Efforts by the Consumer Financial Protection Bureau (CFPB) and other would-be advocates to police the fairness of auto loans have accelerated in recent years. Since 2013, the CFPB has recovered more than $220 million from several large car-financing companies to settle claims of discriminatory lending practices.
And even though the agency doesn’t have direct enforcement authority over franchised dealers, the CFPB’s recent actions could foretell a new wave of attacks by federal and state regulators and private attorneys to sue dealers for the way finance rates are set. Simply put, it is imperative that dealers have policies and processes in place to comply with fair credit laws.
Existing compliance policies provide helpful guidance. Yet despite the best efforts of many dealerships, written credit compliance policies that call for standardized processes are routinely replaced in the day-to-day operations with subjective decisionmaking done on a customer-by-customer basis.
And while there may be no intent to discriminate, the fact that these written policies have been largely overridden by subjective decisionmaking makes it all the more problematic for a dealer if a statistical evaluation of past credit transactions shows a disparity in finance rates paid by consumers in a protected class. This article examines some continuing areas of exposure for dealerships and offers suggestions for a more comprehensive approach to fair credit compliance.
1. Existing Compliance Programs
In January 2014, the National Automobile Dealers Association (NADA) and the American International Automobile Dealers Association (AIADA) released a Fair Credit Compliance Policy & Program and made it available to their members. The NADA/AIADA Program recommends that dealers establish a preset amount for the finance reserve, like a fixed number of basis points over the buy rate. Dealers can then allow for a documented downward adjustment of that amount should a predetermined condition occur.
The NADA/AIADA Program does not attempt to address every issue that potentially relates to fair credit compliance at a dealership, and it specifically acknowledges desking procedures as an area that must be considered separately by dealers as part of a broader, comprehensive solution to fair credit compliance.
To address the desking piece, most lawyers and compliance experts advise dealers to establish and use a credit score drive “rate matrix.” A typical rate matrix divides car buyers into 50-point credit score tiers (over 700, 650 to 699, 600 to 649 and under 600) and then assigns a rate using the captive finance company’s buy rate for each tier while adding a standard markup of 100, 150 or 200 basis points. So in practice, every customer who has a 625 credit score should be quoted the same rate.
From there, the final transaction becomes a negotiation. If the dealer agrees to lower their standard markup for legitimate business reasons — and if the dealer has implemented the NADA/AIADA Program — then the change will be documented to mitigate any finding of disparate impact discrimination under fair credit practices requirements.
2. The Challenge of Implementation
It may be unrealistic to assume that most franchised dealers would be able to effectively implement a rate matrix. This is because the use of a rate matrix is predicated on the dealer knowing the buy rate at the time the dealer makes the offer to the customer.
But the buy rate is not known until after the deal is typed and submitted to the bank for approval. Prior to that point, the dealer is making a “best guess” at the rate at which the lender will buy the contract. For the prime credit score customer buying a new vehicle, there is little needed guesswork. But in most scenarios, the guesswork is more involved. A recent monthly snapshot from one of the captive lenders shows that it approved 97% of its Tier 1 applicants. For Tier 2, the approval rate fell to 78%. The approval rate clocked in at 63% for Tier 3 customers and 47% for Tier 4.
The limitation with a credit-based rate matrix is that it omits important variables that factor into lender approvals. The buy rate will be affected by a variety of deal specific variables, such as debt-to-income ratio (DTI), payment-to-income ratio (PTI), and loan-to-value (LTV) percentage.
For example, your 640 credit score customer with income of $3,000 a month and a LTV of 108% because of a negative equity trade will receive quite different approval options than your 640 credit score customer with income of $7,000 a month and a $5,000 down-payment. Adding to the challenge is that franchised dealers have many different indirect lenders to work with, and each lender has its own criteria for lending. Your 600 credit score first time buyer with less than 3 years in the bureau will have different approval options than your 600 credit score customer that has a discharged bankruptcy and 10 years in the bureau.
As you know, the credit score is just one of many factors that determine the eventual buy rate, and the combination of subjective and objective factors makes it nearly impossible to know before obtaining actual lender approval whether the consumer’s loan request will be granted by a given lender.
3. The Role of Subjectivity and Employee Discretion
An experienced sales or finance manager knows how to read a credit bureau, knows the right interview questions to ask the customer, and has knowledge of a variety of lender programs. She knows to look beyond just the FICO scores and to look at factors such as years in the bureau, number of outstanding trade lines, and employment history. She knows which lenders are most likely to approve her C-minus credit customer with a recently discharged bankruptcy, and knows when her B-plus credit customer with a negative equity trade will fall outside the prime lender’s advance guidelines and will need to go to a secondary lender at a higher rate.
The fact is that each deal is unique and requires a sophisticated understanding of credit reading and a wide variety of lender programs. It is also the reason why dealers are entitled to earn a profit in helping to secure financing for its customers, and why experienced sales and finance managers are in high demand.
But uncertainty still exists with even the most experienced sales and finance managers. When dealing with an uncertain outcome, a capable sales or finance manager may structure a deal with two or three lender programs in mind. For example, given a particular deal structure and customer credit profile, the finance manager might anticipate receiving an approval from Wells Fargo at 11.9% or Capital One at 13.9%. So if we assume that this dealer’s preset standard participation rate is 2%, an approval may come back from one of these lenders with dealer participation falling somewhere between zero and two points depending on how the deal is structured.
Alternatively, of course, the lender may turn down the deal as it was structured resulting in an unwind and recontract (if the customer will agree to the new terms). Successful sales and finance managers seek to structure deals in a manner that satisfy the customer, limit unwinds and provide a fair profit to the dealer. So while every manager will have the inevitable unwinds and deals with zero percent reserve, those deals will be the exception to an otherwise profitable department with high CSI.
Notice these objectives are no different when it comes to dealing with the A-plus credit customer. But because the buy rate for the prime customer is known with more certainty, it is less likely that dealer participation will remain at the dealer’s preset standard participation rate. It is simply the law of probability: Greater uncertainty brings greater statistical variance. Stated differently, disparity in dealer participation — particularly on nonprime deals — is often attributable to the uncertainty of knowing the buy rate before the contract rate is negotiated with the customer.
So while you may never intentionally discriminate against customers with lower credit scores, the fact that it is harder to predict the buy rate of credit-challenged customers leads to a greater disparity between buy rate and the contracted APR. And this statistical variance may give rise to post-transaction claims that such disparities are attributable to a customer’s background (i.e. the customer’s status as a member of a protected class) and therefore in violation of the Equal Credit Opportunity Act (ECOA).
4. The Dealer’s Dilemma
Existing compliance programs leave the dealer with a choice: Choice A is to demand strict adherence to a rate matrix and disallow employees from considering any factor other than a customer’s bureau score in setting the rate. If the deal is turned down or the callback is at a higher rate, then the contract would be unwound and the dealer would attempt to make a new deal with the customer. Notice here that, unless you also eliminate spot deliveries, this process would destroy CSI and likely get you in even hotter water for what consumer advocates like to call the “yo-yo” transaction: contracting the customer at a rate that is unlikely to be approved, with the anticipation of recontracting the customer on different terms.
Choice B is to allow experienced sales and finance managers to continue relying on their knowledge and judgment to take a “best guess” at the rate at which the customer will be approved. But when one 655 credit score customer is contracted at your matrix rate and another 655 credit score customer is contracted at a rate that is one or two tiers below, will you be in any better position to defend against allegations of discrimination?
In order for a dealer to implement an effective fair lending compliance program, you should not only have a good process for documenting any disparity in dealer participation rate; you should have a standardized process that can explain pricing disparities in wholesale rates, and therefore the contract rate, in the event that a court, governmental enforcement agency, or indirect auto finance source is monitoring the dealer’s credit contracts and seeks to question disparities among customers with similar credit scores. Although recent actions by the CFPB have largely focused on the disparities in participation rate, dealers and indirect lenders would be well-advised to ensure that their fair lending policies account for varying wholesale rates among customers of similar credit scores.
5. The Missing Piece
The ideal complement to the NADA/AIADA program would be a rate matrix not based solely on credit scores, but one that takes into account the relevant credit history information that may factor into the lender approvals (such as positive trade lines, revolving credit balances, and bankruptcies) as well as the relevant variables of each deal (such as LTV, DTI and PTI). In other words, a rate matrix that essentially mimics the knowledge and skill of an experienced sales or finance manager. There is at least one software provider that has recently introduced such a system.
With a more “intelligent” rate matrix, dealers can significantly reduce subjectivity and employee discretion in setting rate and thereby provide added protection against claims of discriminatory lending practices. Through technology and the use of well-studied algorithms, dealers can more confidently structure deals at the desk using an objectively calculated and highly predictable rate based on the non-discriminatory credit profile characteristics of each deal.
The scoring system can be electronically archived with the deal to be accessed at a later time if necessary to serve as evidence as to the dealer’s objective process for determining a loan rate for its customers. In an era of litigation and regulatory enforcement based largely on comparable analysis that appears to show discriminatory practices, you would be well-served by having at your disposal such an objectively structured and technologically sound scoring system.
Michael Maledon is an attorney specializing in dealership representation. From 2001 to 2011, Michael served as general counsel of The Van Tuyl Group (now Berkshire Hathaway Automotive), one of the country’s largest dealership groups. [email protected]