Redlining compliance is a top priority for regulators in financial institutions, so they bear the key responsibility of managing redlining risk. In this article, we will try to explain the importance of analyzing data for redlining and share the top seven methods to analyze your data for compliance risk. Let’s get started.
To effectively manage redlining compliance, it is imperative to conduct a thorough analysis of your data for potential risk exposure.
While intentional redlining by financial institutions is rare, it can often result from unintended consequences of underwriting standards or marketing practices. Therefore, it is crucial to conduct data analysis to identify potential risk exposure and ensure compliance.
However, conducting this kind of analysis in-house is a daunting task that would require a team of statisticians, geocoders, and analysts. Therefore, most institutions will require specialized redlining software to effectively analyze their data and identify potential risks.
To obtain the necessary budget for this software, it may be necessary to convince leadership or team members of the importance of redlining analysis. Here are the reasons why this analysis is critical for managing redlining risk:
1. Regulators, such as the CFPB, have advised financial institutions to conduct regular data analysis as part of their redlining compliance programs. This analysis should include examining lending patterns for statistically significant disparities, evaluating physical presence, monitoring marketing campaigns, and assessing Community Reinvestment Act (CRA) assessment areas and markets more broadly.
In addition, state and federal regulators, community action groups, and journalists are utilizing data analysis to identify potential instances of redlining.
2. Redlining data analysis plays a critical role in any redlining compliance management program. Although most institutions may not intentionally engage in redlining, they can still face penalties for unintentionally discriminating against certain communities due to policies, practices, or procedures that result in unequal access to products and services. The only way to identify such disparities is through thorough data analysis.
If you’re new to analyzing data for redlining compliance, it may seem like a daunting task. However, this blog post aims to demystify the process and provide you with the necessary guidance.
It’s crucial to keep in mind that the ultimate goal of data analysis is to determine whether high-minority or low- to moderate-income census tracts are underserved, ignored, or excluded.
To help you identify potential redlining risks, here are seven essential factors to consider when analyzing your data:
- Examine your geocoded lending data for spatial disparities.
- Analyze marketing practices using application data.
- Evaluate underwriting practices using origination data.
- Assess the risk of reverse redlining.
- Compare your performance to industry benchmarks.
- Evaluate changes to your branch and ATM network.
- Review your fair lending and Community Reinvestment Act (CRA) data.
Redlining compliance is more important than ever before, and it will continue to be in the foreseeable future. Effective management of redlining compliance requires a comprehensive data analysis approach that helps you identify, understand, manage, and mitigate your risk exposure.
With Ncontracts‘ Redlining Analytics software, you can identify redlining risk exposure, see what examiners see, and ensure that your efforts to improve are effective.