Most credit unions lose members not on values, but on digital experience. Here’s how to use AI and a progress-over-perfection mindset to stay truly member-centric.
Most credit unions don’t lose members because their values are wrong. They lose them because their digital experience is stuck a decade behind everyone else.
That’s the tension John Janclaes, President of Nymbus CUSO, keeps coming back to: progress over perfection. Credit unions can’t wait until every system, policy, and process is perfectly aligned before acting on digital transformation and AI. Members are already comparing their experience to big banks, fintechs, and even non‑financial apps.
This matters for our AI for Credit Unions: Member-Centric Banking series because AI isn’t a “nice-to-have” add‑on anymore. It’s becoming the engine behind member-centric banking: smarter decisions, better personalization, and faster service without sacrificing the human touch.
In this post, we’ll take the spirit of Janclaes’ conversation on The CUInsight Network and turn it into a practical roadmap: how credit union leaders can drive digital transformation, adopt AI thoughtfully, and build a growth culture that keeps members at the center.
Digital Transformation for Credit Unions Starts With New Markets
The fastest way for a credit union to use digital as an advantage is to target new markets and segments with focused offerings, not to digitize everything at once.
Janclaes describes Nymbus CUSO’s mission as helping credit unions “win new markets of members, enhance processes, and modernize technology.” That order matters. Too many institutions start with technology and hope members will follow.
From “online brochure” to digital growth engine
Member-centric digital transformation means shifting from a generic online presence to purpose-built digital brands and products aimed at specific segments, such as:
- Gig workers who need irregular-income friendly checking
- Teachers or healthcare workers with predictable but modest incomes
- Young adults seeking their first credit product
- Retirees looking for income stability and fraud protection
Instead of a one-size-fits-all digital experience, leading credit unions are spinning up niche digital brands or product lines tailored to these groups. AI then becomes the engine that:
- Predicts which members are most likely to adopt a new product
- Personalizes offers based on behavior, not just demographics
- Flags friction points in the onboarding journey in real time
The reality? It’s usually faster and more effective to launch a tightly defined digital offer for a focused segment than to rebuild your entire tech stack.
Where AI fits in new market growth
AI-powered tools can help credit unions validate and grow these new markets:
- AI-driven market analysis: Identify underserved segments in your footprint by analyzing transaction data, app usage, and product penetration.
- Member personas from data, not assumptions: Cluster members by behaviors (e.g., cash-heavy, subscription-heavy, side-hustle income) instead of just age and ZIP code.
- Personalized messaging: Use AI to test and optimize email, in-app, and SMS content for each segment to improve response and conversion rates.
Progress over perfection here means launching a minimum-viable offer for one segment, learning quickly, and expanding based on data—not waiting until you’ve “fixed” everything.
Culture First: Growth Mindsets Beat Shiny Tools
The credit unions that are winning with AI and digital transformation don’t start with software demos. They start with culture.
Janclaes emphasizes leadership’s role in creating a culture of growth. During the pandemic, credit unions showed impressive tenacity. The organizations that kept that momentum were the ones whose teams were empowered to experiment and iterate instead of waiting for flawless plans.
What a growth culture looks like in a credit union
A growth culture in a member-owned cooperative isn’t about chasing unicorn valuations. It’s about:
- Learning speed over planning perfection: Short pilot cycles instead of 18‑month, all-or-nothing core projects.
- Cross-functional squads: Lending, member service, IT, and compliance in the same room shaping digital initiatives.
- Comfort with experimentation: Launching an AI chatbot to handle a subset of member questions, then expanding as you learn.
- Clear member-centric metrics: NPS, digital adoption, response time, and financial health outcomes—not just cost per call.
I’ve found that credit unions that publish three to five non-negotiable member experience principles make better decisions about AI and digital. For example:
- Members should get a clear answer to routine questions in under 60 seconds.
- No member should feel surprised by a fee.
- Every product push must be tied to a member benefit, not just revenue.
These principles become the lens for deciding which AI projects to prioritize.
Leadership’s role in AI adoption
Leaders who succeed with AI do three things consistently:
- Translate AI into business language. They frame AI as “helping frontline staff serve more members, more personally,” not as abstract algorithms.
- Set constraints. For example: “We’ll only use AI models that are explainable for loan decisions,” or “We’ll always offer a path to a human.”
- Celebrate small wins. A 15% reduction in call-center handle time from a smart FAQ assistant is a big deal. Talk about it.
AI doesn’t replace culture. It amplifies it. If your culture rewards curiosity and member advocacy, AI becomes a powerful extension of that mindset.
Where AI Creates Immediate Member-Centric Value
AI can feel overwhelming when presented as an all-encompassing platform. The better approach is to start where AI clearly improves member outcomes and staff capacity.
Here are four practical areas where credit unions are seeing quick wins.
1. AI-powered fraud detection and member protection
Fraud is exploding across digital channels. In 2024, many institutions reported double-digit increases in card-not-present and account takeover fraud. Credit unions can’t rely on rule-based systems alone.
AI fraud models can:
- Analyze thousands of transaction attributes in milliseconds
- Learn what “normal” looks like for each member
- Flag suspicious behavior earlier and more accurately
The member-centric angle: better AI fraud detection means fewer false declines for legitimate purchases and faster outreach when something’s wrong. That builds trust.
2. Automated member service that still feels human
Members don’t care whether an answer comes from a human or a bot—as long as it’s fast, accurate, and empathetic.
AI in member service works best when:
- A virtual assistant handles repetitive questions (balances, hours, routing numbers, card replacement)
- Conversations that get complex are routed to humans with full context
- Agents see AI-suggested responses and knowledge articles in real time
I’m a big fan of “human in the loop” for credit unions: AI drafts, suggests, and triages, while staff make final decisions and add the personal touch.
3. Smarter, fairer loan decisioning
This is where many leaders are interested but cautious—and they should be. Used correctly, AI-based credit models can improve access and fairness by:
- Incorporating more data than traditional scores (cash flow, utility payments, stable income patterns)
- Identifying members who are lower risk than their credit score suggests
- Producing consistent decisions with explainable factors
The key is governance:
- Use explainable AI so loan officers can justify decisions
- Regularly test for disparate impact across protected classes
- Ensure there’s always an appeal path where a human can override
Done right, AI loan decisioning supports the cooperative mission: more members approved responsibly, with pricing aligned to actual risk.
4. Proactive financial wellness tools
Member-centric banking isn’t just about selling products; it’s about improving members’ financial health.
AI-driven financial wellness capabilities can include:
- Personalized nudges when spending spikes or savings drops
- Predictive alerts about low-balance risk before an overdraft occurs
- Smart, automated savings rules based on cash-flow patterns
A practical example: an AI model sees that a member’s balance typically dips below $50 three days before payday. Instead of just charging an overdraft fee, the system could:
- Suggest a small line of credit or overdraft protection
- Recommend a savings buffer target and show progress
- Proactively message the member with options
That’s member-centric AI in action.
Progress Over Perfection: How to Start Your AI Journey
Most credit unions stall on AI because they think they need a five-year roadmap and a new tech stack. Janclaes’ advice—“Progress over perfection. Don’t wait for perfection.”—is exactly right.
Here’s a practical, low-drama way to get moving.
Step 1: Pick one member-centric problem, not a technology
Examples of good starter problems:
- Call wait times are too long during peak hours
- Fraud losses or member complaints about false declines are rising
- Loan decision times are inconsistent or too slow
- Members rarely use your current financial education resources
Frame it as: “How might AI help us solve this for members?”
Step 2: Form a small, cross-functional team
Include:
- A business owner (lending, operations, or member service)
- Someone from IT or digital
- A compliance/risk partner
- A frontline representative who hears member pain daily
Give them 60–90 days to:
- Map the current process
- Identify where AI could support decisions or communication
- Evaluate one or two vendor options or existing tools
- Design a limited pilot with clear success metrics
Step 3: Run a controlled pilot and publish the results
For example, you might:
- Launch an AI-powered virtual assistant for 10–20% of inbound chats
- Use an AI fraud model only on card-not-present transactions
- Pilot an AI loan scoring assist for a specific product like small-dollar loans
Track both member outcomes (speed, satisfaction, reduced friction) and business metrics (cost, risk, conversion). Then share results internally—even the messy parts.
This is how you build a culture of progress over perfection: quick, transparent cycles of experiment → learn → improve.
Why AI and Culture Must Move Together
Janclaes talks about the pandemic as proof of the sector’s resilience. Credit unions pivoted to digital channels and remote work faster than many expected. The next phase will test something deeper: can credit unions align AI and culture without losing their member-first DNA?
Here’s the thing about AI for credit unions: if you treat it purely as a cost-savings tool, members will feel it. If you treat it as a way to extend your promise—more access, more clarity, more support—it becomes a growth engine.
For leaders who care about member-centric banking, the path is clear but not comfortable:
- Start with member problems, not AI features
- Ship small, learn fast, and resist perfectionism
- Anchor every AI decision in your cooperative values
Credit unions that do this won’t just “stay relevant.” They’ll set the standard for what trustworthy, intelligent, human banking feels like.
The question now is simple: What’s the first small, member-centric AI experiment your team is willing to run in the next 90 days?