Written by Andrew MacDowell
Offering longer-term auto loans may be an attractive option for lenders, but the practice carries inherent risks that may not be immediately apparent. The proper application of data science to plan, model and analyze results is critical to managing these risks and maintaining reliable revenues from loans with extended terms.
As every lender already knows, the auto lot in the U.S. is already an enormous market and still has vast potential for growth. According to data from the most recent (November 2017) U.S. Consumer Finance Protection Bureau (CFPB) report, by the latter part of 2016 there were 100 million auto loans outstanding totaling more than $1 trillion. Ninety percent of U.S. households own a vehicle. Of the vehicles purchased every year, about 86 percent of new vehicles and about 53 percent of used vehicles are financed.
Responding to the high demand for auto loans by loosening credit standards and offering longer terms can lead to rapid short-term revenue gains and capture a wider segment of the market by catering to customers with lower FICO scores. And the pattern of auto lending in the past few years suggests many lenders have, at least until very recently, considered it a competitive necessity. Data from the same CFPB report shows that the proportion of total auto loans with terms of six years or longer rose from 26 percent in 2009 to 42 percent by mid-2016.
The CFPB identified a number of risks associated with longer-term auto loans. While the focus of the bureau’s analysis was on risks to consumers, in keeping with its mandate, the corresponding risks to lenders are obvious. First, longer-term loans are generally larger; the average loan amounts for six and seven-year loans are $25,300 and $32,200, respectively, compared with $20,100 on average for a five-year loan. Longer-term loans are also more expensive; a borrower will pay more in interest and have a larger outstanding balance over a same time period with a longer-term loan than a shorter one.
These are not necessarily negatives for lenders, of course, but other factors come into play to negate many of the advantages and increase the risks. The average FICO score of six-year borrowers (674, according to CFPB) is about 39 points lower than that of five-year borrowers. And since the average ownership lifespan is 6.5 years, many borrowers on 72 or 84 month terms will have an outstanding balance on a vehicle they no longer own. And if loans happen to end in the last quarter of the year, the chances that they will not be paid increase dramatically. Delinquency rates over the past several years have been 0.5 to 0.6 percent higher in the fourth quarter than in the first quarter of the year. All of this combines to make default rates on longer term loans roughly twice as high. The CFPB found that default rates for loans of six years or longer exceed 8 percent, compared with about 4 percent for loans up to five years.
While many lenders are still offering longer-term loans and actively pursuing new customers, concerns are growing about the viability of the practice, and a significant number of lenders are pulling back. In their reports to investors at the end of 2016, Capital One, Citizens Financial Group and Huntington Bank – the nation’s third, eighth and 10th largest auto lenders, respectively, all indicated they were reducing their exposure in auto lending generally, and in lending to subprime borrowers specifically.
Opportunity and Challenge
In a sense, reduced enthusiasm for longer-term auto lending among some industry players creates market opportunities for others willing to take on the challenge of serving riskier customers, but it is a challenge that must be managed carefully. The key is risk adjustment. Being able to analyze and predict loan outcomes for potential borrowers, and in order to do that efficiently and effectively, a lending platform specifically built with features and functionality that allows lenders to manage and integrate customer data is a critical need.
To be effective, the lending solution must be able to capture customer data across platforms, including credit profile and history as well as the customer’s internal records with other lender products; organize and maintain the records in a way that is both scalable and aligned to compliance and accounting requirements; and automate many processes and decisions, using the integrated database as a “single source of truth” for any transactions. Such a system, particularly with its automated features, can be easily configured to provide analysis of potential loan outcomes for individual customers in minutes. The lender gains the advantages of faster, more consistent and guaranteed-compliant decision processes and originations, cutting down on man-hours and processing costs, while customers benefit from being able to receive fast, accurate responses to their loan applications.
For small and medium sized lenders interested in selling longer-term auto loans and diving deeper into the subprime borrower market, I would be happy to talk to you about how we are helping others manage this particular risk. Feel free to reach out to me at email@example.com ~ I would love to hear your thoughts on this topic.
About the Author:
Andrew MacDowell has over two decades of senior management experience in the credit card industry with Fortune 500 financial institutions such as MBNA Corporation and Bank of America.
Andrew has specific expertise in areas such as Business Development, Loyalty Marketing, Corporate Project Management, Bank Operations, Payments, and Fraud. Most notably, Andrew was a key founding stakeholder of MBNA Canada during its peak growth phase in the Canadian marketplace, which ultimately led to it becoming the largest MasterCard issuing bank in Canada.
Andrew is a graduate of Georgian College where he holds a diploma in Business Administration and majored in Marketing Management.