In this blog post, we discuss the evolving landscape of fair lending risk management for banks. We highlight increases in regulatory scrutiny, emphasizing the shift from simply proving the absence of discrimination to proactively mitigating potential risks. We also introduce a risk management template focusing on data, compliance structure, and effective communication as crucial components of a robust fair lending program.
There is an old adage that says if you don’t like the weather, just stick around, because it will change. As the weather is often unpredictable and in a constant state of change, so is the world of financial regulation.
The regulatory landscape for fair lending has undergone a seismic shift in recent years. For financial institutions, the message is clear: the old ways of managing fair lending risk simply won't cut it anymore.
This blog post will explore the key changes in the fair lending environment, highlighting dominant concerns and outlining a robust risk management template to help your institution stay ahead of the curve.
A New Paradigm:
Until recently, fair lending compliance meant ensuring no discrimination existed in lending practices. Institutions focused on explaining any inconsistencies as a function of relevant factors, not prohibited ones. As long as there were adequate explanations for differences in treatment based on non-prohibited factors, lenders generally fared well in an examination. However, the landscape has evolved dramatically.
The bar for what constitutes discrimination has been significantly lowered, and the burden of proof to demonstrate a pattern or practice of discrimination is much less. This new reality requires institutions to "prove a negative" - a much more challenging task.
Heightened Regulatory Scrutiny:
Fair lending is now a focal point during examinations, particularly for larger banks. Regulators expect compliance professionals to possess deep knowledge of lending functions, and examinations delve deeper into potential issues. The reliance on data and statistical analysis has also increased significantly, with regulators frequently employing statistical analysis during examinations.
Key takeaway - your data tells a story, and you MUST understand what that story is saying.
Dominant Fair Lending Concerns:
Several key areas have emerged as dominant fair lending concerns:
- Redlining: Determined largely by data and peer metrics, redlining examinations leave little room for explanation outside of data. Key risk factors include underperforming peers for three consecutive years, significant disparities in lending patterns, and the presence of “holes” in lending within minority areas.
- Exceptions: Any exception to established lending policies must be clearly defined, tracked, and managed. Vague or subjective exception policies invite regulatory scrutiny. Key risk factors include undefined or untracked exceptions, a large proportion of exceptions, and the inability to present a data-driven analysis of exceptions.
- Discretion: Unmanaged discretion in lending decisions is a significant red flag for regulators. Inconsistent treatment often stems from discretion, making it a major source of fair lending risk. Institutions should strive to minimize discretion in both pricing and underwriting.
- Underwriting: Regulators, particularly when examining larger institutions, may conduct regression analyses to assess underwriting practices. Disparities between target and control groups, subjective policies, and loan officer discretion are all red flags.
Understanding Statistical Concepts:
As data-driven analysis plays an increasingly prominent role in fair lending examinations, compliance and risk professionals must grasp basic statistical concepts. A key concept is statistical significance, which helps determine whether observed effects are due to chance or reflect true patterns in the population.
For example, in redlining analysis, statistical significance helps determine if observed differences in lending patterns between your institution and peers are substantial enough to warrant not only concern, but potentially a DOJ referral.
Risk Management Template:
To effectively navigate this complex landscape, institutions need a robust risk management framework built on three key components:
- Compliance Management System (CMS): A strong CMS should include:
- Appropriate structure and resources to cover all aspects of fair lending risk.
- Robust monitoring processes that are both risk-based and policy-based.
- A knowledgeable compliance team that understands the institution's products, policies, and the lending function. This team should also stay informed about changes in fair lending laws and regulations.
- Effective communication channels with executive management and the board, ensuring frequent and interactive two-way dialogue.
- Data: Data is the foundation of fair lending risk management in this new paradigm. Institutions must:
- Ensure they have adequate data to support monitoring efforts. This is absolutely essential. In our ever-increasing digital world, accurate and vetted data availability is rapidly becoming an expectation.
- This data should be analyzed, reviewed regularly, and segmented by lending channel. Most critically, data should be based on and include policy factors.
- Understand the story their data tells. Is the data valid? Does it align with established policies? Does it raise any red flags?
- Evaluate if they have all the necessary data to support their position and potentially "prove the negative." In addition to target and control group analysis, this should include comparisons to peer institutions to identify any outlier trends.
- Executive Management & Board Communication: Successfully engaging executive management and the board is paramount. Institutions should:
- Connect the dots by ensuring leadership understands fair lending risks, reporting processes, and their role in oversight. Executive management needs to truly understand the real regulatory risk they are facing and the potential consequences of failing to mitigate these risks.
- Train leadership on fair lending principles and keep them informed about the institution's risk profile.
- Establish clear, documented communication channels that link compliance, risk management, and the board. This reporting should be interactive, responsive, and provide a clear record of information flow.
Conclusion:
The regulatory landscape for fair lending is evolving rapidly. By understanding the current concerns, embracing data-driven analysis, and implementing a robust risk management framework, institutions can adapt and thrive in this new environment.
The key to success lies in proactive risk management, clear communication, and a commitment to fair lending principles at all levels of the organization.
