AI only helps credit unions if advocacy and compliance keep pace. Here’s how to use AI for member-centric banking while staying true to the CU mission.
AI, Advocacy & Compliance: The Real Trio Driving CU Growth
Dan Berger from NAFCU has a simple goal:
“We want to create a legislative and regulatory environment so credit unions don’t just survive, but thrive and grow.”
Most credit union leaders I talk with are wrestling with three things at once: rising member expectations, relentless regulatory pressure, and a flood of new tech—especially AI. The hard part isn’t seeing the potential; it’s staying compliant, staying member-centric, and still moving fast.
Here’s the thing about AI for credit unions: the tech only works long term if the advocacy and compliance foundations are strong. If policy shifts under your feet or your AI program trips over fair lending rules, the “innovation” becomes a liability.
This post connects the dots between advocacy, compliance, and AI-powered, member-centric banking, drawing on the themes from Dan Berger’s work at NAFCU—and translating them into practical moves for your AI roadmap.
Why Advocacy Matters More When You’re Implementing AI
Advocacy sets the rules of the game AI has to play in.
NAFCU’s work in Washington isn’t abstract lobbying; it directly shapes how feasible it is for a credit union to adopt AI for fraud detection, loan decisioning, and member service without getting buried in uncertainty or risk.
Regulation is accelerating right when AI is exploding
Regulators are zeroed in on AI in financial services:
- Fair lending & bias in automated decisioning
- UDAAP concerns in personalized offers and marketing
- Model risk management for machine learning
- Data privacy and security across growing data sources
When you combine that with the usual alphabet soup—NCUA, CFPB, FFIEC, state regulators—you get a landscape where every new AI use case raises questions:
- Is this explainable enough?
- How do we document and test it?
- Does this change our compliance program?
Advocacy groups like NAFCU exist to push for clarity and balance in that environment so CUs can innovate without guessing what regulators meant.
Advocacy protects the “credit union difference” in an AI era
Berger often emphasizes how credit unions set themselves apart with a member-first mission. The risk with AI is that you accidentally drift toward the same black-box behavior that big banks and fintechs get criticized for.
Strong advocacy helps:
- Keep proportional regulation in play so CUs aren’t treated like megabanks
- Ensure member-owned, community-focused models aren’t penalized for using modern tools
- Promote innovation sandboxes, pilots, and guidance that work for smaller institutions
If you want AI that reinforces your identity instead of eroding it, advocacy isn’t a “nice to have.” It’s part of your tech strategy.
Compliance as a Strategic Enabler for AI, Not a Brake
Compliance shouldn’t be the department that always says no to AI. It should be the group that helps you implement AI in a way that’s safe, documented, and sustainable.
NAFCU supports credit unions through compliance assistance, education, and training. The same mindset is what you need internally to make AI work.
Build AI on top of your existing compliance strengths
Most credit unions already have:
- A BSA/AML and fraud framework
- Fair lending policies and testing protocols
- Vendor management and model oversight (even if basic)
Those become the scaffolding for AI. Instead of treating AI as a new, exotic category, extend your existing framework:
- Add AI models to your model inventory
- Include AI use cases in your risk assessments
- Document AI assumptions and limits like any other critical system
The reality? A solid, well-documented compliance program makes it easier to approve new AI use cases, not harder.
Where AI and compliance intersect for credit unions
Here are four AI areas that tie directly into compliance—and how to think about each one:
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AI fraud detection
- Use machine learning to spot anomalies in real time.
- Align alerts and actions with BSA/AML processes.
- Document how the model reduces false positives and supports SAR quality.
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AI loan decisioning and pricing
- Combine alternative data with traditional credit metrics.
- Run fair lending tests and disparate impact analysis regularly.
- Keep “human-in-the-loop” decisioning for edge cases and overrides.
-
AI-powered member service automation
- Use chatbots and virtual assistants for FAQs and self-service.
- Train them to recognize complaints and route them properly.
- Log interactions to support complaint management and QA.
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AI for financial wellness and member insights
- Provide proactive nudges about budgeting, savings, and debt.
- Avoid manipulative nudges; keep it in the member’s interest.
- Align messaging with UDAAP expectations and disclosures.
Compliance isn’t just about avoiding penalties. Done right, it gives your board and regulators confidence that your AI strategy is thoughtful, controlled, and member-centric.
Designing Member-Centric AI That Reflects the CU Mission
You can’t bolt “member-centric” onto an AI platform later. It has to be in the design.
NAFCU’s culture, as Berger describes it, is built around extreme member service—supporting credit unions so they can better support their own members and communities. That same philosophy translates directly into how you design AI use cases.
Start with real member problems, not shiny tools
I’ve found that the strongest AI projects at credit unions usually start with questions like:
- Where are members waiting too long? (contact center, mortgage, card disputes)
- Where do members feel confused or anxious? (collections, fees, credit denials)
- Where do we see preventable financial stress? (overdraft patterns, payday loan usage)
From there, AI becomes a tool, not the headline.
Example: AI for financial wellness
A mid-sized CU could:
- Use AI to scan transaction histories (with consent) to spot recurring late fees or high-cost subscriptions
- Send personalized, plain-language recommendations: “If you shifted this payment date, you’d avoid $120/year in overdraft fees”
- Offer a quick way to chat with a human advisor if the member wants help acting on it
Member-centric AI respects context, avoids shame, and focuses on practical, positive change.
Keep humans visible and available
Member-centric banking with AI doesn’t remove staff—it changes how they spend their time.
- Chatbots triage simple questions; humans handle complex, emotional, or high-stakes situations.
- AI flags at-risk members; humans reach out with empathy and options.
- AI drafts communications; humans review, tweak tone, and ensure clarity.
Credit unions win when members feel like the AI is part of a caring team, not a wall between them and a real person.
Turning Advocacy Insights into a Practical AI Roadmap
Advocacy, compliance, and AI only matter if they translate into concrete steps for your organization.
Here’s a practical path I’d recommend for a credit union leader in late 2025 who wants to stay member-focused and regulator-ready.
1. Clarify your “why” for AI in the context of your mission
Tie every AI initiative to one of these outcomes:
- Better member experience (speed, clarity, convenience)
- Stronger member financial health (wellness tools, coaching, early warnings)
- Safer member assets and data (fraud detection, cybersecurity)
- Stronger CU sustainability (operational efficiency, smarter risk management)
Write this down. Use it when evaluating vendors and internal proposals.
2. Involve compliance and risk from day one
Don’t throw a polished AI plan over the wall to compliance at the end.
Instead:
- Invite compliance, risk, and internal audit to your AI steering group
- Ask them what documentation, testing, and controls they’d need to feel confident
- Align with current and emerging regulatory expectations based on advocacy briefings and updates
This shifts the relationship from “compliance as roadblock” to “compliance as design partner.”
3. Start with one or two high-impact, low-regret use cases
Good starting points for most CUs:
- Member service AI: a chatbot that answers account, card, and branch questions 24/7, with strong escalation to humans
- Fraud detection AI: enhanced transaction monitoring to catch unusual patterns quickly
Both are clearly in the member’s interest, easy to explain to regulators, and typically lower risk than full AI-based credit decisioning.
4. Build an “explainability and fairness” habit
Before you scale any AI tool:
- Demand plain-language explanations of how it works from your vendors
- Test outputs for bias and disparate impact where relevant
- Document what you’ll do if the model drifts or behaves unexpectedly
Regulators increasingly expect this level of discipline. Boards appreciate it too.
5. Stay close to advocacy channels
This is where Dan Berger’s world and your AI strategy intersect most directly.
Use advocacy-driven insights to:
- Stay updated on emerging AI guidance and expectations
- Understand how peer credit unions are responding
- Help shape comment letters and feedback that reflect the realities of AI adoption at smaller institutions
Your voice, combined with organizations like NAFCU, influences whether the future regulatory environment enables responsible AI—or makes it a minefield.
Where AI for Credit Unions Goes Next
The direction of travel is clear: AI will be embedded in almost every aspect of credit union operations—from fraud detection to underwriting to financial wellness—over the next few years.
This matters because credit unions have a unique opportunity: use AI to deepen the member relationship, not just digitize it. Advocacy and compliance are the guardrails that keep you from drifting into big-bank behaviors your members actively chose to avoid.
If you’re leading a credit union right now, your next steps are straightforward:
- Identify one or two member-centric AI pilots you can run in 2026
- Pull compliance, risk, and frontline staff into the design process
- Stay plugged into advocacy and regulatory updates so you’re not caught off guard
There’s a better way to approach AI than chasing every new feature. Start from your mission, work within a smart regulatory strategy, and use AI to do what credit unions have always promised: put members first, even as the tools get more complex.