Fair Lending laws and regulations prohibit discrimination on prohibited bases, including race, gender, age and ethnicity.
Laws, such as the Equal Credit Opportunity Act (ECOA) and the Fair Housing Act (FHA), require equitable treatment of all customers. The regulatory and enforcement agencies generally recognize (3) forms of discrimination in lending: (1) overt, (2) disparate treatment, and (3) disparate impact.
Note that the regulations emphasize equal and fair treatment, not preferential treatment. However, some institutions, in their eagerness to eliminate fair lending risk (or to try and avoid regulatory scrutiny), may be tempted to favor protected classes in lending. This may take the form of favorable underwriting/pricing practices for protected classes or attempting to favor protected classes when granting underwriting/pricing exceptions.
Such efforts may be well-intended and, at least partially, consistent with the spirit of fair lending laws and regulations. But, it may actually create fair lending risk rather than eliminate it.
Newton’s Third Law
Transliterating Newton’s Third Law of motion, every action has an equal and opposite reaction. The first rule of policy analysis is similar in that any type of policy action will likely have both some intended and unintended consequences. That law is applicable here. An institution may see favoring protected groups as a form of insurance, hoping that it may insulate them from potential issues with fair lending. However, it may have the opposite result.
Although there are a number of issues that could arise from such a practice, we focus on one aspect here that is particularly relevant to fair lending analysis.
In determining whether there is evidence of a financial institution discriminating, a regulatory examination’s analysis compares protected classes to non-protected classes. For example, females would be compared to males, and racial and ethnic minorities would be compared to non-Hispanic whites. The critical issue to bear in mind is that these comparisons are conducted independently.
For example, suppose that a well-intentioned institution deliberately and systematically favors a protected class in granting pricing exceptions. A subsequent fair-lending analysis would likely show no evidence of discrimination toward borrowers of that class. But, what will the analysis show with regard to borrowers of other protected classes?
Protected classes overlap. So, an institution may grant frequent pricing exceptions to females and not realize that Hispanic borrowers are disparately impacted. Or, an institution may grant frequent pricing exceptions to racial minorities and not realize that females are disparately impacted.
An Example
Let’s suppose that an institution made it a point to make more pricing exceptions to Asian borrowers than to white, non-Hispanic borrowers in an attempt to minimize fair lending risk with respect to Asians. A fair analysis of these groups would likely show no adverse impact on Asian borrowers with respect to its pricing practices.
Now let’s further suppose that the distribution in terms of male to female overall is 50-50. However, for Asian borrowers the sample is 80% male and 20% female. It is easy to see that due to favoring Asian borrowers the institution will also unintentionally favor males. There are dozens of possible scenarios, but this simple example is sufficient to make the point. Although it may be possible with a lot of thought to “outsmart” the numbers, these distributions will change over time. Therefore, doing so the numbers will be increasingly complicated and risky over time.
No Shortcuts
The point here is that there really are no shortcuts with regard to mitigating fair lending risk. It should be pointed out as well that there has been at least one regulatory action for so called “reverse discrimination” in which an institution was cited for intentionally favoring protected groups (see https://www.americanbanker.com/news/unusual-occ-order-hits-bank-for-discriminating-against-white-males).
An institution should create objective policies and manage the process. In essence, “Do it right.” Although fair lending risk cannot be totally eliminated, this is the best way to mitigate risk.
How to cite this blog post (APA Style):
Premier Insights. (2018, August 15). The Fair Lending Risk of Good Intentions [Blog post]. Retrieved from https://www.premierinsights.com/blog/the-fair-lending-risk-of-good-intentions.
