Artificial intelligence (AI) has become pivotal for financial institutions’ compliance and risk management, particularly in the area of lending models. Ensuring fair lending practices requires striking a balance between innovation and regulatory compliance — a challenging goal. Consider the long-standing rule for lenders to proactively search for and adopt less discriminatory alternatives (LDAs) in their business practices. Will the use of AI support or stymie efforts to meet this requirement? This article addresses the question, its paramount importance, and regulators’ heightening stance on it.

The Imperative of Fair Lending

Fair lending is not only a legal mandate but a moral and ethical obligation for financial institutions. It’s essential to consider the potential economic impacts that discrimination in lending can have on marginalized communities. At the same time, serious business risks, such as fines and reputational damage, must be mitigated.

As the industry increasingly embraces AI-driven business processes, the potential for bias in lending models becomes a pressing concern, necessitating a comprehensive approach to address biases in both traditional and AI-based models. Financial institutions must constantly monitor internally developed and third-party developed models to ensure fairness in lending, this includes both new models and material changes to existing models.

Challenges of Bias in Lending Models

Biases inherent in historical data can permeate both established and AI-driven lending models, leading to discriminatory outcomes. Machine learning algorithms, if not carefully designed, monitored, and updated, may inadvertently perpetuate these biases, undermining the principles of fair and inclusive lending. Recognizing and mitigating these challenges is crucial to fostering an environment where lending decisions are equitable and unbiased.

Biased models can perpetuate disparities, limiting access by marginalized groups to financial resources and opportunities. Addressing these challenges requires not only technical solutions but also a broader understanding of the societal implications of lending practices.

Less Discriminatory Alternative (LDA) Models

The LDA concept has drawn attention for its potential to address the biases present in lending models. LDAs are designed to reduce discrimination while enhancing overall model performance.  Utilizing advanced algorithms and methodologies, today’s LDAs systematically identify and rectify biases, ensuring fair treatment across diverse demographic groups. The incorporation of LDAs underscores a commitment to achieving regulatory compliance while embracing innovation and transparency.

When implementing LDAs, institutions should prioritize transparency in their methodologies. Transparent models foster trust among regulators, consumers, and stakeholders. Clear communication about the features and variables considered in LDAs builds confidence in the fairness and equity of lending decisions. Institutions can also engage in community outreach and education initiatives to enhance transparency and build trust with the communities they serve.

Insights from the CFPB

Recent insights from the CFPB underscore regulatory expectations, emphasizing the need for lenders to rigorously test and iterate high-tech models for fairness throughout their operations. Notably, this raises the bar, requiring lenders to demonstrate that their models could not be reasonably enhanced to be fairer both before and during their use.

These expectations align with a commitment to avoiding systemic fairness issues that could potentially violate fair lending laws. The emphasis is on continuous improvement and proactive measures by financial institutions to ensure that their models are reasonably related to a business purpose that could not be achieved in a less discriminatory manner.

Machine Learning, AI, and Fair Lending Compliance

While machine learning and artificial intelligence offer powerful tools for refining lending models, regulatory bodies like the Consumer Financial Protection Bureau (CFPB) have consistently flagged the risk of these systems replicating or exacerbating systemic fairness issues. They are now emphasizing the importance of not only testing models before their launch but actively monitoring and updating them to ensure ongoing fair lending law compliance.

The expectation set by the CFPB requires companies to not only rigorously test their models but also actively search for LDAs throughout their operations. This shift underscores the regulatory emphasis on continuous improvement and proactive measures by financial institutions to ensure that their models meet the standard of serving a business purpose in the least discriminatory manner.

Practical Considerations and Testing for LDAs

The emphasis on searching for LDAs aligns with decades of federal court decisions evaluating disparate impact claims under the Equal Credit Opportunity Act (ECOA). The disparate impact standard requires lenders to demonstrate a legitimate business purpose and show that there is no less discriminatory alternative to achieve that purpose. Rigorous testing for LDAs is consistent with this standard and is further reinforced by the Department of Housing and Urban Development’s Disparate Impact Rule.

When conducting tests for LDAs, financial institutions should consider the evolving nature of data and technology. For example, models that utilize data collected under the Home Mortgage Disclosure Act (HMDA) should be independently assessed for a use case and tested prior to adaptation to the Dodd-Frank Act’s Section 1071 provisions. Regular updates to models and testing methodologies are crucial to adapting to changing patterns and trends. Institutions can establish internal processes for continuous monitoring and improvement, ensuring that LDAs remain effective and relevant in dynamic market conditions.

While some lenders may argue that fair lending evaluation should only consider the inputs side of things, regulators’ comments serve as a clear directive that evaluating inputs while ignoring outcomes is insufficient. Specifically, the CFPB’s position challenges the notion that testing for fair lending compliance can be shortchanged by only exploring inputs, emphasizing the importance of searching for LDAs as the third and final prong of the disparate impact test under ECOA.

Moreover, it’s important to recognize the interconnectedness of fair lending considerations with broader corporate social responsibility (CSR) initiatives. Institutions can enhance their reputations and community standing by aligning fair lending practices with CSR goals. Demonstrating a commitment to social and economic equity goes beyond regulatory compliance, contributing to a positive brand image and fostering long-term customer loyalty.

The Role of Consumer Advocates and Fintech Leaders

Consumer advocates have long advocated for fuller accountability guidance in fair lending compliance. So have fintechs, who as both partners and competitors to traditional banks have led the way toward AI-enhanced operations and services. Regulators’ emphasis on testing for LDAs aligns with their input, reflecting a shared commitment to ensuring that models and data used by lenders are not only demonstrably fair but are also actively tested for alternatives that could lead to fairer outcomes.

These advocates recognize that the financial industry’s proactive testing for LDAs should go beyond regulatory compliance; it serves as a testament to an institution’s commitment to building trust with consumers and fostering a more equitable financial landscape. Fintech leaders, in particular, are at the forefront of leveraging innovative technologies to implement and refine LDAs, acknowledging the pivotal role technology plays in shaping the future of fair lending practices.

Fintech leaders can further contribute to fair lending by collaborating with regulatory bodies and consumer advocates to establish industry standards for LDA testing. This collaborative approach ensures a unified effort in addressing fairness concerns and establishes best practices that benefit the entire financial ecosystem. Fintech firms that actively engage in these initiatives position themselves as industry leaders in responsible and inclusive financial technology. A comprehensive, independent model validation process also helps their financial institution partners de-risk lending models and provides assurances.


In navigating the evolving landscape of fair lending, the incorporation of less discriminatory alternative (LDA) models emerges as an important strategy. Insights from the CFPB underscore rising regulatory expectations, emphasizing the need for lenders to rigorously test and iterate models for fairness throughout their operations.

Financial institutions that proactively embrace these principles position themselves as industry leaders committed not only to regulatory compliance but also to ethical and equitable lending practices. The integration of LDAs, whether in traditional or AI-driven models, becomes a beacon guiding institutions toward a future where fairness, transparency, and innovation coalesce to shape a more inclusive financial ecosystem. As the industry moves forward, proactive testing for LDAs stands as a testament to the commitment to fair lending, setting the stage for a new era of responsible and equitable financial decision-making.


Daniel Johnson Sr.

Daniel Johnson is a Managing Director at Treliant. He is an experienced regulatory compliance and data science professional with comprehensive financial services experience in regulatory compliance, risk management, internal audit, fair lending, statistical analysis, operations management, enterprise program administration, and compliance training.