From Raw Data to Real Decisions in Credit Unions

Most credit unions are sitting on a goldmine of member data and using about 5% of it.

That’s the gap Lesley DeCator, Senior Director for PaymentsEdge Marketing and Advisory at FIS, focuses on: turning messy transaction data into clear actions credit union leaders can actually take. Not more dashboards. Better decisions.

For credit unions serious about member‑centric banking and modern AI tools, this isn’t a “nice to have.” If you want to compete with fintechs and big banks in 2026, actionable data is the entry fee.

This article builds on themes from Lesley’s conversation on The CUInsight Network and connects them directly to AI for credit unions: fraud detection, smarter loan decisioning, member service automation, financial wellness tools, and competitive intelligence.

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Why “actionable data” matters more than more data

The core idea is simple: data only matters when it changes what you do tomorrow.

Credit unions already have:

• Core transaction data
• Debit and credit card usage
• Digital banking behavior
• Loan performance and collections history
• Contact center and chat transcripts

What most don’t have is a consistent way to turn this into member‑focused decisions. Lesley’s work with PaymentsEdge is exactly about that: transforming huge payment datasets into manageable reports and recommendations.

In practice, that means:

• Segmenting members based on real behavior, not just demographics
• Seeing where you’re leaking interchange or loan opportunities
• Spotting early warning signs of attrition or fraud
• Validating whether a new product is actually used the way you intended

Here’s the thing about data for credit unions: the competitive advantage isn’t that you have it. It’s how fast you act on it.

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Turning raw data into member‑centric action

Actionable data starts with one question: “What decision will this change?”

When you combine strong payment analytics with AI, you get a practical roadmap for member‑centric banking instead of just nicer BI reports.

1. Make payment data usable, not just visible

Lesley’s team focuses on distilling payments data into clear, prioritized insights. AI can take this even further by automating pattern detection.

Examples of payment‑driven actions:

• Card portfolio optimization

  • Identify members who use a competitor’s card for everyday spending but your card only for ATM withdrawals.
  • Trigger a campaign with personalized rewards or balance transfer offers.

• Fee sensitivity and satisfaction

  • Use historical fee data + complaints + call transcripts to model which members are most fee‑sensitive.
  • Offer tailored alerts, fee waivers, or alternative products before dissatisfaction turns into attrition.

• Channel preference

  • Analyze which members use mobile vs. branch vs. call center.
  • Adjust outreach: push app tips to the digital‑heavy segment, and proactive staff outreach to branch‑loyal members.

The metric that matters: how many insights are tied to a next step in your CRM, marketing system, or operations playbook? If the answer is “not many,” you don’t have actionable data yet.

2. Use AI to scale “credit union common sense”

Credit unions know their members. AI just makes that knowledge scalable and consistent.

Practical AI use cases built on actionable data:

• Fraud detection
  Use transaction patterns, device fingerprints, and geolocation to flag suspicious activity in real time. Then, tune models based on your specific member base instead of generic industry rules.

• Loan decisioning
  Go beyond FICO by incorporating account behavior, savings habits, and historic relationships. AI models can surface thin‑file or near‑prime members who are actually strong credit risks, aligning with the credit union mission.

• Member service automation
  Train chatbots and virtual assistants on real member questions from call center logs. The goal isn’t to replace people; it’s to let staff focus on complex, emotionally sensitive conversations.

• Financial wellness tools
  Use actual spending patterns to power budgeting tips, savings nudges, and debt payoff suggestions that are tailored for each member, not generic advice.

The reality? AI is only as good as the data you feed it and the actions you’re willing to take from the results.

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Flexibility, speed, and “future‑proofing” the credit union model

Lesley has a clear stance: if credit unions want to stay relevant and competitive, they need three things—continuous improvement, flexibility, and speed. Data and AI are how you operationalize all three.

Continuous improvement: small, frequent changes beat big “transformations”

Most data projects fail because they aim for a massive transformation instead of steady, iterative progress.

A better approach:

1. Pick one high‑impact area: cards, auto loans, digital engagement, or collections.
2. Define 1–3 clear metrics (e.g., active cards per member, digital login rate, approval rate for members with FICO 640–700).
3. Use existing data to form hypotheses: “Members with direct deposit are X% more likely to…”
4. Test actions in 30–60 day cycles.

For example, a mid‑sized credit union might find that:

• Members who set up direct deposit and e‑statements in the first 60 days have 40% higher product penetration and lower attrition.
• That insight then drives an onboarding journey with AI‑driven prompts and staff follow‑ups.

You don’t need a giant AI tech stack to do this. You need clean data, a clear question, and the discipline to run experiments.

Flexibility: design for change, not one perfect answer

Product and channel strategies will keep shifting. Your data and AI strategy needs to adapt faster than your competitors’.

That means:

• Building modular data pipelines instead of one monolithic “data warehouse project”
• Choosing AI tools that can adapt as new data sources appear (e.g., new digital channels, open banking APIs)
• Avoiding rigid hard‑coded business rules when machine‑learning models can respond dynamically

Lesley’s experience with mergers and acquisitions highlights this: institutions that keep data flexible integrate faster and serve members better post‑merger. If every system is brittle and custom, combining two organizations becomes a multi‑year slog instead of a 6–12 month acceleration.

Speed: shorten the loop from insight to action

Speed doesn’t mean rushing. It means shrinking the time between seeing something and doing something about it.

That’s where AI‑enhanced workflows help:

• Real‑time fraud alerts based on unusual spending
• Next‑best‑product recommendations that surface during member interactions
• Dynamic credit line management based on updated risk profiles

You’re not chasing perfection. You’re chasing a faster feedback loop than the bank across the street.

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Practical steps to build an actionable data culture

Tools and vendors matter, but culture determines whether any of this sticks. The institutions getting the most from AI and advanced analytics share a few habits.

1. Start with business questions, not tools

Strong prompts:

• “How can we increase active debit card usage by 10% in 6 months?”
• “Which members are at highest risk of attrition in the next 90 days?”
• “Where are we approving too many risky loans—or denying too many good ones?”

Weak prompts:

• “What can AI do for us?”
• “Can we get a dashboard of everything?”

I’ve found that when leaders define three sharp questions per quarter, the data team suddenly looks a lot more strategic.

2. Translate analytics into playbooks

Lesley talks about distilling huge volumes of information into manageable reports. Go one step further: pair every key report with a playbook.

For example:

• Report: “Members with large direct deposits going to external banks.”
  Playbook: outbound calls + digital campaigns offering high‑yield savings or relationship checking.

• Report: “Members with sudden drops in debit usage.”
  Playbook: check for service issues, card declines, or competing cards; route to retention team.

If a report doesn’t have an attached playbook, ask why it exists.

3. Involve front‑line staff in AI and data design

Some of the best insight about data comes from the people who talk to members all day.

Ways to pull them in:

• Ask call center teams which questions they answer repeatedly—and train AI assistants to handle the first layer.
• Have branch managers review member segmentation: “Does this reflect what you see?”
• Close the loop: share wins that came from their feedback so this doesn’t feel like a top‑down tech project.

When staff see AI as a copilot, not a threat, adoption jumps.

4. Treat compliance and ethics as design constraints, not blockers

Credit unions have a strong trust advantage. Don’t lose it by treating AI like a black box.

Practical guardrails:

• Document how your AI models use member data
• Regularly test models for bias and disparate impact in loan decisions
• Give members clear opt‑ins and explain benefits in plain language

Ethical AI isn’t just a regulatory requirement. It’s a brand advantage for member‑owned institutions.

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How this fits into a broader AI strategy for credit unions

Within the AI for Credit Unions: Member‑Centric Banking series, actionable data is the backbone for everything else:

• Fraud detection needs consistent, high‑quality transaction streams.
• Loan decisioning depends on unified member profiles, not siloed systems.
• Member service automation runs on accurate historical interactions.
• Financial wellness tools require up‑to‑date spending and saving patterns.
• Competitive intelligence comes from benchmarking your own data against peers and your market.

Lesley DeCator’s perspective reinforces a simple truth: you don’t need to be huge to be smart with data. You just need to be intentional.

If your credit union wants to be more member‑centric in 2026, start with three questions:

1. What decisions are we making today based on hunches that could be grounded in data?
2. Where could AI help us respond faster—without losing our human touch?
3. Who inside the organization is responsible for turning data into action, not just reports?

There’s a better way to approach AI adoption: begin with one member problem, one dataset, and one measurable outcome. Then repeat.

Your members are already telling you what they need—through every payment, login, and conversation. The next move is yours.