Most credit unions say they’re member-centric. The ones that actually are use data, AI, and storytelling together to know members better and act faster.
Most credit unions still market like it’s 2005: product pushes, generic campaigns, and dashboards full of lagging metrics. Meanwhile, the members you care about are training with Netflix, Amazon, and Apple to expect you to know them—really know them.
Here’s the thing about member-centric banking: it isn’t a slogan, it’s an operating system. And in 2025, that operating system runs on data, AI, and story working together.
Karen McGaughey from Strum said it bluntly on The CUInsight Network:
“It’s no longer an option to move slow.”
She’s right. Credit unions that cling to slow cycles and product-centric thinking are quietly losing relevance. The good news? The path to a truly member-centric credit union is clearer than most teams realize.
This post connects Karen’s strategic marketing perspective with the practical reality of AI for credit unions—fraud detection, loan decisioning, member service automation, financial wellness, and competitive intelligence—so you can turn “member-centric” from a buzzword into measurable results.
Member-Centric Starts With Knowing Members Better Than Anyone
A credit union becomes genuinely member-centric when its decisions, products, and messages are driven by real member behavior and preferences, not assumptions or internal politics.
Karen’s core argument is simple: your competitive advantage is knowing your members better than any bank or fintech in your market. Data and AI are how you prove that.
What “knowing your members” looks like in practice
A member-centric credit union doesn’t just segment by age or ZIP code. It understands:
- Which members are rate shoppers vs relationship seekers
- Who’s financially stressed and silently churning
- Who’s likely to need an auto loan or HELOC in the next 90 days
- Who’s at higher risk of fraud based on behavioral patterns
- Which channels each member actually uses—and when
AI makes this practical by turning raw data into predictive signals:
- Propensity models predict who’s likely to respond to a credit card, HELOC, or refinance offer
- Churn models flag members who show early signs of leaving
- Fraud models spot unusual transaction patterns in real time
- Next-best-action engines suggest the most relevant product, message, or education for each member
The reality? Most credit unions have enough data already. It’s scattered across the core, online banking, contact center logs, marketing platforms, and third-party systems. The gap isn’t data—it’s turning that data into member insight and then acting on it consistently.
Why “Moving Slow” Is Now the Biggest Risk in CU Marketing
Karen’s line—“It’s no longer an option to move slow”—hits especially hard coming out of years of digital acceleration, economic uncertainty, and rising fraud.
Credit unions had to pivot fast during the pandemic: digital account opening, remote lending, contact center surges. Many proved they can move fast when they have to. The problem now is staying at that speed.
What moving too slowly looks like
You might recognize some of these patterns:
- Marketing calendars planned 6–12 months out with minimal room for data-driven changes
- Manual list pulls that take days or weeks to execute
- Campaigns measured only by opens and clicks, not product adoption or member outcomes
- Member experience initiatives stuck in committee for months
Meanwhile, fintechs iterate weekly. They A/B test offers, adjust pricing dynamically, and refine journeys with every click.
What agile, data-informed marketing looks like
The credit unions that are adapting well tend to:
- Use weekly or biweekly performance reviews tied to member behaviors, not vanity metrics
- Feed AI insights into their marketing platforms for real-time personalization
- Treat their websites and apps as living prototypes, not finished projects
- Give cross-functional teams (marketing, lending, IT, operations) shared KPIs tied to member value
AI doesn’t replace your strategy; it shortens the feedback loop. You see what’s working faster, adjust sooner, and avoid spending months pushing a message members don’t care about.
From Product-Centric to Member-Centric: A Practical Blueprint
Karen’s big strategic shift—product-centric to member-centric—sounds nice in presentations. The challenge is making it real when you still have loan goals, deposit targets, and board expectations.
Here’s a concrete way to make that shift without blowing up your existing structure.
Step 1: Start with member problems, not product quotas
Instead of: “We need 500 new HELOCs this quarter.”
Reframe it as: “Which members are:
- Homeowners with high-rate credit card debt?
- Planning large expenses (tuition, renovations, medical)?
- Sitting on equity but showing signs of financial strain?”
Now your target isn’t “anyone with a house.” It’s people AI has flagged as most likely to benefit from a HELOC based on their behavior, products, and transactions.
Step 2: Use AI to find the right members for each initiative
For each major product or initiative, build or adopt models that answer:
- Who is likely to need this soon?
- Who would be harmed or stressed by this offer?
- Who’s likely to say “yes” if we reach out the right way?
Even if you’re not ready for full-blown data science in-house, many AI-driven tools now bundle:
- Propensity scoring (likelihood to buy
- Risk scoring (likelihood of delinquency or fraud)
- Engagement scoring (who’s actively connected vs at risk of attrition)
Karen’s point about partnering with agencies is key here. The right external partner can:
- Audit your member data and segmentation
- Design AI-driven campaigns tied to actual business goals
- Help your team interpret the insights—not just hand off dashboards
Step 3: Build journeys, not blasts
Member-centric marketing feels different from the member’s side because it respects context.
Instead of one-off blasts, create journeys guided by AI signals:
- A member browses auto loan content → gets a tailored follow-up with real payment examples from their account history
- A member’s account shows rising credit card balances and late fees → they receive a support-first outreach focusing on financial wellness, with an option to consolidate debt
- A long-time member stops using bill pay and debit → churn model flags them → they get a retention-focused check-in from a human, not just a generic email
You’re still hitting product goals, but the narrative is, “We see you and we’re here to help” instead of “We need to hit our numbers.” Members feel that difference.
Storytelling + Data: The Differentiator Big Banks Can’t Copy
Karen emphasizes storytelling for a reason: data tells you what’s happening; story tells members why it matters and why you’re different.
Most credit unions underuse their greatest asset—real member stories backed by real data.
How to connect story, data, and AI
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Start with the outcome, not the feature.
Don’t lead with “new mobile features” or “great rates.” Lead with:- “We helped 3,214 members lower their monthly payments this year.”
- “Members using our financial wellness tools are 47% less likely to miss a loan payment.”
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Use data to prove the story.
Take anonymized insight from your AI and analytics platforms:- How many fraud attempts were blocked last quarter?
- How many hours did your virtual assistant save members in call wait times?
- How many members improved their credit score by using your digital tools?
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Translate that into human language.
For example:“Last year, our systems quietly stopped over 2,000 suspicious transactions. That’s 2,000 times you didn’t have to wake up to a drained account. You don’t always see that work—but we think you should feel it.”
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Put members at the center of the narrative.
Storytelling isn’t about how sophisticated your AI is. It’s about how safe, confident, and supported your members feel:- A young member whose first car loan you approved using AI-enhanced decisioning
- A retiree whose account was protected from fraud because your models saw something odd before they did
- A family who avoided overdraft cycles with proactive alerts and personalized budgeting insights
Story + data is hard for megabanks to fake at a local level. Your stories are grounded in real community impact, not slick national campaigns.
Building the Right Team and Partnerships to Move Faster
Karen is clear on one thing: strategy and people matter as much as tools. AI won’t rescue a credit union that’s siloed, unclear on goals, or allergic to change.
The minimum viable “member-centric” team
You don’t need a 20-person analytics department to start. But you do need a small, focused group with clear ownership:
- Marketing lead – owns member communication and campaigns
- Data/analytics lead – owns data quality and insight generation
- IT/digital lead – owns tech stack and integrations
- Business owner (lending, retail, etc.) – owns outcomes tied to member value
Give this group:
- Shared objectives: e.g., “Increase product penetration per member by 10% while improving NPS by 5 points.”
- Authority to test, learn, and adjust campaigns without months of approvals
- Regular access to AI-driven insight dashboards, not just static reports
When and how to use agencies and AI partners
External partners like Strum can accelerate your progress if you use them well. They’re most valuable when:
- You’re stuck in product-centric messaging and need a fresh, member-first brand strategy
- Your data is fragmented and you need help designing a unified member data model
- You want to roll out AI use cases (like next-best-offer or attrition modeling) but don’t have in-house data science
The most successful credit unions I’ve seen treat agencies as strategic collaborators, not just production vendors. They share real numbers, real constraints, and real member anecdotes—and expect honest pushback in return.
Where AI for Credit Unions Goes Next
Member-centric banking is the thread running through this entire “AI for Credit Unions” series. Karen’s marketing lens adds a critical reminder: If members don’t feel the benefits, the AI project doesn’t matter.
Over the next few years, expect member-centric AI to show up in:
- Real-time financial wellness coaching built into mobile apps
- Hyper-personalized offers that feel like advice, not ads
- Smarter fraud detection that’s tougher on criminals and lighter on members
- Fairer, faster lending decisions using explainable AI models that regulators can understand
Credit unions that win won’t be the ones with the fanciest technology. They’ll be the ones that:
- Know their members better than anyone else
- Use data and AI to act on that knowledge quickly
- Tell honest, human stories about the value they create
If your credit union is still mostly product-first, this is the moment to change that. Start with one member problem, one dataset, and one journey. Prove it works. Then scale.
Members don’t need perfection. They need to feel that you see them, understand them, and are willing to move faster on their behalf.