The next step is to evaluate how you are going to approach your data. I believe there are two distinct sets of data that provide a defensible position for you.
With the individual loan, you want to corral enough data points to prove that the consumer received the best available loan option, on the day they choose to lock their rate. Demonstrate to the examiner that you have data, systems and monitoring in place to ensure that each client receives the best loan for their situation.
•Document your borrower options from your pricing engine. Examiners ask for what the loan originator saw.
•How will you prove that the client is not being steered by an originator?
•Price being equal, did they receive the lowest rate?
•If not, how are you documenting the “why”?
•Document the LLPA from the investor and any that your company adds.
•Check for any disparity between branches in the MSA.
•Document re-locks by auditing that trail and by notating a reason for the re-lock.
•Secondary and compliance can do a “double check” with the historical database and make any notations to the file regarding any questions about why something was done.
With aggregated data you want enough data points to prove that everyone is being treated fairly as a whole, and your company actively seeks out opportunities to offer access to credit in the MSA(s) that you serve. The compliance management system and the data that you collect will assist you in providing a defense against Disparate Impact and Redlining; or an action plan to correct any deficiencies. With the exception of market analytics, the data you collect throughout the loan processing, loan search and pricing should be sufficient, it will just be packaged differently.
-Define the investor pass-throughs.
-Define the ones you add.
-Define how they are applied or show the rules in your pricing engine.
-Show comparable files in the same MSA to show standard application on a neutral basis.
-Data required to underwrite a loan per loan group type (i.e. FHA, VA, USDA, Conventional)
-Data required to underwrite as a correspondent for an investor, if different.
-Exceptions, and compensating factors data fields. How are they tracked and applied to ensure fair treatment?
•Pricing Policy Exception
-How do you track them?
-How do you ensure that they are applied fairly between protected and non-protected classes?
•Market analytics-How well do you know your market?
-Do you have statistical evidence regarding any business justification you try to use?
Example: You state that you do not do loans within a minority population of your MSA because the homebuyers in that area do not meet the minimum underwriting guidelines for your company. This justification requires market research so that you do not get side swiped at the exam. The key to this analysis (before you form a business justification) is to research the average qualifications of home-buying consumers in that area before making that statement. This is data gathering that needs to be collected for two main reasons. One, does your company offer programs that are comparable to the average homebuyer in that area? Two, if your company does not, are there other alternatives (or investors you can sell those loans to), that would meet the needs of that area without causing your company financial harm?
Tomorrow I will highlight the glaring data that is missing from your HMDA or HMDA plus software programs, to build a defensible position. Next week I am going to put the individual pieces that I have discussed so far, into a workable data flow and checks and balances. Covering all the “data chunks” for you to think about is an important first step to bring it all together.