Is Your FI Complying with Fair Lending Laws? - Leverage Analytics
Is your financial institution complying with fair lending laws? It’s a deceptively simple question with a complicated answer.
Your institution is committed to fair lending and would never intentionally discriminate against an applicant based on race, ethnicity, gender, or another prohibited basis factor. You have policies and procedures to prevent disparate treatment and your staff has been trained.
Yet FIs often fall short of their fair lending obligations. We’ve already seen several fair lending enforcement actions and fines over the past year:
- A mortgage company settled a joint DOJ and Consumer Financial Protection Bureau (CFPB) redlining suit for $24.4 million—the second largest redlining settlement in DOJ history and the first against a non-bank lender.
- A $17 billion-asset bank settled a redlining lawsuit for $9 million in a consent order with the CFPB, DOJ, and the Office of Comptroller of the Currency.
Fair lending disparities are surprisingly common—even at tech-savvy and experienced companies. Last year online real estate service Redfin was sued by the National Fair Housing Alliance (NFHA) and other public interest groups. The groups allege that Redfin discriminates against home buyers and sellers in majority-minority communities because the company had a minimum home price policy that ended up excluding many homes in these areas.
How Fair Lending Violations Happen
Inconsistency and personal discretion due to lack of policies and procedures is a common source of fair lending violations.
Consider fee waivers, with so many customers experiencing financial distress right now, your FI may be waiving some fees. It’s an altruistic act—but it can also lead to fair lending trouble if waivers aren’t offered consistently. If staff is given free rein to waive fees or the policy for fee waivers is even a little vague, it can lead to disparate treatment if similarly situated consumers aren’t given the same waiver.
That’s why FIs need fair lending policies and procedures that define when it is permissible to offer a waiver or other special accommodation that impacts pricing or terms.
Even when policies and procedures are clear, human error can lead to disparities. A loan officer taking shortcuts or making exceptions for friends, family, or “good customers” can create fair lending headaches. Policies and procedures are no guarantee of fair lending compliance making monitoring a must so that your institution can uncover and correct failures to follow these rules.
Related: How to Build a Strong Fair Lending & Redlining Compliance Management System
Uncovering Fair Lending Risks with Data Analysis
The only way to know for sure that your FI is complying with fair lending is to take a hard look at loan data. Banks, mortgage companies, credit unions, and auto and student lenders should all be analyzing their loan data to uncover fair lending disparities.
Fair lending analytics allows an FI to quantify its fair lending performance and determine if improvements are needed. More than just an algorithm, Fair Analytics software can quickly pinpoint the loans that are causing the disparity so that compliance officers can identify and remediate fair lending risk.
It’s a best practice. Comparative statistical analysis of loan data is referenced by roughly half of the FFIEC guidelines for fair lending.
On the flip side, choosing not to analyze data increases fair lending risk, leaving open the possibility that examiners will find a problem you didn’t.
Free Report: Fair Lending Denial Disparity Analysis
Fair Lending Analytics: What Areas Should Be Analyzed?
Every FI should have a Compliance Management System (CMS) that includes the policies and procedures for managing and mitigating fair lending risk. This should include monitoring, data analysis, risk assessments, remediation, and training in addition to the overall board and management oversight of the compliance program.
The depth of your CMS should be appropriate to your FI’s risk profile. The greater the risk, the more comprehensive a CMS should be.
It begins with analyzing your data and interpreting the results to identify risks. Comparing the control group to prohibited basis groups while considering these key risks:
- Marketing risk. Is your applicant pool consistent with your market’s demographics? If not, you may not be marketing your services equally to similarly situated individuals.
- Underwriting risk. Are approval and denial rates of the control group and minority or prohibited basis group of applicants consistent for each product, market, and channel? Similarly, situated borrowers should receive similar terms.
- Pricing risk. Are there differences in pricing between the control group and prohibited basis group or minority applicants? Regulators are quick to notice pricing disparities. FIs that use statistical data analysis can determine if its products are priced equally to all borrower types. In our experience, these pricing differences are often easily explained with fair lending analytics.
- Steering risk. Are some applicants being pushed towards less desirable products? Looking at the loan data by product and borrower credit characteristics can defend an FI against accusations of steering. fair lending analytics helps an FI understand if disparities are justified or a problem.
- Redlining risk. Is your FI lending enough in low-income or high-minority census tracts? Geocoding data and comparing borrower groups makes it possible to find out if loans are appropriately distributed.
- Servicing risk. Are application processing times and exceptions consistent across all groups or do some applicants receive a higher quality of service?
Once risks are identified, your FI should investigate any risk exposure and make changes to your fair lending programs and policies to reduce risk. It’s also important to monitor data going forward to identify emerging risks and track progress on risk management.
How to Analyze Fair Lending
They say where there is smoke, there is fire, but that’s not always the case in fair lending analytics. As we noted above, there is often a sound and legal explanation for lending disparities.
The problem is that not everyone knows how to find it. Unless a compliance officer has significant experience in statistics, data analysis, and demographics, it can be very challenging to make the proper statistical analysis and benchmark comparisons.
In fact, the risk of misinterpreting data is yet another fair lending risk. It can cause your FI to accidentally ignore a problem or find problems where there aren’t any.
If your FI is concerned about its fair lending compliance, Ncontracts can help with Nrelief (formerly known as Nfairlending), our fair lending analytics software. It can conduct in-depth loan analysis in seconds and provides easy to read dashboards that your board can understand—in addition to guidance, interpretive support, and clear action steps from our team of fair lending data analysts.
Topics: Fair Lending, Ncomply, Lending Compliance, Nfairlending, Product Insight, Compliance, Lending Compliance Management,