Navigating AI Governance in 2025: A Practical Guide for Community Banks
Essential strategies for community banks to implement AI governance frameworks that balance innovation with regulatory compliance.
Robert Viering
Founding Principal
Navigating AI Governance in 2025: A Practical Guide for Community Banks
The adoption of artificial intelligence and machine learning in financial services is accelerating rapidly. For community banks and credit unions, AI presents both tremendous opportunities and significant governance challenges.
Why AI Governance Matters Now
In 2025, regulatory expectations around AI governance have crystallized. The OCC, Federal Reserve, and other regulators are increasingly focused on how financial institutions manage AI/ML model risk. Community banks can no longer afford to treat AI as a "nice to have" governance consideration—it's now essential.
Key Regulatory Drivers
- SR 11-7 Compliance: Traditional model risk management principles apply to AI/ML models
- Fair Lending Concerns: AI models must be evaluated for bias and disparate impact
- Explainability Requirements: Regulators expect institutions to explain AI-driven decisions
- Vendor Management: Third-party AI tools require enhanced due diligence
The Community Bank Challenge
Unlike large banks with dedicated AI teams, community institutions face unique challenges:
- Resource Constraints: Limited staff and budget for AI governance
- Technical Complexity: AI models are often "black boxes"
- Vendor Dependence: Many AI tools come from third-party providers
- Regulatory Uncertainty: Guidance continues to evolve
A Practical AI Governance Framework
Based on our work with dozens of community banks, here's a practical approach to AI governance:
1. Start with Inventory
Action: Create a comprehensive inventory of all AI/ML models in use, including:
- Credit scoring models
- Fraud detection systems
- Chatbots and virtual assistants
- Marketing and CRM tools
- Document processing automation
Why It Matters: You can't govern what you don't know about. Many banks discover they have more AI systems than expected.
2. Assess Risk and Materiality
Action: Categorize each AI system by risk level:
- High Risk: Direct lending decisions, fraud prevention
- Medium Risk: Marketing, customer service
- Low Risk: Internal process automation
Why It Matters: This determines the level of governance oversight required.
3. Establish Clear Accountability
Action: Assign ownership for AI governance:
- Board Oversight: Annual AI risk review
- Management Committee: Quarterly AI governance meetings
- Model Validators: Independent validation for high-risk models
- Business Owners: Day-to-day monitoring
Why It Matters: Without clear ownership, AI governance becomes everyone's responsibility and no one's priority.
4. Implement Validation Processes
Action: For high-risk AI models, implement SR 11-7 compliant validation:
- Conceptual soundness review
- Ongoing monitoring
- Outcomes analysis
- Bias testing
Why It Matters: Regulatory examiners will ask for evidence of independent validation.
5. Document Everything
Action: Maintain comprehensive documentation:
- Model development records
- Validation reports
- Monitoring results
- Management responses to findings
Why It Matters: "If it's not documented, it didn't happen" applies to AI governance.
Common Pitfalls to Avoid
Pitfall #1: "Our Vendor Handles It"
Reality: Regulatory responsibility remains with the bank, regardless of vendor controls. You need to validate vendor AI models independently.
Pitfall #2: Waiting for Perfect Clarity
Reality: While guidance continues to evolve, waiting means falling behind. Start with SR 11-7 principles and adapt as needed.
Pitfall #3: Treating AI Like Traditional Models
Reality: AI models have unique characteristics (adaptability, complexity, data dependencies) that require specialized governance approaches.
Technology-Enabled Governance
Modern AI governance doesn't have to be manual and resource-intensive. Technology can help:
- Automated Monitoring: Track model performance continuously
- Bias Detection Tools: Identify fairness issues proactively
- Documentation Platforms: Centralize governance records
- Alert Systems: Flag potential issues before exams
The RegVizion Approach
At RegVizion, we help community banks implement practical AI governance through:
- Governance Playbooks: Customized frameworks based on your risk profile
- Model Validation Services: SR 11-7 compliant reviews
- Training: Build internal capability
- Technology: Tools that automate governance processes
Next Steps
If your institution is using or considering AI/ML, start here:
- Conduct an AI Inventory: Know what you have
- Assess Regulatory Risk: Understand your exposure
- Establish Governance: Don't wait for the exam
- Get Expert Help: Partner with specialists who understand both AI and community banking
Conclusion
AI governance in 2025 isn't optional for community banks—it's a regulatory expectation. But with the right framework, technology, and expertise, even resource-constrained institutions can implement effective AI governance that enables innovation while ensuring compliance.
The key is starting now, starting practical, and building capability over time.
Need help with AI governance? Schedule a consultation to discuss your institution's needs.
About the Author: Robert Viering is a Founding Principal at RegVizion, where he leads the AI Governance practice. With two decades of experience in model risk management and AI implementation, he helps financial institutions adopt AI responsibly.