In previous posts we have emphasized the importance of customer service as an aspect of managing fair lending risk. Since fair lending deals primarily with treatment of applicants, and the agencies recognize that discrimination can take place at any aspect of the transaction, inconsistency in levels of service could constitute fair lending risk. Consequently, having good customer service that is consistent among all staff is an important piece of risk management.
In a speech earlier this year, FDIC Chair Jelena McWilliams provided some insights concerning her vision with regard to bank regulation.
As a New Orleans native, we are very proud of our cooking and food – and, for good reason. My grandmother had to be one of the best cooks that ever walked the face of the earth. No matter the dish, whatever she was serving up was unbelievably good.
A regulatory examination of lending activity, whether fair lending related or safety and soundness, always focuses on data. Data integrity notwithstanding (which is another blog post entirely), such data is a function of (2) things: (1) policies and (2) actual practices. The interaction of these two forces creates the lending data that will be the subject of a regulatory examination. These data, in turn, will shape the ultimate outcome of the review.
All of us are familiar with the term “perfect storm.” A perfect storm can be defined as the occurrence of a highly improbable event. In the context of the perfect storm, the event is improbable because a combination of factors or conditions have to occur or exist either simultaneously or in a particular sequence in order to produce the event. It is the unlikely nature of the simultaneity of multiple factors or conditions that produces the “perfect storm.”
On December 6, the FDIC announced actions to promote a “more transparent, streamlined, and accountable deposit insurance application process” to encourage the establishment of new, or de novo, banks.
Your fair-lending regression results indicate a statistically significant disparity… now what? In our last blog post, we discussed the importance of a common-sense approach to statistical analysis. One common error in statistical analysis is to assume that a result is practically meaningful just because a result is statistically different from zero. This in not always the case. In fact, finding a statistically significant result may or may not be meaningful.
As fair lending analysis becomes increasingly technical, industry practitioners have had to familiarize themselves with the terminology of statistical analysis. Statistical significance is one of the most common and foundational concepts to successfully navigating these new waters. Moreover, it is a concept that, when misunderstood, may result in serious error.