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Community Action Groups Pressure Banks with Fair Lending Data Analysis

4 min read
Oct 13, 2016

Community action groups are following the regulators' lead and using data analysis to put pressure on local lenders. They may analyze your Fair Lending data and build a picture of your institution. You need to be prepared to share your version of the story, backed by data analysis and your unique understanding.

In today's sophisticated and increasingly data-focused regulatory compliance environment, even community action groups are leaning on Fair Lending data analysis to put pressure on local banks, credit unions and mortgage companies. Just like the regulators, they are analyzing your public loan data and looking for evidence of discrimination, i.e. disparities.

The results they're sharing are ending up on the front pages of local newspapers, like last Sunday's Chattanooga Times Free Press. Unfortunately, "Black Families Disproportionately Denied Loans" is a catchier headline than "Banks Work to Achieve Compliance." 

That article cites a report - "Whose Reinvestment? The Failures of Equitable Home Lending in Chattanooga" - that was released by community action group Chattanooga Organized for Action (COA).

COA analyzed the public HMDA data of lenders in the area from 2011-2014, and concluded that the local banks were failing to lend to black/African-American and other minority communities. 

In the article, local community leaders spoke out against the banks' lending practices, even offering tips for how to fix it. Some banks did also share their opinions, explaining that the data used doesn't paint the whole picture. However, the overall impression is that banks in Chattanooga are discriminating against black borrowers.

The reality is that lenders are making FHA and VA loans to majority-minority and LMI neighborhoods, but this arcticle (and others like it) may focus on limited data, such as conventional lending only. While bankers know the difference, the public often does not...and lenders' reputations may be undermined as a result.

With that kind of narrative, the only way to really defend yourself is to understand and be able to explain your numbers, so that you can share your side of the story.

Here's what we know: we have never encountered a lender that wanted to discriminate.

We work with hundreds of lenders nationwide that deeply value their relationship with their communities. Those relationships are the foundation of successful, sustainable lending for community banks, credit unions and mortgage companies.

While there are certain situations where a lender has room to improve their lending to minority communities, there are also situations where general data analysis may paint a picture that does not accurately reflect reality.

Here are some considerations when analyzing your loan and deposit data for Fair Lending compliance:

  • Statistical Significance: Are the data sets large enough to generate statistically significant results?
    • Ncontracts Tip: When analyzing your loan and deposit data, make sure that you consider statistical significance. A few loans sometimes can generate big disparities, but it's unlikely to be statistically significant. If it's not statistically significant, it will be difficult to prove that it indicates a pattern or practice of discrimination.
  • Similarly Situated: In order to truly interpret these numbers, you need to compare apples to apples. Are the minority group individuals being compared to similarly situated control group borrowers?
    • Ncontracts Tip: There are a few data points that lenders use to evaluate the credit-worthiness of an applicant, like credit score. For example, a minority lender with a lower credit score would not be eligible for the same products that a control group borrower with a higher credit score. However, discrimination may be occuring if minority applicants are denied more often than control group applicants, even though they have similar credit characteristics. Data analysis needs to be sophisticated enough to accommodate these nuances. You will also want to consider matched pairs or overlap analysis to identify specific minority applicants that were denied when other similarly situated control group applicants were accepted.
  • Non-HMDA Lending: Does the analysis consider non-HMDA Lending? Some of the banks did mention that much of their lending to minority individuals was made through government-backed or secondary markets acting as loan origination offices, meaning that some of the data wouldn't be on the HMDA LAR. 
    • Ncontracts Tip: A community group or examiner is not likely to know the subtleties of your business. As a lender, you need to understand any disparities that may indicate discrimination, and be prepared to explain them. Sometimes, the disparities are statistically significant and indicate that you have room to improve; in that case, it's much better to be proactive, and take steps to better serve your community before the examiners or community groups draw the wrong conclusion.

If you're able to prove that you are working to serve all communities in your REMA (Reasonably Expected Market Area) and CRA Assessment Area, you'll be in a good position to provide a valuable insights about your performance to community action groups, examiners, and even journalists.

Although many lenders have strong relationships with local community action groups and other community leaders, working together to strengthen relationships with minority communities, it's still important to be able to defend your institution and explain your performance.

Ncontracts Viewpoint: Data analysis is essential for Fair Lending compliance. Not only is it critical to any internal monitoring and reporting, it is one of the first lenses through which external organizations and individuals will evaluate your performance. Why? Your HMDA data is public. Your unique policies, procedures and relationships with your community are not - at least not the same way.

In this day and age, spreadsheets and pivot tables are just not going to provide you the insight you need. And most lenders don't have the need - or the budget - to hire a team of statistical analysts, even though they do need to analyze their data for Fair Lending compliance.

That's where Fair Lending software fits. It provides clear analysis, powerful insights, and experienced guidance to help you understand your numbers and improve your compliance. This is also where we excel.

TRUPOINT Analytics is a powerful software tool that runs millions of calculations on your data in just a few minutes. The platform will help you visualize your data and highlight any disparities that are likely to attract the regulators' attention. It will then show you the statistical significance of those disparities, so that you know what to focus on.

In addition, it includes Matched Pairs or Overlap analysis at no additional charge. Finally, it provides a comprehensive understanding of your Fair Lending risk in each of the key risk stages: marketing, underwriting, pricing, steering and redlining.

The best part? Every report comes with an consultant-led review to guide you through the findings.

If you want to learn more about how we can help, just get this free Report Preview Kit. It includes samples and much more! 

Discover how you can build your own lending compliance management system.

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