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Consumer Fair Lending Analysis - What's in a Name?

2 min read
Apr 27, 2023

With fair lending a critical part of every compliance exam, more lenders are making the wise decision to review their loan data every year. Those who really want to stay on top of their data should follow the examiners’ lead and work “surrogates” into their review of non-HMDA files.

Let me explain. The Interagency Fair Lending Examination Procedures manual addresses the use of “surrogates” to identify potential risk factors in a consumer-loan portfolio. It explains that “the term surrogate in this context refers to any factor related to a loan applicant that potentially identifies that applicant’s race, color or other prohibited-basis characteristic in instances where no direct evidence of that characteristic is available.”  For example, a Hispanic surname could serve as a surrogate for an applicant’s ethnicity. Similarly, an applicant’s given name could serve as a surrogate for his or her gender.  Once gender or ethnicity is assigned, examiners are encouraged to conduct a comparative analysis to review the prohibited-basis and control-group data and determine if any discriminatory patterns exist.

Related: Is Your Institution Complying with Fair Lending Laws?

As you may have experienced, the process of assigning gender or ethnicity based on an applicant’s name can be a laborious process because of the manual nature of the work (typically a one-by-one file review). In addition, a manual review typically includes many personal assumptions that can negatively impact the accuracy. For example: Is Addison a male or a female name?  My male friend’s name is Addison, so I think of it as a man’s name. It’s Old English in origin, meaning “son of Adam.” 

However, the name Addison is statistically higher among females. This demonstrates that, if I were a compliance officer, I shouldn’t be making personal assumptions based on this name. Instead, I should be using statistical probability – just as the examiners do. Government databases clearly show that Addison is overwhelmingly a female name (85 percent) and should receive a female-gender surrogate. 

Consumer lenders no longer need to review files one-by-one for gender and ethnicity data when they conduct annual fair-lending reviews. There are simple-to-use tools that can quickly, efficiently and accurately predict gender and ethnicity for consumer loans. By linking surnames and given names to available government databases, it is possible to more accurately predict gender while saving valuable time and money. The process can be a simple one with the right tool.   

We have that tool – a set of proprietary algorithms – at Ncontracts. We make it quick and easy for you review consumer loans (non-HMDA) from an examiner’s point of view. 

Related: How to Build a Strong Fair Lending & Redlining Compliance Management System

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