Most credit unions use a fraction of their data. Here’s how to turn payments and member data into real AI-powered actions that improve member-centric banking.
Most credit unions are sitting on a gold mine of member data and using maybe 10% of it.
The rest is buried in card transactions, loan files, call center logs, digital banking clicks, and core systems. Meanwhile, your members are comparing your experience to the apps on their phone—not the bank down the street. If your data isn’t driving faster, more personal service, they feel it.
That’s why I like Lesley DeCator’s simple line from her CUInsight Network conversation:
“Taking action on data is critical.”
She’s right—and in 2025, actionable data for credit unions increasingly means AI-powered, member-centric banking. Not dashboards for the board packet. Not a quarterly analysis. Actual decisions changing what members see, get offered, and experience.
This post connects what Lesley talks about—turning raw payments data into something credit unions can use—with where many credit unions are headed next: AI tools for fraud detection, loan decisioning, member service automation, and financial wellness.
If you’re trying to figure out “what should we actually do with our data?” this is for you.
Why “Actionable Data” Is The Real AI Strategy
Actionable data is the difference between knowing something about your members and actually doing something about it.
Here’s the thing about AI in credit unions: models, tools, and vendors don’t matter if your underlying data never turns into a decision, an offer, or a better experience.
Lesley’s work at FIS is a good example. Her team takes messy card and payments data and turns it into manageable reports that credit union leaders can actually move on—like:
- Which members are shifting spend to competitors
- Which merchants are driving card loyalty
- Where card declines or friction are causing frustration
AI just extends that idea. Instead of a quarterly report, AI makes those insights continuous and proactive:
- Spot a card that suddenly stops being used after a fraud event
- Detect a member falling behind on bills before they call collections
- See that a member’s income pattern changed and adjust offers
If you remember one line from this section, make it this:
AI for credit unions only works when every project starts with a clear, member-facing action.
No action, no value—no matter how fancy the tech.
Three Data Problems CUs Need To Fix Before AI Works
Most credit unions don’t have a technology problem. They have a data-to-action problem.
I’ve seen the same three issues repeat:
1. Data is everywhere, but insight is nowhere
You’ve got:
- Core data
- Card and payments data
- Online and mobile banking behavior
- Contact center and chatbot transcripts
- Loan origination and collections data
But it lives in silos, owned by different teams, with different formats and quality. That’s why Lesley’s focus on distilling data into manageable reports resonates. You can’t jump straight to AI if no one trusts the basics.
A realistic first step is a unified member data view: not some 5-year data lake project, but a usable layer where:
- Member identifiers are consistent
- Key behaviors (spend, deposits, logins, calls) are visible together
- Data definitions are agreed on (What’s “active”? What’s “at-risk”?)
2. Analysis is interesting, but not operational
Most CUs can run a solid analysis. Fewer can wire that into daily operations.
Where this shows up:
- Marketing gets insights but can’t easily trigger automated campaigns
- Lending sees delinquency trends but doesn’t have proactive outreach workflows
- Fraud teams know where risk lives but can’t configure rules fast enough
AI for credit unions needs clear pathways:
Insight → decision → workflow → measurable outcome.
If you can’t map that chain today with simple analytics, AI will only add complexity.
3. Culture is still “slow and careful,” not “test and learn”
Lesley talks about flexibility and speed as critical for staying relevant and competitive. I agree completely.
Many credit unions are wired for risk avoidance instead of informed experimentation. AI adoption needs the opposite:
- Small pilots, not big-bang transformations
- Clear boundaries and guardrails for compliance
- Frequent iteration based on member feedback
The CUs I see winning with AI act more like product teams than traditional financial institutions: quick tests, short feedback loops, and a focus on member outcomes, not just project completion.
Where AI Actually Delivers For Credit Unions Right Now
The fastest wins for AI in member-centric banking show up in four practical areas: fraud, lending, service, and financial wellness.
1. AI fraud detection that feels invisible to members
Taking action on payments data is exactly where firms like FIS have been focused for years. AI just makes that fraud insight faster and more precise.
Done well, AI-enabled fraud systems:
- Analyze every transaction in milliseconds
- Learn from individual member patterns, not generic thresholds
- Reduce false positives that annoy good members
What this looks like in practice:
- A member’s usual Saturday grocery and gas pattern is always approved, but a sudden 3 a.m. out-of-country charge is flagged instantly.
- Instead of blocking the card outright, the system can trigger a real-time push notification or chatbot interaction: “Is this you?”
The member experiences protection, not interruption. That’s the standard you should aim for.
2. Smarter loan decisioning that’s still fair and compliant
AI in lending doesn’t have to mean black-box underwriting.
A more practical approach is augmented decisioning:
- Keep your existing risk framework and policy
- Use AI and advanced analytics to add more signals
- Focus on speed, consistency, and early risk warning, not replacing human judgment overnight
Examples:
- Pre-qualifying members for auto or personal loans using transaction and deposit history
- Identifying members who could safely handle a higher line of credit
- Spotting early signs of stress (declining balances, missed smaller payments) and offering help
If your lending team can see an AI-driven risk score alongside traditional criteria—and override it with a reason—you get the benefit of speed and insight without losing control.
3. Member service automation that feels personal
AI-powered chat and virtual assistants are quietly becoming one of the most member-centric moves a CU can make.
The trick isn’t just “have a chatbot.” It’s feed the bot with your real data and real member journeys:
- Pull card transaction data so members can ask, “What’s this charge?” and get a meaningful answer
- Integrate with loan systems to answer “What’s my payoff amount today?”
- Connect to your knowledge base so answers reflect actual CU policy and terms
A good AI assistant should handle 40–70% of routine questions while escalating complex cases to human reps with full context. That’s how you get both speed and empathy.
4. Financial wellness that uses behavior, not just content
Most financial education programs are content-heavy and impact-light.
AI can flip that by using real member behavior to personalize guidance:
- Spot subscription creep and suggest ways to save
- Notice income volatility and recommend building a buffer
- Identify young members who look ready for their first auto loan or credit card
Instead of generic blog posts, members get timely, specific nudges. It’s the difference between “budgeting tips” and “You’re on track to pay an extra $600 in interest this year—here’s how to fix that.”
Making Data Truly Actionable: A Simple Blueprint
Lesley’s focus on turning data into manageable, actionable reports is the right place to start. You can extend that mindset to AI projects with a straightforward blueprint.
Step 1: Pick one narrow, member-centric problem
For example:
- Card abandonment after a fraud incident
- High call volume about simple balance or payment questions
- Members using competitor cards for key merchant categories
If a problem doesn’t have a clear member impact, it’s not a good AI starting point.
Step 2: Define the specific action you’ll take
Be concrete:
- Trigger a personalized outreach sequence when card usage drops 80%
- Offer self-service options when a member calls with a simple inquiry
- Send a targeted card campaign to members spending heavily at certain merchants with competitor cards
You should be able to explain the action in one sentence to your board.
Step 3: Identify the minimum data needed
Resist the urge to “use everything.” Instead:
- List 3–5 essential fields (e.g., last 90 days of card transactions, logins, product mix)
- Confirm where that data lives and who owns it
- Align on definitions and quality checks
You’re trying to get to usable data, not perfect data.
Step 4: Choose the simplest AI/analytics approach
Not every problem needs deep learning.
Use the least complex tool that works:
- Rules-based models for simple triggers (e.g., usage drops, missed logins)
- Basic propensity models to rank members by likelihood to respond
- More advanced AI models only when the signal is hard to spot with simple rules
Your goal is decision support, not algorithmic art.
Step 5: Measure, learn, and tighten the loop
Before launch, decide:
- What success looks like (e.g., 20% increase in retained card spend, 15% fewer simple calls, 10% lift in digital engagement)
- How long you’ll run the pilot
- How you’ll gather member and staff feedback
This is where culture matters. The best credit unions treat each AI project as a learning engine, not a one-and-done.
Future-Proofing Your Credit Union’s Business Model
Lesley talks about future-proofing the credit union model through continuous improvement, speed, and data-based strategy. AI is now part of that conversation whether we like it or not.
Here’s the reality:
- Your members already interact with AI every day—in the apps they use, the recommendations they see, the fraud checks they never notice.
- Fintechs and big banks are using data to set the default expectation for “normal” financial service.
- Credit unions that rely only on great people and legacy processes will feel slower and less relevant, even if their intentions are member-first.
But this isn’t a story about being left behind. There’s a better way to approach AI for credit unions:
- Start from your strengths. You already have deep member trust, strong relationships, and a culture of service. Use AI to amplify that, not replace it.
- Make data actionable before you make it fancy. If a report doesn’t change a decision, it’s noise. If an AI tool doesn’t change a member experience, it’s shelfware.
- Treat AI as a long-term capability, not a one-time project. Just like digital banking, the first launch is the beginning, not the end.
If your next leadership discussion about AI focuses less on buzzwords and more on questions like, “What are the top three member experiences we’d improve with better data?”, you’re on the right track.
The credit unions that thrive over the next decade won’t be the ones with the flashiest tech stack. They’ll be the ones that quietly, consistently turn their data into better decisions for their members—every single day.
So the real question is simple: What’s the first piece of member data you’re going to turn into action this year?