10 steps credit unions should take now to prepare for AI adoption
October 9, 2025
0 min read
AI is becoming a valuable tool as credit unions adopt more advanced technology to serve their members and stay competitive. From streamlining operations to enhancing fraud detection and improving member experiences, AI can help credit unions keep pace with financial institutions while maintaining the personal touch. However, success depends on preparation.
That preparation includes knowing how to evaluate the AI tools and vendors credit unions bring in. Regardless of the use case, the same core questions apply: How does the vendor protect member data? How is performance measured and monitored? And can the credit union understand and explain the system’s outputs for oversight, compliance, and member-facing decisions?
This Abrigo article was originally published October 3, 2025 on CUInsight.com.
This article covers these key topics:
Proactive planning for introducing AI and machine learning
In a recent comment letter, America’s Credit Unions Director of Innovation and Technology, Andrew Morris, shared what credit unions need to continue successfully deploying AI. The letter notes that future AI action plans should:
- Understand that many potential risks of using AI are not unique to AI itself, and many of these risks are already managed by current laws, regulations, or supervisory guidance.
- Prioritize educating financial regulators about the practical applications of AI to prevent misunderstandings about the division of control between human and machine agents across different use cases.
- Recognize that determining whether risks are material is especially important when evaluating AI systems. Without planning, the risks can outweigh the rewards.
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Learn more10-step checklist for preparing to implement AI
Credit unions adopting AI must be strategic and thoughtful. This list offers a framework to help credit unions prepare for AI implementation that is secure, ethical, and aligned with their mission.
1. Define your AI goals and governance structure
Credit unions adopting AI should have clear strategic objectives that align with their business goals — whether that’s improving risk management, modernizing member service, or gaining efficiency in operations. Before committing to new technologies, establish a cross-functional AI governance committee that includes stakeholders from compliance, data analytics, legal, technology, and business units. This group should oversee all AI use cases, maintain a model inventory, and ensure that high-risk models are reviewed regularly.
2. Build AI literacy across your credit union
Successful AI adoption depends on widespread understanding. Train staff at all levels on core AI concepts like machine learning, predictive analytics, and generative AI. Credit unions adopting AI should consider offering ongoing AI literacy programs to help team members understand how AI will be used and their roles in oversight and implementation.
3. Identify use cases and track ROI
Prioritize high-value, low-risk pilot projects that deliver tangible benefits. Whether it’s automating document classification, enhancing fraud detection, or reducing underwriting time, each AI use case should include defined outcomes and an ROI plan. Credit unions adopting AI must continuously measure performance and adjust based on results and risk evaluations.
4. Prepare for evolving regulatory expectations
Credit unions adopting AI should prepare for compliance with future expectations from the NCUA, CFPB, and other agencies. Begin by documenting AI governance activities, cybersecurity protocols, and risk assessments. Simulate internal audits to assess regulatory readiness and include AI discussions in board meetings to ensure oversight at the highest level.
NCUA does not have AI-specific regulations, but existing safety-and-soundness, compliance, internal control, and third-party risk expectations still apply. Credit unions should document how AI is approved, monitored, and controlled, with governance practices appropriate to the use case and risk.
5. Vet and manage third-party AI vendors
Ask how the AI works, what data it uses, how member data is protected, and how the vendor manages its own service providers. Review contracts for audit rights, incident notification requirements, data-use restrictions, and compliance with applicable privacy and security requirements. If relying on a SOC 2 report, confirm the AI service is within scope.
Beyond security, understand how the vendor measures and monitors performance for the intended use. For fraud or transaction monitoring, that can include detection effectiveness and false-positive rates. For lending, determine whether the tool supports applicable fair lending and adverse action requirements. Also ask how the vendor explains AI-assisted outputs. Your team should have enough information to understand, review, document, and appropriately explain how results are being used.
6. Prioritize explainability and ethical use
Document how each model is developed, trained, tested, and validated. Pay special attention to high-risk models, such as those used in credit decisions or fraud alerts. Select models that balance performance with transparency, ensure inputs and outputs are logged, and conduct regular bias audits to maintain fairness and trust.
7. Strengthen data privacy and cybersecurity controls
AI adds new layers of complexity to cybersecurity. Ensure sensitive member data is encrypted and cannot be used for unauthorized model training. Ask vendors how they defend against adversarial threats such as prompt injection or model manipulation. Update your incident response plan to include new risks introduced by AI systems.
8. Establish generative AI usage policies
Credit unions adopting AI should restrict the use of generative tools to institution-approved platforms and specify the types of data that can be input into these systems. Provide guidance on what constitutes appropriate use and require staff to review AI-generated content for accuracy and compliance before use in member communications or decision-making.
9. Plan for member communication and transparency
Inform members when AI is being used in ways that impact them — especially in areas like credit underwriting or fraud prevention. Offer clear opt-out options where possible, and make sure members know there’s still a human in the loop. Credit unions adopting AI should also set clear service level agreements for AI-driven tools that interact directly with members.
10. Invest in long-term innovation planning
AI is not a one-time investment. Create a roadmap that aligns with long-term business goals and supports responsible experimentation while maintaining regulatory compliance and ethical standards. Track the ROI of AI initiatives over time, and make adjustments based on results, risks, and changing member needs.
Taking the next step with AI at your credit union
Before signing an agreement with an AI vendor, credit unions need a plan to manage AI adoption intentionally. Following these 10 steps can help credit unions outline clear goals and defined governance procedures, educate staff on best practices, and create a plan for ongoing oversight. Responsible AI use in the financial services industry includes thoroughly understanding how a vendor protects member data, measures performance, explains AI-assisted outputs, and complies with regulations. With thoughtful planning and strong due diligence, credit unions can adopt AI-assisted solutions while maintaining the controls and oversight their members and regulators expect.
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Learn moreFrequently Asked Questions
How do banks and credit unions evaluate AI vendors for security, performance, and explainability?
When evaluating an AI vendor, both banks and credit unions should assess security controls and governance, model accuracy and integrations, and explainability and regulatory defensibility. Security reviews cover data protection and third-party risk. Accuracy reviews ask how performance is measured by use case and what triggers revalidation. Explainability means the institution can understand, document, and understand, review, document, and defend AI-assisted outputs to regulators. For a complete framework, see Step 5 above.
What do NCUA examiners look for when reviewing AI systems at credit unions?
NCUA reviews AI through its existing supervisory framework rather than through AI-specific regulations. Examiners may consider safety and soundness, compliance with applicable laws, internal controls, ongoing risk monitoring, and third-party due diligence. Credit unions should be able to demonstrate that they understand the AI they use and have controls appropriate to its risks.
What is model validation and when does a credit union need it?
Model validation assesses whether a model is appropriately designed, performs as intended, and remains fit for its use. Whether formal independent validation is appropriate depends on the type and use of a model, its risk, and applicable supervisory requirements. For third-party AI solutions, credit unions should understand what testing or validation the vendor performs and what oversight remains the credit union’s responsibility.
What should a credit union document before an AI-related regulatory examination?
Credit unions should be prepared to document AI use cases, relevant risk assessments and vendor due diligence, governance and controls, testing or validation where applicable, and ongoing monitoring. For higher-risk or member-facing uses, documentation should also show how outputs are reviewed and how problems or exceptions are addressed.
How do credit unions manage third-party AI vendor risk?
Ask how the system works, what data it uses, how it has been tested, how performance is monitored, and what limitations have been identified. If the credit union relies on a SOC 2 report, confirm the AI service and relevant controls are within its scope.
What is an AI governance committee and does a credit union need one?
An AI governance committee is one way to create cross-functional oversight of AI use. It may include members of the credit union’s compliance, technology, lending, risk, legal, and other relevant teams. A standalone committee is not required by NCUA; depending on the credit union’s size and AI use, oversight may instead sit with an existing risk, technology, or management committee. The important point is to establish clear responsibility for approving, monitoring, and managing AI use.
About the Author
Kate Randazzo
Senior Content Marketing Manager
Abrigo
Kate Randazzo is a Senior Content Marketing Manager at Abrigo, where she collaborates with industry thought leaders to develop digital content for banks and credit unions. Drawing on her background in strategic communications and content marketing, she translates complex financial topics into practical insights that help financial institutions better serve
