New vehicle loans made to subprime and deep subprime consumers are decreasing as prime and superprime consumers are receiving the highest volume of new-vehicle funding they have received since 2012, according to Experian’s first quarter State of the Automotive Finance Market report.
Specifically, subprime and deep subprime consumers received a 22.5% fewer new-vehicles loans in the first quarter compared with the same period the year prior. The percentage of subprime shrank to 18.7% of loan balances compared with 19.7% the comparable period.
Meanwhile, prime and superprime consumers increased their share of total loan balances to 63.8% compared with 61.4% in 1Q17, which could serve as an indication that lenders have become more risk-averse. The percentage is even higher when isolating new-vehicle sales with 73.4% of those loans going to the two highest credit tiers.
However, according to Melinda Zabritski, Experian’s senior director of automotive financial solutions, credit standards could loosen. The percentage of 30-day delinquencies have dropped to 1.90% compared with 1.96% the same period the year prior, while 60-day delinquencies have remained flat at 0.67%.
“As delinquencies drop, lenders may start to extend loans to higher risk segments – which can open up the opportunity for a larger population to purchase vehicles,” Zabritski told Auto Finance News. “That said, it’s important to maintain underwriting standards such as advances, payment-to-income and debt-to-income ratios.”
Experian attributes these trends to a rise in loan amounts and monthly payments. In 1Q 2018, average new-vehicle loans hit $31,455, an increase of $921 from the year prior. Additionally, monthly payments for a new vehicle climbed to $523, a $15 increase over the same period. The average interest rate for a new vehicle was 5.17% during the quarter.
Fiat Chrysler Automobiles Group confirmed earlier reports that it plans to form a U.S. captive finance arm and one of the ways it may get there is by buying the Chrysler Capital portfolio from Santander Consumer USA.
FCA Chief Financial Officer Richard Palmer made the announcement during an event in Italy that took place in the early hours of Friday morning U.S. time. He said the OEM has initiated discussions to buy the Chrysler Capital book from the lender, which would accelerate the company’s ability to grow profits compared with starting from scratch.
If FCA bought the book it would add $500 million to $800 million in incremental pretax earnings within four years, he said according to Bloomberg. Should FCA choose to start from scratch the company predicts $100 million in incremental profits over the same period.
Forming a captive will also allow the OEM to securitize vehicle fleets and offer financing and services on a per-mile basis and allow FCA to “participate more fully in capturing value from emerging platforms,” Palmer added.
During a press conference today, Santander expressed its interest in a future with FCA.
“We are a good partner to FCA and we expect to continue to compete today,” said Santander Chief Financial Officer Juan Carlos Alvarez de Soto.
Santander Chief Executive Scott Powell said that the companies’ interests are aligned throughout the early discussions between them. “We want to work toward giving them what they have the right to have for their benefit and our benefit,” he said.
In an earlier press release, Santander reiterated that it “underlying business remains strong, and our future growth potential and capabilities extend far beyond our relationship with FCA.” Yet, 46% of Santander’s total retail loans for the quarter were from its relationship with the manufacturer.
FCA also announced a number of changes to its line of cars including plans to phase out diesel vehicles in favor of electric cars, the expansion of a partnership with Google’s autonomous car project Waymo, expectations of doubling Jeep sales volume by 2022, an emphasis on Ram trucks, and the introduction of a Tesla-competing electric Maserati sports car.
Fiat Chrysler Automobiles Group is expected to announce the formation of a U.S. captive unit on Friday, thus diminishing volume to lending partners such as Santander Consumer USA and Ally Financial Inc., according to a Bloomberg report.
The announcement is expected to be part of a larger strategic push by the OEM’s Chief Executive Sergio Marchionne, who is planning to push Jeep- and Ram-branded SUVs as well as Maserati luxury cars in favor of some of its legacy name brands such as Fiat, Dodge, and Chrysler.
The captive finance unit is expected to be structured similarly to other U.S. competitors in the space, but leaves lingering questions for the future of its current finance partners.
Santander Consumer originated $1.9 billion in Chrysler Capital retail loans in the first quarter — a 24% increase year over year, according to earnings. Those FCA originations accounted for 46% of Santander’s total loans for the quarter. While leases are not broken out by brand, Santander originated $2 billion of auto leases, most of which were likely made on FCA vehicles.
Santander made a point of repairing its relationship with Chrysler late last year. In October 2017, newly appointed Chief Executive Scott Powell appointed Rich Morrin as president of Chrysler Capital and auto relationships in an effort to put a single point-person with a long history of leadership at Santander in charge of the lender’s most important partnership, AFN previously reported.
“FCA is a very important relationship for SC, and the team and I are focused on identifying opportunities to assist FCA in selling more vehicles and to utilize incentive spending more efficiently,” Morrin told AFN in January.
Additionally, the company opened a flow agreement with its Spanish parent Banco Santander SA in an effort to open up more funds for prime lending to FCA consumers. While those efforts have boosted volume, the penetration rate has fallen below expectations.
As of March 31, Santander recorded a 28% penetration rate for Chrysler Capital loans — up from 23% in the prior-year period, but well below the April target of 65% set by the original agreement between the companies.
The partnership was signed in 2013 and was expected to last 10 years, however, it includes a termination provision if the lender fails to meet origination goals within the first five years — which, for those counting, would occur this year — according to regulatory filings.
“We can’t speculate on FCA’s strategy for its U.S operations,” a Santander spokeswoman told AFN in a statement. “Santander is the preferred provider for FCA’s consumer loans and leases and dealer loans via Chrysler Capital, and we continue to operate in the existing agreement, which began in 2013.”
Ally Financial also originates for FCA. Last quarter, Ally originated $9.5 billion in auto loans — 26% of which came from Chrysler.
“Ally has created a strong business as an independent, market competitor,” a company spokeswoman told AFN. “Ally will continue to support FCA dealers nationwide with a full spectrum of industry-leading, auto finance products and services.”
The Bloomberg report sparked a reaction on the stock market in which Santander dropped as much as 9.6%, the company’s steepest intraday drop since July 2016. The stock recovered slightly, falling 6.5% on the day. Similarly, Ally erased earlier gains on the day to drop 1.2%.
However, Mark Palmer, equity researcher with BTIG, believes the investor response is overblown and notes the initial negative reaction investors had when General Motors decided to form its own captive in 2015 and diminish its relationship with Ally Financial.
“The extent and impact of GM’s shift on Ally ultimately proved to be significantly less than initially feared, and we believe such is likely to be the case with Fiat/SC if the automaker does indeed create a U.S. captive finance arm,” he wrote in a note to investors.
Carvana customers now have access to a portion of Manheim’s auction inventory, thanks to a new partnership announced today between the two companies.
As part of the agreement, Carvana can buy and sell cars on Manheim’s auction platform. Carvana uses suppliers like Manheim — and purchases from the general public — to establish its inventory of roughly 10,000 vehicles.
With Manheim, wholesale vehicles come with condition reports and pricing guidance based on insights from the Manheim Market Report.
“Utilizing Manheim’s broad remarketing experience and diverse digital solutions has helped us accelerate as demand increases for our new way to buy a car,” said Ernie Garcia, founder and chief executive of Carvana.
Carvana’s platform allows consumers to shop, finance, and purchase vehicles online. They can also schedule seven-day test runs before deciding on their final purchases.
Subprime auto finance companyHonor Finance received its second warning last week that its 2016 inaugural securitization is at risk for downgrade.
Kroll Bond Rating Agency placed the deal’s $8.86 million Class C notes on “watch downgrade” because of higher-than-expected losses and continued management turnover, according to a May 23 surveillance report.
Currently, cumulative net losses in the transaction are at 17.63%, — 5.98% higher than the expected 11.65%. Additionally, KBRA’s loss expectation for the deal at closing was between 19.9% to 21.9% based on “collateral mix at closing and the historical performance data,” KBRA noted.
Yet, KBRA’s recent analysis indicates that total losses may reach the 27%-to-29% range.
In February. Honor modified its categorization of defaults and liquidated receivables, which resulted in significantly higher defaults in the transaction. “Delinquency and extension rates are higher than we have seen in other subprime deals,” the report noted.
As of April, delinquency levels were 24.67% and extension levels were 15.82%. Bankrupt receivables were 2.29% and repossessions not yet defaulted are at 7.10%.
KBRA’s first warning was issued in December 2017. Since then, Honor’s co-founders, chief executive officer, and chief financial officer have departed the company. Honor is in the process of evaluating candidates for CEO, according to KBRA.
Honor originally securitized $100 million of loans in December 2016.
KBRA “will continue to monitor the transaction to determine whether a downgrade of the Class C” is necessary, as loan performance continues to fall short of expectations since the deal was established, the rating agency said.
The 2013 Consumer Financial Protection Bureau (CFPB) regulation, which held financial institutions responsible for potential discriminatory lending practices at dealerships, was repealed by President Donald Trump on Monday. The original 2013 CFPB bulletin was intended to address the potential for racial discrimination at dealerships by encouraging lenders to cap interest rate markup at 150 basis points, as opposed to the industry standard of 250 basis points. This was all based on disparate impact theory, which refers to practices that adversely affect protected classes of individuals, even though employer rules and practices are meant to be neutral. The CFPB used this theory to make the argument that dealer markup practices could result in unintentional discrimination during the credit process, and must therefore be reined in.
During its five-year existence, the directive prompted the implementation of flat fees, as well as millions of dollars in fines charged to financial groups in the form of consent decrees. The root of the CFPB guidance took issue with the practice of dealers placing the buyer into a higher-interest deal than the lender had originally approved, and then the dealership collects the difference.
Thanks to some fancy footwork by Sen. Pat Toomey (R-Pa.), who asked the Government Accountability Office to review the CFPB’s guidance, and Sen. Jerry Moran (R-Ks) for putting S.J. 57 onto the floor for a vote, the regulation and its guidance has ceased to exist. And if you ask some, all is now right with the world.
Groups on many sides of the situation took issue with the ruling. Auto industry trade groups argued that the bureau used its guidance to indirectly regulate the activities of dealers, which are mostly exempt from the bureau’s oversight under the Dodd-Frank Act. In addition, they argued that the guidance would ironically have an adverse effect on the groups of people it was trying to protect by limiting a dealer’s ability to secure competitive funding. Banking and financial groups reaffirmed their commitment to fair lending practices, saying they have been regulated for years and were not to blame for dealer actions that may or may not result in unintentional discrimination.
While dealer markups are back in play, it’s important to not throw the baby out with the bathwater. After investing millions of dollars in updating compliance procedures, it is unlikely that the industry will undertake a complete reversal of that investment. For example, Toyota Financial Services (TFS) was approved for early termination of its 2016 consent order, and the lender intends to raise markup caps immediately. However, they are not planning on returning to the rate markups of old. In fact, TFS plans to raise that limit to 2% for finance terms of up to 72 months and 1.5% for terms up to 84 months, according to Auto Finance News.
As a lender, what’s your next move? EFG encourages all of its lender clients to maintain their high standards of compliance and serve as a guidepost for their dealership partners. You are an expert in compliance and regulations, and your insight can be invaluable during this transition.
Don’t fall into the trap of going back to relying on rate to compete in today’s auto finance market. Instead, focus on the value you provide to your consumers. One of the best ways to differentiate your institution beyond rate is with consumer protection products, like a vehicle service contract or vehicle return protection. These products have the potential to not only increase loan volume, but also insulate your loan portfolio from the risk of default.
With more than 40 years in innovating and implementing proven go-to-market strategies in the dealership space, EFG Companies understands the balance between ensuring complete compliance, and retaining and building profit margins. That balance lies in the value proposition. Which is why EFG structures its products and services to not only provide value to you, but also to the end-consumer. Our unmatched client-engagement model goes well beyond simple product innovation to mitigating liability through superior claims processes, and continuous training and auditing practices.
With more than 40 years innovating consumer protection products that enhance profitability and customer retention, EFG Companies knows how to help lenders maintain long-term customer relationships. Contact us today to get started.
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Toys ‘R’ Us plans to shutter all 200 of its physical stores after filing for bankruptcy, an indication to retailers of the changing landscape of consumers increasingly opting for online shopping and digital platforms.
This shifting consumer mindset is not just affecting e-commerce, but also the auto finance industry — which appears to be on the cusp of a revolution as it relates to digital car buying, vehicle ownership, and automation. It has long been said by auto finance executives, including at last month’s Auto Finance Innovation 2018, that if lenders are not rethinking their business models — or at least considering innovation — they could lose marketshare. This notion particularly held true with regard to machine learning, a hot-button topic at the event.
Machine learning — a type of artificial intelligence (AI) that gives computers the ability to learn without manual input — has captured the
industry’s attention not just for underwriting purposes, but for automating customer service.
However, because the technology is so new, some lenders are wary — particularly of its regulatory implications.
There are no international regulatory standards for machine learning, and data on the growing usage of AI is largely unavailable, leaving regulators unsure about its impact on the industry, according to a report by the global Financial Stability Board (FSB), an international body that monitors and makes recommendations about the global financial system.
The FSB left open in the report whether regulation is needed, but added that “developments in this area should be monitored closely,” an idea echoed by several auto lenders.
While machine learning can be utilized for credit decisioning to potentially reduce losses, if a lender doesn’t have the manpower — i.e., a team of data scientists — or the funding to outsource this technology to a third-party provider, it is a difficult beast to tame.
However, there are other areas within the auto finance sector where lenders can utilize more of these “deep-learning tools,” Sang Kim, vice president of risk at Consumer Portfolio Services, told Auto Finance News. “Machine learning doesn’t have to be used for [credit] decisions, but for more business process improvements — which is an area where we can utilize those techniques more.”
Exploring the Use-Cases
While much of the hype of machine learning has centered on the ability to assess credit quality and more accurately predict risk, there are many other use-cases for AI.
Daimler Financial Services, for example, is combining artificial and emotional intelligence (EI) to create a new mobility experience — the digital voice assistant called “Sarah,” the captive announced in March. Sarah is a proof of concept Daimler Financial has been developing since 2017 with its partner Soul Machines, an AI solutions provider based in Auckland, New Zealand.
“The project is in the early stage of development,” Udo Neumann, Daimler Financial’s global chief information officer, told AFN. “Emotional intelligence is one of many ways Daimler Financial Services is exploring technology to further improve customer experience and to tailor information customers are seeking for their specific needs.”
Sarah will be able to assist customers like a personal concierge, with the goal to offer the right information at the right time with an emotionally intelligent, digital touch point. Sarah can be programmed with knowledge about leasing options or the latest Mercedes models, for example. Sarah can see, hear, and recognize human emotions and non-verbal actions in an interaction, such as a head nod.
“Daimler Financial Services is the first captive automotive finance organization to attempt to redefine the customer experience with digital humans using data-driven insights, AI, and EI,” Neumann said. “Even though we are in the proof-of-concept stage of combining these technologies, it is clear that this powerful combination of technology will be a gamechanger in the industry in the near future.”
Other renditions of this customer service AI concept are being tested in the market, such as through voice assistants. SpringboardAuto, which has integrated its platform with Amazon’s Alexa, offered a demo at Auto Finance Innovation 2018 showing how consumers can shop for a car and obtain financing all through the use of the Amazon Echo Dot.
Another use-case includes A/B testing. Ally Financial Inc., for example, struck an interest in machine learning and is in discussions with an undisclosed company to deploy this form of artificial intelligence to optimize its web pages, Jennifer Heil, executive director of Ally unit Clearlane, said at Auto Finance Innovation 2018.
A/B testing is a method of using statistical analysis to compare two or more versions of an app or webpage to determine which one performs better for a given conversion goal.
Heil did not offer specifics on the machine learning initiatives at Ally. Typically, uses include running AI in the background to automatically recommend the best experience for each user.
“Our tech [team] has been looking at [machine learning], and we’re actually partnering with a company — though we don’t have a contract yet — to deploy that for our A/B testing so we [can go] online and look at what’s happening in the [direct lending] funnel,” she said. There is a “learning curve” to using machine learning techniques, but Ally is “exploring those types of processes when engaging with the customers,” she added.
Machine learning can also be used to automate daily tasks in an effort to streamline processes, which is an avenue Global Lending Services LLC is exploring.
“Automation is not really about [being] a cost-savings play, it’s about speed, consistency, scalability, and compliance,” Senior Vice President of Information Technology Andy Clements told AFN.
The company is looking to better leverage machine learning to take categorization tasks humans do and provide dealers faster turn times. For example, the lender already has automation in place to detect missing pieces in auto loan applications and to catch “certain types of weird paystubs,” Clements said. “We already have quite a bit of automation on the funding side, but we are looking to continue to push the boundaries — not with a goal of removing the human, but with the goal of focusing humans on where we need them most,” such as customer service and other areas of the operation.
“In 2019, what does machine learning mean for us?” Clements asked the attendees at Auto Finance Innovation. “One [use-case] is classification of the present. Meaning, how do we use artificial
intelligence to take away some of the work our teams do that are really routine like the things that aren’t fun or sexy? Are the contracts signed? Do the names match? Being able to use artificial intelligence to remove some of that tedious work from our workforce … can improve compliance and scalability.”
AI in Underwriting
Several lenders, including Ford Motor Credit Co., have used machine learning in the past couple years to bolster underwriting.
For underwriting, computer algorithms can use nontraditional data points to determine risk and creditworthiness. For example, machine learning can assess data points, such as whether applicants supplied the same cell phone number on previous loan applications and whether they have occupational licenses. Because the platform is capable of “learning” over time, it can propose changes to variables as patterns evolve or emerge.
Last August, Ford Credit explored changes to its underwriting approval process that included the incorporation of machine learning to look beyond credit scores. The change stemmed from a study of Ford Credit customers, conducted by fintech startup ZestFinance, that measured the effectiveness of machine learning to better predict risk.
In January, powersports lease provider MotoLease LLC improved the accuracy of its internal credit forecasting model, called M-Score 2.0, by using machine learning and alternative data.
“We used machine learning techniques so that our score is not a static score, but it changes and improves based on portfolio performance,” said MotoLease’s Managing Partner Emre Ucer. “For that reason, the importance of Fico scores went down because of everything else we put into this ‘secret sauce.’”
MotoLease has been reviewing its scoring in the past year, and conducted an “extensive” study with TransUnion analyzing almost 50,000 consumer files, Ucer said. “We came up with a very sophisticated algorithm” to underwrite subprime customers differently from prime or near-prime customers, he added. For thinfile or lower credit consumers, MotoLease reviews the consumer’s payment history, past delinquent accounts, and other payment patterns from the past 82 months.
However, there are several core challenges as it relates to using machine learning in underwriting.
“In terms of the originations scorecard, we have to be cautious due to regulatory reasons,” Consumer Portfolio Services’ Kim said, adding that while CPS does not use this AI, she has used it at several other companies. “We use the originations scorecard for every application decision we make, and we have to have a very clear, transparent method. When you utilize something like machine learning, it’s a black box.” Machine learning has been touted for its ability to allow lenders to better monitor disparate impact and change the model to quickly limit unintentional discrimination of protected classes. However, the uncertainty of the algorithm could pose as a roadblock, according to Kim.
“The more data I have, the more I learn about you — usually that’s the case,” Kim said. “What happens is that I really don’t know what this computer is doing. For example, let’s say I insert 100,000 data points into this machine learning system, and it gives me answer A. Then I give the machine 1 million data points, and … now it is answer Z. The answers are totally different because now the machine has more data points to make the decision. The ending result: I could tell the second model works so much better, but the thing is that I don’t know how that machine got to point Z.”
For regulatory reasons, it’s important to be “very careful using that type of machine learning in your decision making process,” Kim said.
That being said, regulators tend to move slower than technology, said Eric Hathaway, vice president of marketing at Zoot Enterprises, an automated credit decisioning platform.
“The benefits of machine learning are that there will be an electronic trail of the transaction,” Hathaway said. “The issue comes in whether the regulators will accept these changes [in] the reduction in human interaction. But more relevant is the speed in which changes can occur in the decision process by using machine learning and AI.”
Overall, knowing when a lender can and cannot use machine learning from a compliance and regulatory perspective is important, Kim said. “Then, if there is an area that you can be more research-oriented, you can play around with a new model or tool,” she added.
The Human Touch
Despite the “black box” effect that machine learning creates, lenders can effectively navigate this hurdle with a team of data scientists monitoring and knowing how the system works, some said.
“We are entering a big discussion in this country about algorithmic accountability and algorithmic transparency, because these algorithms are proliferating at higher and higher stake use-cases,” Kareem Saleh, executive vice president at ZestFinance, told AFN. ZestFinance, founded in 2009, applies big data and machine learning to credit underwriting.
“The math is just one part of [what we’ve built], but it’s also about opening up these black-box algorithms,” he said. “This technology that we have built is not only super-predictive, but its reasoning is really transparent. We can tell you what variables the model is taking into account and to what extent.”
However, “there still needs to be a human in the loop evaluating those variables from a compliance perspective to get everybody comfortable,” he added. Notwithstanding all the leadership and regulatory changes at the Consumer Financial Protection Bureau, “there are a bunch of other regulators who care about fair lending issues … so I think anybody who is popping champagne corks because they think fair lending enforcement is done for, is premature.”
Currently, 99% of machine learning is based on human input, Zoot’s Hathaway said. “We aren’t at the point of true AI in the enterprise or society quite yet. Even in the advanced stages of AI today we continue to see errors, biases, and indecision and/or lack of ability.”
Data scientists and many other personnel are going to be needed to implement a true AI model within large organizations. “AI doesn’t just create opportunity and benefits, it also opens up new channels for fraudulent activities as well,” Hathaway added. “They will need to have strategy, manpower, expertise, and teams to implement correctly.”
World Omni Financial Corp., for example, is partnering with universities in Florida and utilizing Ph.D. candidates as part of its overall focus on data integration and utilizing analytics, Alfredo Cateriano, vice president of risk management, data, and analytics, told AFN in May 2017.
“We’re partnering with scientists, statisticians, econometricians, and operations research,” he said at the time. “But, also, we are really using this to bring individuals with expertise in machine learning.”
Through the program, students will utilize World Omni’s data to explore the use of machine learning as well as data mining, though it’s unclear in what ways the lender will utilize machine learning techniques. World Omni declined to comment for this story.
“Outsourcing is a viable option if you don’t have the expertise in-house,” Global Lending Services’ Clements said. “One of the pitfalls of machine learning is you can throw data at it, but if you don’t understand what you are throwing at it, then you will not understand the output either. The debate we have is, ‘How do we push the boundaries of machine learning but make sure we hold true to our expertise and learnings developed over lifetimes of data analysis and stats?’ I think we are starting to find ways to leverage more and more machine learning, but you have to be careful how you do it because of the pitfalls of understanding your data.”
Car-sharing platform HyreCar is looking to go public, according to a filing with the Securities and Exchange Commission on Wednesday.
HyreCar intends to offer 2 million shares, with an additional 300,000 set aside for underwriters, at $5 to $6 a piece. Network 1 Financial is currently the sole underwriter.
As a peer-to-peer car-sharing marketplace, HyreCar allows car owners to rent their idle cars to drivers for rideshare companies, such as Uber and Lyft.
HyreCar’s competitive edge is that it places more cars on the road to meet growing demands by providing an option for owners who do not have their own qualifying vehicles to work for ridesharing services.
“In some more established markets, Uber is struggling to keep enough cars on the road to meet the demand — and that’s a problem,” HyreCar noted in the IPO prospectus. “It means cars either aren’t available, or if they are, there are longer wait times and lower overall satisfaction with the service.”
HyreCar’s driver base shot up 318% last year, to 4,430 drivers from 1,060 in 2016, according to the filing. However, the company’s rapid growth since its inception has served as a challenge, and its limited operating history also makes it difficult to evaluate its current business and prospects — serving as just one of the risks listed in the prospectus.
HyreCar, formed in 2014, is based in Los Angeles, with operations in Atlanta, Chicago, Dallas, Philadelphia, San Francisco, Washington, D.C., and several other cities.
The Cox Automotive subsidiaries Autotrader and Kelley Blue Book are partnering with online direct-loan marketplace LendingTree in an effort to provide consumers an all-in-one tool to streamline the online car-buying process from research to financing, the companies announced in a press release today.
Senior Vice President of Product for Cox Automotive Jai Macker said the comprehensive program provides new and used-car information including finance options for a customers’ specific needs.
“Bringing LendingTree’s capabilities to Autotrader and Kelley Blue Book will help new- and used-vehicle shoppers by creating a seamless experience to research, shop for and secure financing on a vehicle all in one spot,” Macker said.
The program collects a consumer’s credit information and provides options for the customer to choose from — beginning with loan type and ending with the type of car desired. The portal does not provide a live inventory of cars, but consumers can research the vehicle they want and apply for a direct loan.
Once completed, the program will filter lenders based on the loan amount and type of car desired. Then the potential buyer can choose from the matched lenders and connect via phone and email to continue the process. LendingTree’s technology is now available on both Kelley Blue Book’s and Autotrader’s websites.
“Together with Autotrader and Kelley Blue Book, we are able to provide 36 million-plus shoppers the information they need to find and compare rates and terms from multiple lenders, all while online,” Dimitar Alexandrov, vice president of automotive for LendingTree, said in the press release.
The website also provides an auto loan calculator tool that helps visitors determine monthly payments based on loan amount, interest rate, and loan term.
