies. But the precision of any prescreen strategy depends on the data behind it. What financial behavior might traditional credit data alone not reveal? With Clarity data now available for Instant Prescreen decisioning, lenders can bring alternative credit insights into their targeting strategy, helping them identify prospects who may align with their established criteria, refine targeting strategies and explore additional acquisition opportunities while maintaining control over their risk thresholds. Additional insights alongside traditional credit data For many consumers, a traditional credit file tells a rich and reliable story. But it doesn't always tell the whole story. Consumers may also be using alternative financial products, such as small-dollar installment loans, single-payment loans, auto title loans or rent-to-own agreements and building payment histories that provide additional signals about their financial behavior. For lenders, those unseen signals can represent untapped opportunities. With more than 60 million unique subprime identities, Clarity's database helps lenders gain a more complete view of their applicant pool. Clarity data adds another dimension to that view, providing alternative credit insights that can help lenders better understand consumers whose financial behavior may not be fully represented by traditional credit data alone. How Clarity data sharpens instant prescreen decisioning Clarity provides specialty alternative credit data, with insights into subprime and near-prime consumer activity that may not appear in traditional credit files. And because Clarity is part of Experian, those insights can now be brought directly into Instant Prescreen decisioning. That means lenders can incorporate additional attributes and scores into their credit decisioning strategies without managing a separate data feed or stitching together disconnected sources. It has quickly become a visibility gap lenders can't ignore. Additional data may help support more granular segmentation and targeting strategies. Lenders remain in control of their criteria and risk thresholds while gaining additional information to inform their prescreen strategies. When considered alongside traditional credit data, alternative credit insights can support several aspects of prescreen decisioning: Identify more opportunities: Surface qualified prospects who may be harder to identify using traditional credit data alone. Refine targeting: Add alternative credit insights to help differentiate consumers with greater precision. Inform offer strategies: Use a broader view of financial behavior to help align consumers with appropriate offers. Expand intelligently: Explore incremental audience opportunities while maintaining control over your established risk criteria. Simplify execution: Access Experian and Clarity insights within a connected Instant Prescreen decisioning environment. See more opportunity in your prescreen strategy Growth doesn’t always require looking for an entirely new audience. Sometimes, it starts with seeing more in the audience already in front of you. By bringing Clarity data into Instant Prescreen, lenders can add another layer of insight to their decisioning, helping identify incremental opportunities, refine targeting and support acquisition decision processes across a broader range of consumers. Explore prescreen solutions

September 3, 2026 by Zohreen Ismail
Are Fraudsters Building Better Identities Than Your Customers?

Fraudsters are getting surprisingly good at onboarding. Sometimes, better than your customers. Legitimate customers treat onboarding like an errand. They start an application between other tasks, get distracted, forget a password, switch devices, upload a document or come back later to finish. Their digital lives aren’t always linear, because real life isn’t either. Fraudsters approach onboarding differently. For them, opening an account is the objective. Every interaction is designed to increase the odds of success. The difference raises an uncomfortable question hanging over onboarding: What exactly are we rewarding? When smooth becomes suspicious Digital onboarding has traditionally rewarded experiences that feel smooth, consistent and complete. The challenge is that legitimate customers rarely behave that way. Most people approach onboarding somewhere between mildly distracted and mildly annoyed. They pause halfway through because dinner is burning. They reopen an old account only to realize everything is attached to an email they made in college and, somehow, still use for airline receipts. Digital life accumulates history unevenly, because ordinary life does too. Fraudsters have every reason to eliminate those inconsistencies. Applications may be rehearsed. Identity attributes are assembled deliberately. Contact points are prepared in advance. Every interaction is optimized to make the application appear credible. Ironically, the qualities organizations often associate with confidence — clean submissions, steady progression and few corrections — can also describe applications that have been carefully engineered to pass inspection. The challenge isn't that smooth onboarding is meaningless. It's that smooth onboarding, by itself, doesn't tell the whole story. Context changes interpretation A smooth onboarding experience should be the beginning of the evaluation, not the end. Behavior provides important context. How someone moves through an application can reveal whether the experience feels naturally human or unusually orchestrated. Do they interact naturally? Do they hesitate, correct mistakes or navigate in ways that resemble ordinary human behavior? Or does the session appear unusually scripted, automated or repetitive? Identity verification adds another layer. Matching information across trusted sources, validating identity details and strengthening confidence in account creation remain important, particularly when onboarding decisions carry financial, fraud or customer experience consequences. But verification largely answers a point-in-time question: Does this information match right now? A third layer comes from digital history. An inbox attached to years of airline receipts, loyalty accounts, subscription renewals, account recovery, financial notifications and familiar digital routines introduces a different kind of confidence. Legitimate digital identities leave behind patterns of persistence and engagement that develop gradually over time. Fraudsters can assemble convincing identity attributes, but creating years of ordinary digital life is much harder. Building confidence in an identity requires more than verifying information submitted during a single onboarding session. It requires understanding whether the identity reflects a broader history that supports what the application suggests. A multilayered approach builds stronger identity confidence No single signal can provide a complete view of identity risk. Organizations need multiple sources of confidence that reinforce one another. That's the thinking behind our approach: combining behavioral intelligence, identity verification and digital identity continuity into a more complete view of risk. We bring these complementary layers together through: • NeuroID adds behavioral context during onboarding and account creation, helping identify interaction patterns that may indicate automation, manipulation or coordinated fraud. • Precise ID® strengthens identity verification and resolution by comparing applicant information with trusted identity data. • AtData, recently added to our portfolio, contributes email-centered intelligence based on persistence, engagement and long-term digital history. Together, these capabilities help organizations move beyond evaluating a single moment in time to understanding whether an identity is supported by consistent behavior, trusted identity data and an established digital history. The future of fraud prevention isn't about rewarding the smoothest application. It's about recognizing the most trustworthy identity. Fraudsters can rehearse an application. They can optimize an onboarding journey. They can even assemble convincing identity attributes. What they can't easily manufacture is years of ordinary digital life. That's why digital identity continuity has become an important layer of modern fraud prevention. Combined with identity verification and behavioral intelligence, it helps organizations distinguish between identities that simply look convincing and those supported by a history that is much harder to fake. Learn more Contact us

September 2, 2026 by Julie Lee
From Hybrids to Refinancing: Consumers are Finding New Roads to Vehicle Affordability

For today’s automotive consumers, considering a vehicle purchase isn’t just about the price they see on the window, it’s about finding the right combination of their vehicle preference and monthly payment. In fact, data from Experian Automotive’s State of the Automotive Finance Market Report: Q2 2026 highlighted how affordability continues to shape the automotive finance market. For instance, hybrids offered the lowest average new vehicle loan payment across all fuel types, coming in at $646 in Q2 2026, compared to electric vehicles (EVs) at $692, and gasoline-powered vehicles at $721. This led to considerable growth in new vehicle market share for hybrids this quarter, accounting for 16.80%, from 12.99% last year. While the automotive market continues to offer consumers an expanding mix of fuel types, the combination of growing hybrid share and comparatively lower monthly payments is something worth watching. Affordability isn’t just about what consumers drive, it’s how they finance it While hybrid vehicles are continuing to pave their way in the vehicle market, consumers who already have an auto loan are finding greater savings through refinancing. In the second quarter of 2026, automotive refinancing reached approximately 140,000 loans. More notably, the financial benefit associated with refinancing has grown. Consumers who refinanced this quarter reduced their average interest rate by more than 2.4%, with the average rate moving from 10.40% on the original loan to 7.97% on the refinanced loan. Those rate reductions translated into meaningful monthly savings, especially when refinancing through particular lenders. In Q2 2026, refinancing saved consumers an average of $83 per month, compared to an average monthly savings of $64 this time last year. However, credit unions delivered the largest average payment difference among lender types at $102 this quarter, followed by banks ($65), and finance companies ($38). It’s important for automotive professionals to acknowledge that affordability is not a single moment in the vehicle journey. It can influence the vehicle a consumer chooses, the financing they opt for during that transaction, and the decisions they make years after driving off the lot. Understanding and leveraging those different moments can help professionals identify opportunities to better serve consumers throughout the vehicle ownership lifecycle. To learn more about automotive finance trends, view the full State of the Automotive Finance Market Report: Q2 2026 presentation on demand.

August 27, 2026 by Melinda Zabritski

Subscribe to our Newsletter

Enter your name and email for the latest updates.

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Subscribe to our Newsletter

Don't miss out on the latest industry trends and insights!
Subscribe