7 Ways to Analyze Your Data for Redlining Compliance Risk
Redlining compliance remains a priority for regulators, and redlining risk management remains a key responsibility for financial institutions nationwide.
In this post, you'll learn why redlining data analysis is essential, and seven ways to analyze your data for redlining compliance risk. Let's jump right in.
It’s no secret that redlining is a hot issue. From enforcement actions and warnings from regulators to public interest groups and media reports investigating discrimination against minority communities, there is no tolerance for discriminatory lending.
Managing your redlining compliance absolutely requires analyzing your data for risk exposure.
Few financial institutions deliberately engage in redlining. It’s often an unanticipated consequence of underwriting standards or marketing practices. That’s why it's essential to analyze your FI’s data for risk exposure and compliance.
However, it's nearly impossible to conduct this kind of data analysis in-house; you'd need a team of statisticians, geocoders and analysts. Most institutions will need redlining software to analyze data and identify risk.
To get a budget for redlining analysis software, you will likely have to convince leadership or the rest of your team that redlining analysis is essential. Here are three reasons why redlining analysis is essential for redlining risk management:
1. The regulators said that financial institutions should be analyzing their data for redlining compliance. Agencies, including the CFPB, have told FIs that strong compliance programs:
- Examine lending patterns regularly,
- Look for any statistically significant disparities,
- Evaluate physical presence,
- Monitor marketing campaigns and programs, and
- Assess CRA assessment areas and markets more generally.
Regulators at both the state and federal level, community action groups, and journalists are using data analysis to identify potential redlining.
Related: 3 Ways to Mitigate Redlining Risk
2.Redlining data analysis is an important part of your redlining compliance management program. Most institutions have good intentions, and don't think that they redline. However, just like with other areas of fair lending compliance, you can still get pinched for unintentionally redlining, by having policies, practices and procedures that lead to different communities getting access to different products and services. The only way to identify disparities is through data analysis.
Now that you know redlining data analysis is important, we will show you how to do it.
We know it can seem daunting to analyze your data for redlining compliance, particularly if you're not doing it yet. This blog post should help demystify the process.
It's important to remember that as you're analyzing your data, you're looking to answer this key question: do you have high-minority or low- to moderate-income census tracts that are being underserved, ignored or excluded?
Here are 7 things to analyze as you review your data for redlining risk:
1. Review your geocoded lending data for croissants or donuts.
Take a quick glance at your geocoded loan and deposit activity. Does your lending seem concentrated in certain areas? Do those areas form either a C-shape, like a croissant, or an O-shape, like a donut? This cursory evaluation of your lending will provide a temperature test.
If a C-shape or an O-shape is visible, you do have redlining risk; don't waste any time. Start digging into your data. While these shapes are not proof that you're redlining, they will attract regulators' attention.
If you don't see a clear "croissant" or "donut" in your lending, you are unfortunately not off the hook. The next step is looking at your data for any potential disparities in marketing, originations, and underwriting.
2. Evaluate marketing risk using application data.
To avoid redlining, you need to market to all communities and geographic areas that you serve. To evaluate marketing risk, you should analyze:
- Applications in minority census tracts,
- Distribution of applications inside market areas, and
- Application market share within the unique market areas.
3. Look at underwriting risk using origination data.
In addition, to avoid redlining, you need to ensure that you're doing business in every community that you serve. To evaluate underwriting risk, analyze:
- Originations in majority-minority census tracts,
- Distribution of originations inside market areas, and
- Origination market share within the unique market areas.
4. Consider your reverse redlining risk.
Reverse redlining is the practice of targeting individuals in LMI or high-minority census tracts for specific products that are often less desirable. Do you have higher-priced products that are concentrated in high-minority or LMI tracts?
To answer this question, analyze:
- Applications and originations in high-minority census tracts, and
- Compare market share within unique market areas.
In addition, review both statistical analysis and maps.
5. Compare your performance to peers.
When evaluating your marketing, underwriting, and reverse redlining risk, you also need to consider how you compare to your peers.
For example, if you have a disparity of 2x, but your peers have a disparity of 4x, you are outperforming your peers. This means that even though you do have disparity, it's less than others in your market. That is an important fact to be able to share with regulators and one you can use to help defend your institution against scrutiny.
Ncontracts' redlining Analytics includes peer and benchmark filtering in each of the redlining dashboards to make understanding how your performance compares to peers a breeze. You can see some of those filters in the image below:
6. Assess changes in your branch and ATM network.
If your branch and ATM network changes, your redlining risk exposure may also have changed. Changes may include newly opened or closed branches, and newly added or removed ATMs.
Additionally, you will also want to consider whether your branches all have the same hours, and whether your branches and ATMs all offer the same services. Many institutions provide different hours and services at different branches in order to serve different communities or internal business goals. While this is justifiable, and can be beneficial for customers, it also opens up the possibility for redlining risk exposure.
7. Review your fair lending and CRA data.
Examiners usually assess redlining risk at the initial phase of a fair lending or CRA exam. To evaluate redlining risk, they will use HMDA and Census data. Peer analysis is usually involved in this process to evaluate how your performance compares to others in your market.
As you can tell, analysis is a first step for the regulators in determining fair lending, CRA and redlining risk exposure. It should be a first step in your compliance risk management, too. In this regulatory environment, you can't afford not to know your numbers.
Redlining compliance is important than ever today and will likely be even more important in the coming days, weeks, months, and years. Effective redlining compliance management relies on comprehensive data analysis that will help you identify, understand, manage, and mitigate your risk exposure.
Ncontracts' redlining Analytics software will help you identify redlining risk exposure, stand in the examiners' shoes and see what they see, and also ensure that your good efforts to improve are working as intended.
Topics: Lending Compliance