was successfully added to your cart.

Cart

There comes a point where too much speculation and too much information result in chaos that does not serve any human being well, despite race or ethnicity.

In an effort to tag a label to all of us, the CFPB announced how it is going to use proxies and complex statistical models to put all of us in our place. If you are interested in reading it, I’ve attached it below.

As all of you know, I am all about giving everyone equal access to credit, provided they are able to demonstrate that they can pay the money back. I also realize that fair lending goes much deeper than that, into biases and predatory elements in our society. Unfortunately, many of those predators are ethnic on ethnic, as humans like doing business with those they perceive to be most like themselves. For some reason we believe that we can trust those like us more. Wrong? Yes, but unfortunately still true.

First we started with HMDA data and “guessing” if the client did not provide that information. That technique still leads to a mess of misinformation.

I realize that a brainiac in Washington thought this would be a good method for assigning race and ethnicity to those who are not required to report HMDA data in lending, but …..

Now, the CFPB is going to use complex psycho-babble mathematics to put all of us into a category based on our name (first, last), our address and the composition of those we live around according to the census amongst other data bases.

I ran my own name and my probability is that I am a black woman.  I then ran a friend of mine whose name is “Meredith” and HE came up as a SHE. Same thing happened with “Dayle”, who is a SHE and came up as a HE. I can’t even imagine how some of the more unique names come out.

I also had a conversation with my oldest son’s girlfriend (who I hope one day will be my daughter in law). Both of her parents were born and raised in Mexico. They came to the US and became citizens and gave birth to her. She was upset that because her last name sounds Hispanic (because she is) that her cell phone company kept changing the view of her website to Spanish, and sending her information in the mail in Spanish, despite the fact she has called them many times. She is highly fluent in Spanish, but English is her first language. This is just one example of the melting pot that we live in which is why I do not like labels; Race, Ethnicity or otherwise. I have so many other examples, and I’m sure many of you do too.

When people start seeing each other beyond the house they live in, their job, what they wear or their skin color, maybe we can just treating each other as a human being, instead of plopping them into a label. After all, it won’t be long before we are so mixed raced that even a statistician won’t be able to figure it out.

My solution to the fair lending debacle is simple and easy:

  1. Individual Consumer: All applicants have a set of data tied to them. Don’t ask anyone what they are and see how the lender performs based on the analysis of just the data. Then at closing, get the information from the client or “based on visual observation” from the closing office. This way all decisions were made without any labeling information.
  2. Disparate Impact, Redlining and Aggregated Data: Make it clear! Draw a 10-20 mile radius around every office and make sure under-served areas are as well served as others. Then look at the MSA and determine if the offices are strategically placed only in the well served areas. Finally, look at the lender products to see if they exclude due to guidelines as compared to other lenders.

Done!! Now, can we all get back to what we love? Helping others?

To Read the Bulletin Click Here!

Next Post
Tammy Butler, Master CMB

Author Tammy Butler, Master CMB

More posts by Tammy Butler, Master CMB

Join the discussion 2 Comments

Leave a Reply