Most credit unions are sitting on strong data but weak action. Here’s how to use data and AI to power truly member-centric banking and stop being a “best kept secret.”
Most credit unions sit on a goldmine of member data and still feel invisible in their own communities.
That tension is exactly what Ryan Housefield, SVP of Sales at Marquis, calls out with his line:
“Without credible data, you only have an opinion.”
For credit union leaders staring down 2026 planning, that quote should sting a little. You’re competing with national banks, fintechs, and now AI-powered experiences members get everywhere else in their lives. Opinions aren’t enough anymore. Data and AI-backed decisions are.
This post takes the ideas from Ryan’s CUInsight Network episode and pushes them further into a very practical question: How do you turn credible data and AI into a member-centric banking engine that actually grows your credit union?
This matters because the credit unions that win over the next 3–5 years will do two things better than everyone else:
- Treat each member as an individual, not an account number.
- Use data and AI to make those relationships feel effortless, timely, and relevant.
Here’s how to get there.
1. Data Isn’t a Project, It’s an Ongoing Practice
The core idea from Ryan’s interview is simple: data is an evolution, not a revolution. There’s no magic go-live date where your “data initiative” is suddenly finished.
The reality: most credit unions fall into one of two traps:
- They collect everything, use almost nothing.
- Or they wait for “perfect” data and never get started.
You don’t need perfect. You need credible enough to act.
What counts as “credible data” for a credit union?
Credible data is data you trust enough to make real decisions with. For member-centric banking, that usually means:
- Accurate core data: balances, transactions, products held, tenure
- Current contact info: emails, mobile numbers, communication preferences
- Engagement signals: digital logins, call center interactions, branch visits
- Lifecycle indicators: age, life events, loan milestones, deposit patterns
You probably already have 70–80% of this. The gap is usually integration and cleanliness, not collection.
Here’s the thing about AI: AI amplifies whatever you feed it. If you feed it messy, outdated, or incomplete data, you’ll get automated noise instead of automated value.
A good starting point for any AI for credit unions initiative isn’t a chatbot. It’s a basic, honest answer to: Which member data can we trust today? Then you clean and connect that first.
2. From Opinions to Actions: Turning Data into Member Outcomes
Ryan’s main argument is that data only matters when it leads to action. He’s right. Dashboards don’t grow deposits by themselves.
For AI-powered credit unions, the real question is: What actions do we want to automate or improve with data? Start small and specific.
Here are three concrete use cases where data + AI consistently improve member experience and ROI.
a. Intelligent onboarding instead of generic welcomes
Most new members get:
- One “Welcome to the credit union” email
- Maybe a postcard
- Then…nothing relevant for months
A data-driven, AI-assisted onboarding journey looks different:
- Day 1–3: Personalized welcome email tailored to how they joined (online, branch, employer referral)
- Day 7: Helpful content based on their primary product (checking vs. auto loan vs. credit card)
- Day 14–30: AI predicts the next best product based on similar member behavior (e.g., high debit usage → checking-plus savings offer)
- Day 45–60: Digital nudges if they’re inactive (no online banking login, no card usage)
All of this can be orchestrated through marketing automation, CRM, and AI models trained on your own member data. The outcome? Higher activation, deeper relationships, and a member who feels seen early.
b. Proactive, data-driven financial wellness
Member-centric banking isn’t just about cross-sell. It’s about financial wellness that’s specific, not generic.
Data and AI can power:
- Alerts when a member’s pattern suggests financial stress (frequent overdrafts, rising credit card balances)
- Budget coaching nudges tailored to their real spending, not hypothetical categories
- Personalized savings goals based on income, expenses, and life stage
Think: instead of a static “financial literacy” page, a member gets a smart, ongoing conversation in your app that uses their actual data to suggest the next right step.
This is where Ryan’s point lands: use data to improve member lives, not just reporting.
c. Smarter, fairer loan decisioning
AI for credit unions often starts with fraud and chatbots, but loan decisioning might quietly be the biggest opportunity.
With the right guardrails and explainable models, AI can:
- Surface additional context on borderline applications
- Highlight members who are low-risk but thin-file
- Suggest pricing tiers that balance risk and member value
You’re not replacing your lending team. You’re giving them better tools. And you’re making it easier to say “yes” more often, especially to members who don’t fit big-bank credit box rules.
3. High-Tech + High-Touch: Your Real Competitive Advantage
Ryan talks about a “high-tech and high-touch” balance. I’d argue that’s the only viable strategy for credit unions in an AI-driven market.
Big banks can outspend you on tech. Fintechs can out-innovate you on UX. But neither can match a locally rooted, relationship-first institution that also uses AI and data well.
Where AI should support, not replace, human relationships
Think about member journeys where humans matter most:
- Complex lending (mortgages, small business loans)
- Financial hardship conversations
- Life events: marriage, divorce, retirement, loss
AI for credit unions should:
- Flag moments when a member needs a human, not a bot
- Prep your staff with context before they pick up the phone
- Capture notes and learn from each interaction to improve future support
Example: A member has a late payment for the first time in years, after a layoff. An AI system sees the pattern, routes them to a senior agent, and provides prompts:
- “Member tenure: 11 years, historically on-time payer.”
- “Recent drop in direct deposit amount.”
- “Recommend hardship options: skip-a-pay, 3-month reduced payment plan.”
That’s high-tech enabling high-touch.
Stop being a “best kept secret” in your community
Ryan calls out something I’ve seen everywhere: credit unions are often loved by those who know them and unknown to everyone else.
Data and AI can directly help with that awareness problem:
- Use geo and behavioral data to identify lookalike audiences to your best members.
- Target employer groups where you already have strong penetration.
- Personalize acquisition campaigns so they feel like an invite, not a broadcast.
And once someone joins, don’t stop educating. Every statement, every push notification, every in-app message is a chance to reinforce: “We’re your financial partner, not just your account provider.”
4. Building the Data + AI Stack: Start Smaller Than You Think
Most credit unions overcomplicate the tech stack question. You don’t need a Silicon Valley architecture diagram to get started.
You do need four practical building blocks – the same components Marquis focuses on, extended here for an AI context:
1. Data foundation (clean, connected, accessible)
- Connect core, digital banking, LOS, contact center, and marketing systems.
- Standardize key IDs so a “member” is the same person across every platform.
- Establish basic data quality rules: required fields, formats, and refresh cycles.
2. Marketing automation with real segmentation
- Create dynamic segments: “new members,” “at risk,” “high potential,” “digital-only,” etc.
- Trigger campaigns based on behavior (not just calendar dates).
- Test and learn: A/B subject lines, offers, and channels.
3. CRM built for relationships, not just tickets
- Give staff a 360° member view: products, interactions, preferences, life events.
- Log every touchpoint so AI models can learn from real history.
- Surface “next best conversation” prompts for frontline teams.
4. AI services layered on top of real use cases
Don’t start with “Which AI tool should we buy?” Start with:
- “We want to improve new member engagement by 25%.”
- “We want to reduce call volume on simple balance questions by 40%.”
- “We want to catch 30% more fraud attempts before they hit members.”
Then apply AI where it clearly supports those goals:
- Chatbots or virtual assistants for simple, repetitive questions
- Predictive models for churn, cross-sell, or fraud
- Natural language tools to summarize calls and surface insights
The sequence matters: data → automation → AI, not the other way around.
5. Practical First Steps for Credit Union Leaders
If you’re leading a credit union and feeling both excited and overwhelmed by AI, here’s a straightforward path I’ve seen work.
Step 1: Pick one member-centric use case
Choose a use case that:
- Directly improves member experience, and
- Has clear metrics you can track
Good candidates:
- New member onboarding journey
- Card activation and usage
- Digital banking adoption
- Early warning for at-risk members
Step 2: Audit the data you already have
For that one use case, list:
- What data you have today
- Where it lives
- How often it’s updated
- How accurate you think it is
Then ask: Is this credible enough to act on? If not, fix that first.
Step 3: Automate before you “AI it”
Build basic rules-based automation first:
- If new member joins → send welcome series
- If card not activated after 7 days → send prompt
- If login drops for 30 days → send “we miss you” check-in
Once that’s running, bring in AI to optimize timing, channel, and content.
Step 4: Keep humans in the loop
For every AI or automation initiative, define:
- When does a human step in?
- What context do they need to be effective?
- How do we capture what they learn back into the system?
That loop is what turns data into institutional knowledge instead of scattered anecdotes.
AI won’t make credit unions member-centric on its own. But credible data, thoughtful automation, and targeted AI can absolutely help you stop being the “best kept secret” and start being the first choice.
The next 12–24 months are a window. Members are getting used to AI-enhanced everything—shopping, streaming, health. Their expectations for banking are rising with it.
Credit unions that act now, with a data-first, member-focused mindset, won’t just keep up. They’ll stand out.
So here’s the question worth asking at your next leadership meeting: Where can we use the data we already trust to create one unmistakably better experience for our members this quarter?