Today, August 1, 2017, marks the beginning of the 23rd year of Premier Insights. As I look back over the last two decades I am very grateful – thankful for our success and for the many clients and other tremendous individuals and firms we have had the opportunity to work and partner with over the years. It has truly been a blessing and one that I am thankful for each and every day.
A fundamental assumption in fair lending regression analysis is that the model is correctly specified and contains all the relevant variables.
Regression analysis is a powerful tool for assessing fair lending risk and performance. As is the case with any tool, however, it must be understood and used correctly. This includes understanding the limitations which in turn defines what conclusions can be drawn from an analysis.
When preparing to conduct a fair lending regression analysis, the first step is to determine the loan sample to be analyzed. This is usually accomplished by first selecting a particular loan product on which to focus and a time period.
For decades, banks and those of us who have been involved with regulatory compliance have objected to subjectivity that makes managing compliance matters difficult.
In Part 1 of this post I raised the issue of the new HMDA to be reported March 1, 2018, and how this has the potential to alter the fair lending landscape by increasing the level of risk.
Below I detail some of the reasons why I believe this is the case. In a future article, we will discuss steps that can be taken to be prepared and lower exposure to the increased risk .
The update to HMDA reporting is just around the corner, and while most institutions are probably prepared for the data reporting changes, this does not mean those institutions are actually ready. In this series of articles on HMDA 2018, we'll discuss the fair lending risk implications of the new data that will be made available to regulators and the public.
Several years ago, I began a conference presentation by making the statement: “To be successful at fair lending, you must learn how to properly discriminate.”
The statement was obviously meant to be provocative, and it must have worked because it was quite some time before I was invited back (just kidding). The statement, however, is accurate; and the point that is being made is absolutely critical to fair lending compliance. However, explanation is necessary.
Understanding the difference between Z statistics and T statistics for fair lending analysis can be frustrating, even more so when it comes to determining which test to use. Let's take a more detailed look into this world of statistics.
Understanding and managing loan policy exceptions is critical to a financial institution’s fair lending compliance. In this article we explain policy exceptions and describe the different types of exceptions. How an institution defines policy exceptions is also critical and will be the subject of another post.