Global Trends Across Auto Lending

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Global Trends Across Auto Lending

Auto lenders today face a rapidly changing marketplace as consumer behaviors evolve, regulatory requirements shift, and sources for auto financing continue to increase. The world’s leading lenders are staying ahead of their competition by ensuring that their analytical capabilities surrounding pricing and product personalization are not only robust but also nimble enough to rapidly adjust to industry shifts.

Across Europe and Asia, regulators are introducing new restrictions around the role that dealers play in the loan origination process, while also raising concerns about borrowers’ ability to repay. In the United States and Canada, average terms for auto loans are growing steadily longer, creating new opportunities to grow revenue, but also changing the risk and profitability landscape. Proactive lenders who are ready to embrace and take advantage of this evolution will be well-positioned for the increasing complexity of future business.

This paper will explore both the changes in the regulatory environment as well as consumer behavior and will then delve into the opportunity lenders have to advance their pricing and product personalization processes.

Regulatory changes in Europe (the UK) and Asia (Australia) are altering traditional relationships between lenders and car dealers. In both countries, regulators are increasingly cautious of dealers adding discretionary markups on top of the interest rates quoted by indirect lenders. A long-standing business practice, changes to this structure will impact dealers and lenders on several levels. Changes in the relationship between the price quoted by the lender and the customer’s ultimate decision to book a loan will take place. Additionally, a change in the dealer’s incentive structure will almost certainly lead to a change in their behavior towards both customers and lenders.

Likewise, there is also a broader concern about lenders offering loans that borrowers will ultimately be unable to repay. Regulatory agencies worldwide have made it clear that further restrictions on pricing may be just around the corner, introducing an additional level of complexity into the pricing process.

Executive Summary Challenge: Regulatory Changes

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Dealer behavior has always been a critical component of the price-profitability relationship for auto lenders. While the lender has control over the price they quote to the dealer, this is often not the final price that the borrower sees, due to the discretionary markups and changes commonly allowed. Lenders today are embracing advanced statistical or machine learning models that can be used to predict dealer behavior at a much more granular level. Additionally, the lender can more accurately understand what price the customer will be responding to, and thus more accurately predict whether or not a customer will book a loan.

Near term regulatory changes will undoubtedly result in changes in dealer behavior. Lenders must be ready to quantify these changes by ingesting and analyzing new data and then rapidly incorporating it into their modeling frameworks and pricing analyses.

Regulators have also expressed concern about the affordability of car loans. For example, the Australian Securities and Investments Commission (ASIC) recently fined BMW $77MM, finding that “BMW was not meeting its responsible lending obligations properly and providing loans to consumers who could not afford them.” (Main, 2019) Given the disastrous impact of irresponsible mortgage lending leading up to the financial crisis of 2008, this scrutiny is unlikely to subside, with further regulations likely. As lenders will undoubtedly face increased constraints on pricing, reserving and stress testing, it is critical that they can incorporate regulatory constraints into their pricing processes.

Dealer Behavior Increased Scrutiny on Affordability of Car Loans

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Figure 2 - Mid vs. Long-Term Auto LoansAccording to Edmunds analyst Jeremy Acevedo, consumers with terms between 67 and 72 months borrow an average of $33,328 at an average rate of 6.6%, compared to consumers with terms in the 61-66 month range, who borrow an average of $29,591 at an average rate of 4.1% (Reed, 2019).

While longer-term loans generally come with higher interest rates and loan amounts, increasing both Volume and NII, they also expose the bank to more risk, including interest rate risk, prepayment risk. and default risk. While many banks can model these components separately, it is critical that they also be able to view them holistically. This multivariate risk analysis must be seamlessly integrated into pricing analysis and decision-making at the lender level.

The average term or length of an auto loan has been steadily increasing. Industry Research Data from Experian shows that the proportion of 72-month auto loans has increased from 10% of all new car buyers in 2008 to 24% in 2018 (Tatham, 2018), and that 32% of borrowers are signing auto loans with even longer terms between 73 and 84 months (Reed, 2019).

Longer-term loans can expand options for borrowers through more manageable monthly payments, enticing them towards larger loan amounts. These longer-term loans tend to also have higher interest rates, which can accelerate growth in Net Interest Income (NII). The result is that many consumers are taking out larger loans at higher interest rates. For example, the chart below shows the monthly payment for a $31,000 loan financed at 5.1% over different term lengths:

Challenge: Consumer Behavior Changes

Figure 1 - Comparing Monthly Payments Across Loan Terms

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There are also significant risks associated with increasing loan terms – car buying customers may find themselves over-extended and likelier to default. Banks are exposed to more interest rate risk with a longer-term commitment of funds to the car buyer. Trade-ins on these loans often occur several years before the loan is fully paid, which leaves an outstanding balance on the loan. This leaves lenders needing to rethink their methodologies for quantifying and managing prepayment risk.

Managing Risk Quantitatively

Traditional Prepayment Approach

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Different types of prepayments have been divided into categories and modeled separately in the past. For example, a lender might have one analytical model for borrowers who increase their monthly payments to more quickly pay down principal, and another model for full prepayment via trade-in or refinance (Golden, 2018). Prepayment models often rely heavily on financial factors, with less weight placed on external behaviors like borrower characteristics and past payment behavior (Saito, 2019).

This dual approach methodology is economically intuitive and allows modelers the ability to tune parameters based on business judgment and well-understood financial metrics. However, its simplicity can often overlook more complicated relationships between borrower behavior, financial indicators, and prepayment. This becomes critical as loan terms increase, bringing with them trade-ins that occur earlier in the life of the loan and often with negative equity on the vehicle.

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Transforming your pricing processes to account for regulatory and consumer behavior changes is critical to maintaining profitability and volume growth in an evolving lending marketplace. Auto Finance lenders must advance from legacy systems and processes to an agile, controlled, and auditable process. Technology is rapidly transforming nearly every facet of the auto lending industry - from aggregator platforms to peer-to-peer lending models, as financial institutions are facing both competition from outside as well as a technological arms race from other traditional lenders (Dany et al., 2018).

There are two ways that lenders can advance to a more effective personalized pricing process: the first is by moving away from a legacy rate sheet system and the second embracing of machine learning modeling techniques.

Technology is transforming the ability to leverage behavioral data and modeling in the pricing process. Traditionally, “rate sheets” have been used to manage pricing by customer segment. Many lenders clearly understand the powerful implications of personalized pricing, but they are often limited by legacy and siloed systems as well as limited organizational analytical capability.

Bringing more advanced models such as machine learning models into a systemized pricing process affords lenders the ability to identify relationships and interactions that might not be identified by more traditional statistical analyses. Recent research has shown that methods such as artificial neural networks and random forests outperform traditional statistical methods in predicting prepayment behavior (Saito, 2019).

The complexity of machine learning models can often make them difficult to incorporate into scenario analyses. Earnix has solved this problem by offering multiple methods for the integration, deployment, and operationalization of machine learning models into the larger analysis and personalized pricing process. This can be done through the native machine learning capabilities of the Earnix 3D Personalization Suite, or model importation from external modeling environments, such as R, Python, or DataRobot. In either instance, the integration is as quick and simple as creating a traditional statistical model or lookup table. Earnix’s visualization and scenario testing functionalities make it quick and easy to understand non-intuitive coefficients from machine learning models and interpret them quickly. Those who are well-positioned to quantify these risks with technology and nimbly incorporate them into their pricing strategy will win. These changes in the market create tremendous opportunities for volume and revenue growth. However, it requires doing away with established assumptions about consumer behavior and rapidly incorporating new data and new modeling techniques into pricing analyses.

The Earnix 3D Personalization Suite allows modeling and pricing teams to deploy cutting-edge machine-learning models into the analytical personalized end-to-end pricing process in a near real-time manner. This streamlines an often complex and labor-intensive task, allowing the lender to immediately leverage incoming market and competitor data to create a personalized product bundle for the dealer and in turn, each car buyer. Users can make these updates themselves, without IT involvement or system integrator and consulting dependency.

Opportunity: Advancing your Pricing Process

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Meziani, A.. (2011). Modeling Prepayment Behavior In Financial Transactions Backed By Automobile Loans. Journal of Applied Business Research (JABR). 18. 10.19030/jabr.v18i3.2144.

Heitfield, E. & Sabarwal, T. The Journal of Real Estate Finance and Economics (2004) 29: 457. https://doi.org/10.1023/B:REAL.0000044023.02636.e6

Saito, Taiyo. (2018). Mortgage Prepayment Rate Estimation with Machine Learning. Delft University of Technology. https://repository.tudelft.nl/islandora/object/uuid:500f39d8-bf25-4c80-8346-d2d7978b4c48/datastream/OBJ/download

Golden, Patrick. (2018) Man vs. Machine: The Prepayment Modeling Story. https://riskspan.com/news-insight-blog/man-vs-machine-prepayment-modeling/

Dany, Oliver et al. (2018) Staying Ahead as Auto Finance Goes Digital. Boston Consulting Group. https://www.bcg.com/en-us/publications/2018/staying-ahead-auto-finance-goes-digital.aspx

Secuianu, Miruna (2019) Differences Between Canadian and US Car Loans. MEDICI. https://gomedici.com/differences-between-canadian-and-us-car-loans

Main, Liz (2019) ASIC to put brakes on car finance. Financial Review. https://www.afr.com/companies/financial-services/asic-to-put-brakes-on-car-finance-20190116-h1a42m

BBC (2019) Car financing crackdown ‘to save drivers £165m’. https://www.bbc.com/news/business-50052375

Eisen, Ben & Roberts, Adrienne (2019) The Seven-Year Auto Loan: America’s Middle Class Can’t Afford Its Cars. The Wall Street Journal. https://www.wsj.com/articles/the-seven-year-auto-loan-americas-middle-class-cant-afford-their-cars-11569941215

Tatham, Matt (2018) Sticker Shock is Real: Car Payments Hit a Record High! Experian. https://www.experian.com/blogs/ask-experian/sticker-shock-is-real-car-payments-hit-a-record-high/

Reed, Philip. (2019) 5 Reasons to Say No to 72- and 84-Month Auto Loans. Nerdwallet.com https://www.nerdwallet.com/blog/loans/5-reasons-say-no-long-lo/

Sources

Auto lenders vary greatly, not just in terms of size, but also regulatory environment, risk appetite and target market. When it comes to both risks and opportunities, an indirect, near-prime lender in the United States may look very different than a captive lender in the UK. However, they are both seeking to earn revenue and minimize risk in a competitive, regulated and rapidly-evolving environment. As such, it is no longer sufficient to rely on siloed quantitative analyses and “accepted wisdom” based on outdated legacy processes and industry norms.

Today’s successful auto lenders must be able to rapidly identify and quantify the changes taking place in both local and global markets and apply these learnings as a core component of their modeling framework and pricing strategy. This calls for a pricing framework that is not just

comprehensive – incorporating consumer and dealer behavior, financial risk and competitive landscape – but also flexible enough to quickly deploy and execute on the latest modeling techniques and changes in the business landscape.

Earnix Price-it offers pricing and modeling teams not only industry-leading personalization algorithms, but also the flexibility and transparency to quickly deploy cutting edge machine learning models and immediately incorporate them into comprehensive scenario analyses. Without implementation delays or the opacity of “black boxes,” Earnix allows users to precisely align pricing with business goals while outpacing less-equipped competitors in the marketplace.

Conclusion

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