Most credit unions sit on rich data but struggle to act on it. Here’s how to use AI and analytics to turn member data into concrete, member-centric decisions.
Most credit unions are drowning in data and starving for insight.
You see the reports: card usage, mobile logins, NSF fees, loan pull-through, contact center volumes. Yet turning that raw information into clear, member-centric action is where things usually fall apart.
That’s why a line from Lesley DeCator of FIS sticks with me:
“Taking action on data is critical.”
She’s right. And in 2025, taking action on data increasingly means pairing your core analytics with practical AI – not as a flashy project, but as a disciplined way to understand members and serve them better.
This article connects what Lesley discusses—actionable, payments-focused data—with the broader theme of AI for credit unions and member-centric banking. The goal: show how you can move from scattered reports to AI-assisted decisions that actually change product design, fraud strategies, and member experiences.
From Reports to Decisions: What “Actionable Data” Really Means
Actionable data for a credit union is data that directly drives a decision, workflow, or member interaction. If a report doesn’t change what you do, it’s a dashboard, not a strategy.
Here’s the thing about data at most credit unions: it’s accurate, it’s plentiful, and it mostly just… sits there.
Lesley’s work at FIS focuses on distilling complex payments data—interchange, merchant categories, transaction patterns—into reports that credit union leaders can actually use. AI can push that even further by:
- Flagging members likely to churn
- Spotting unusual transaction patterns in real time
- Recommending next-best offers personalized to each member
The three tests of actionable credit union data
Any data initiative—AI or not—should pass three basic tests:
- Clarity – Can a non-technical leader explain what this metric means in one sentence?
- Ownership – Is there a named person or team responsible for acting on it?
- Timeframe – Is there a specific window in which action needs to occur (today, this month, this quarter)?
If your “AI fraud dashboard” doesn’t translate into, “Our fraud analyst can see which cards to block or text in the next 10 minutes,” it’s not actionable yet.
Where AI Makes Data Truly Member-Centric
AI becomes useful for credit unions when it turns member data into timely, relevant decisions—especially in payments, lending, and service.
Lesley emphasizes flexibility, speed, and future-proofing. AI directly supports that in a few concrete areas.
1. AI for smarter payments and card strategies
Payments data is a goldmine for understanding your members’ financial lives. AI can analyze millions of transactions to:
- Identify merchants and categories where your card is underused
- Detect dormant cards that could be reactivated with the right offer
- Find members who rely heavily on payday or cash-advance apps and need financial wellness outreach
Example:
- Traditional: You see that debit transactions rose 5% year-over-year. Interesting, but vague.
- AI-driven: A model surfaces that 18% of prime members under 35 are routing recurring subscriptions to a competitor’s credit card. Marketing then launches a targeted campaign with a tailored rewards offer and a one-click “switch your subscriptions” guide.
Same data, different outcome.
2. AI-powered fraud detection that feels supportive, not punitive
Fraud detection is one of the most mature uses of AI for financial institutions. But member-centric banking means minimizing both fraud and false declines.
AI fraud models can:
- Score each transaction for risk in milliseconds
- Learn individual member patterns (where they travel, how they shop)
- Adapt quickly to new fraud schemes without months of rule tuning
The member-centric twist is how you respond:
- Use tiered interventions: text or in-app confirmation for medium-risk, hard declines only for high-risk
- Let members set their own risk preferences in the mobile app (e.g., block international transactions by default)
- Provide instant, empathetic explanations when a transaction is declined
Fraud AI plus human-centered design = safety that feels like care, not friction.
3. AI for lending and fair, fast decisions
On the lending side, AI helps credit unions remain competitive on speed while staying true to their mission.
Thoughtful AI models can:
- Pre-qualify members for auto or personal loans using internal data
- Combine traditional credit scores with cash-flow and behavior data to better serve near-prime members
- Identify members at risk of delinquency early and trigger proactive outreach
The key is governance. Use AI as decision support, not a black box. Many progressive credit unions use AI to:
- Rank and segment applications
- Suggest pricing ranges or terms
- Flag edge cases for manual review
That keeps member relationships and fairness at the center while still improving speed and consistency.
Turning Data into Action: A Simple Framework for CU Leaders
Lesley talks about distilling data into manageable reports so leaders can act. Pair that with AI, and you get a simple but powerful framework: Observe → Decide → Act → Learn.
Step 1: Observe – build one source of truth
You can’t be member-centric if you’re working from five different versions of reality.
Priorities for this step:
- Consolidate core, payments, digital banking, and contact center data
- Standardize basic definitions ("active member", "primary checking", "active card")
- Create a shared dashboard for the executive team with no more than 10 key metrics
AI can help by cleaning data, deduplicating records, and highlighting anomalies, but the main job here is governance, not gadgets.
Step 2: Decide – define thresholds and triggers
Data becomes actionable only when you decide, “When X happens, we will do Y.”
Examples for a member-centric AI program:
- When a member’s debit card usage drops by 50% over 60 days, then they enter an “at-risk” campaign.
- When a member’s cash-flow model predicts a 30-day delinquency risk above 60%, then they receive a proactive financial wellness outreach.
- When a transaction fraud score exceeds a set threshold, then prompt for real-time verification rather than auto-decline.
Don’t overcomplicate this step. Start with 3–5 clear triggers that tie to your strategic goals: growth, member engagement, and risk.
Step 3: Act – build workflows, not just reports
This is the gap Lesley was pushing on: reports are useless if they don’t map to workflows.
Make sure each AI insight routes to someone or something that can act:
- A marketing automation flow that sends a personalized offer
- A contact center queue labeled “High-Churn Risk Members”
- An alert to the card services team for real-time fraud review
If you don’t know who does what when a metric moves, the data isn’t actionable yet.
Step 4: Learn – measure outcomes and refine models
AI models are not “set and forget”. Member behavior changes, markets change, product lineups change.
Review, at least quarterly:
- Did AI-identified at-risk members actually churn less after outreach?
- Did fraud losses drop without a spike in false declines and complaints?
- Did AI-informed campaigns produce higher engagement or product adoption?
Close the loop: feed results back to your AI models and human teams, so both get smarter.
Future-Proofing Your Credit Union with AI and Data
Lesley stresses future-proofing the credit union business model. From an AI and data perspective, that comes down to three strategic bets.
1. Build flexible data and AI foundations—not one-off projects
Most institutions make the mistake of running isolated AI pilots: a chatbot here, a fraud model there, maybe a collections tool.
Future-ready credit unions treat AI and analytics as shared infrastructure:
- A common member data platform across the organization
- A small, cross-functional data/AI team that supports multiple departments
- Shared standards for model governance, fairness, and privacy
This is less glamorous than launching a flashy member-facing bot, but it’s what actually scales.
2. Keep humans in the loop—especially on member experience
AI can recommend the “next best action”. Your people decide the right action for your culture and your members.
Smart credit unions:
- Train frontline and lending teams on what AI models do—and don’t do
- Give humans the ability to override AI decisions with clear documentation
- Use member feedback (NPS, complaints, call transcripts) to adjust AI-driven processes
Member-centric banking means AI assists, humans decide, members benefit.
3. Use AI to support mergers, partnerships, and growth
Lesley also brings M&A experience. Any credit union that’s growing through mergers or partnerships is sitting on a complex data challenge that AI can actually help with:
- Mapping and reconciling member records across systems
- Identifying overlapping and at-risk members during integration
- Harmonizing product and pricing strategies based on real usage patterns
Handled well, AI can shorten the painful “post-merger fog” and help the combined institution quickly see who its members are now and what they need.
Getting Started: A Practical First 90 Days
If you’re leading a credit union and you want to turn your data into member-centric AI outcomes, here’s a realistic starter plan.
Weeks 1–4: Clarify goals and baselines
- Pick 1–2 focus areas: payments, fraud, lending, or service
- Define 3–5 metrics that matter (e.g., active card rate, fraud loss per $1,000, loan decision time)
- Inventory where relevant data lives and who owns it
Weeks 5–8: Stand up one AI-assisted use case
- Choose a contained pilot, like: reducing card attrition or improving fraud alerts
- Partner with your existing vendors or a trusted AI partner
- Define clear triggers, owners, and workflows
Weeks 9–12: Measure, refine, communicate
- Track early results against your baseline
- Adjust thresholds, messaging, or operational steps
- Share simple, concrete stories with your board and staff: “We identified 1,200 at-risk cardholders and reactivated 23% of them.”
The reality? You don’t need a massive AI transformation. You need one or two data-backed wins that prove this approach serves members better and supports your strategy.
AI for credit unions isn’t about shiny technology. It’s about doing what Lesley DeCator describes—making data truly actionable—at a new level of scale and speed, without losing the human focus that makes credit unions different.
If your 2025 priorities include member-centric banking, smarter fraud detection, faster lending, or better payments engagement, now’s the time to turn your scattered reports into an AI-assisted, always-on feedback loop.
The credit unions that thrive over the next decade will be the ones that combine deep member understanding, disciplined data practices, and practical AI—and then actually act on what they learn.