CFPB Issues Guidance on Credit Denials by Lenders Using Artificial Intelligence

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Financial institutions face a growing challenge in ensuring compliance with the Consumer Financial Protection Bureau’s (CFPB) guidance on providing accurate and specific reasons for credit denials, especially when employing Artificial Intelligence (AI)-driven models and relying on third-party providers for underwriting and verification. This dual challenge raises concerns about understanding the inputs to those models, the reliability of decline reasons, and the sufficiency of adverse action notices.

Treliant understands these critical pain points faced by financial institutions and can assist institutions in navigating this complex landscape by providing expertise in AI model governance, data validation, and third-party risk management to ensure regulatory compliance. Our team specializes in dissecting the black box of AI-driven models, ensuring transparency in decision-making processes. We work closely with financial institutions to enhance their understanding of model inputs, assess reliability, and ensure that adverse action notices provide comprehensive reasons. Our aim is to help institutions not only meet regulatory requirements, but also build trust with consumers and mitigate risks.


  1. Explanation Requirements: Lenders must provide accurate and specific reasons for credit denials, even when using complex algorithms and AI models.
  2. Sample Forms Not Exhaustive: The CFPB’s guidance clarifies that sample adverse action checklists are not exhaustive, and creditors cannot simply rely on them to meet legal requirements.
  3. Detailed Explanations: Creditors must provide specific details regarding the factors that led to adverse actions, going beyond broad categories.
  4. Fair Lending and Technology: The CFPB is actively addressing fair lending concerns related to technology, including AI-driven models, to prevent digital redlining.
  5. Adverse Action Notices: Lenders are required to provide adverse action notices to borrowers when changes are made to their existing credit, ensuring transparency in lending practices.


The changes and rules outlined in the CFPB’s guidance have significant implications for financial institutions, particularly those relying on third-party providers for underwriting and verification, and those using AI-driven models. These challenges impact the institutions in several ways:

  1. Transparency and Accountability: Financial institutions must adapt to the evolving regulatory landscape, which emphasizes transparency and accountability in credit decisions. The challenges arise from the dual issues of third-party dependency and the opacity of black box/algorithm-driven/AI-driven models. A thorough understanding of model features is key in driving an effective compliance program.
  2. Consumer Trust: Maintaining consumer trust is paramount. The inability to understand model inputs and the reliability of decline reasons can erode trust. Compliance with the CFPB’s guidance is not just a regulatory requirement but also a way to demonstrate commitment to transparent and fair lending practices.
  3. Legal Risks: Non-compliance with the CFPB’s guidance can lead to legal issues. Institutions that fail to provide specific and accurate reasons for credit denials may face regulatory penalties and legal action from borrowers. Total alignment of both a model’s systematic adverse credit decisions and the adverse action notice the applicant receives is critical.
  4. Operational Efficiency: Addressing these challenges is not just about compliance; it’s also about operational efficiency. Understanding and improving model inputs and adverse action notices can streamline the lending process and reduce friction with borrowers.

Treliant’s expertise in AI model governance, data validation, and third-party risk management uniquely positions us to help financial institutions tackle these challenges head-on. We specialize in demystifying AI models, ensuring they align with regulatory requirements, and enabling institutions to communicate transparently with borrowers. Our tailored solutions empower institutions to navigate the evolving regulatory landscape while building and maintaining consumer trust.

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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.