Contactless payments are here to stay. Here’s how credit unions can use AI for fraud, rewards, and member-centric payment experiences without losing their soul.
Most credit unions saw the same number in their dashboards in 2020–2021: contactless and digital payments spiked, and they never really went back down. Matt Good from Elan Advisory Services cites it plainly: about 45% of Americans shifted to contactless forms of payment. That’s not a blip. That’s a permanent change in how members expect to pay, be rewarded, and be protected.
Here’s the thing about payments right now: whoever owns the everyday transaction owns the primary relationship. And in 2025, that’s increasingly dictated by how smart your technology is—especially your AI.
For this “AI for Credit Unions: Member-Centric Banking” series, we’ll use themes from Matt Good’s conversation on The CUInsight Network as a launchpad: contactless adoption, rewards, and leadership under pressure. Then we’ll push it further into what many credit unions are wrestling with now—how to use AI in payments to stay competitive, protect margin, and still feel deeply human to members.
From Plastic To Invisible: Where Payments Are Actually Headed
The direction of payments is clear: members want payments that feel invisible, intelligent, and instant. That shift creates both a risk and an edge for credit unions.
Most credit unions already know the basics:
- Contactless usage has surged
- Card-not-present and digital wallet transactions are now normal
- Rewards expectations have moved from “points someday” to “real value now”
What’s changed in the last few years is who is orchestrating those experiences. Big banks and fintechs are quietly wiring AI into every part of the payment stack—authorization, routing, rewards, fraud, and even messaging. If a credit union keeps a legacy, rules-only mindset, it’s effectively competing with a calculator while others are using an entire data science team.
The reality? You don’t need to match the tech budget of a megabank to deliver smart, AI-informed payment experiences. You just need to pick a few high-impact use cases and execute well.
AI Use Case #1: Smarter Fraud Detection That Actually Respects Members
The fastest way to destroy trust in your payment program is to block legitimate transactions—or to miss real fraud entirely. Traditional rules engines (block X after Y attempts, deny all cross-border transactions, etc.) were built for a different era.
AI-based fraud detection is now table stakes, not a future nice-to-have. Here’s what it does better for credit unions:
How AI Makes Fraud Prevention Member-Centric
AI models can analyze thousands of data points about a transaction in milliseconds:
- Time of day, merchant type, location
- Device fingerprint and IP reputation
- Member’s past spending patterns
- Velocity patterns across channels (card, mobile, online banking)
Instead of fixed rules, the model builds a behavioral profile for each member. When something deviates from their normal pattern, it assigns a risk score in real time.
The benefit for your members:
- Fewer false declines. A member traveling who regularly spends at hotels doesn’t get embarrassed at check-in.
- Faster real fraud detection. Abnormal card-not-present charges can be flagged and messaged before the member even logs in.
- Adaptive protection. The system quietly tightens or relaxes rules based on risk without making the member do extra work every time.
Where To Start If You Don’t Have AI Fraud Tools Yet
If your credit union is early in this journey, you don’t need a data science team on day one. Start with:
- An AI-enabled fraud platform from a trusted partner. Many card processors and payment partners now embed machine learning out-of-the-box.
- Clear metrics before you deploy. Track: fraud losses per account, false-decline rate, and call-center volume from declined transactions.
- A member communication plan. Use in-app messages, email, and SMS to explain what’s changing and how members are better protected.
Fraud prevention is the easiest “yes” story around AI. It protects members and reduces loss, which supports more competitive rewards and pricing.
AI Use Case #2: Rethinking Rewards For Streaming, Groceries, And Daily Life
Matt Good highlights one of the biggest payment trends since the pandemic: rewards have shifted toward the real, everyday life of members—streaming services, groceries, food delivery, digital subscriptions.
The mistake many credit unions still make? Treating rewards like a static marketing feature instead of a dynamic, data-driven member engagement engine.
What An AI-Driven Rewards Strategy Looks Like
AI can spot patterns in how members spend and what offers actually get used. That allows you to:
- Personalize categories. One member might see extra cash back on groceries and gas; another on travel and dining.
- Trigger timely offers. For example, after three months of consistent streaming subscriptions, offer a limited-time cash back boost on those services.
- Identify dormant cards. When a primary member suddenly stops using your card but is active elsewhere, trigger a “win-back” rewards campaign.
Rewards then stop being generic and start feeling like: “My credit union actually gets how I live.”
Practical Steps For Credit Unions Right Now
You don’t need a full-blown recommendation engine to make progress. Start with:
- Segmented rewards campaigns. Use existing transaction data to create 3–5 meaningful segments (e.g., heavy grocery spenders, young digital natives, families with kids, low-usage members).
- Dynamic rewards calendars. Rotate focus categories quarterly based on actual usage, not guesswork from last year.
- Always-on communication. Matt is right—reward programs only work if members remember they exist. Make rewards visible: in-app dashboards, monthly “here’s what you earned” recaps, and upcoming offers.
Over time, plug in AI models to predict which reward structures most likely keep each segment using your card as their top-of-wallet choice.
AI Use Case #3: Payment Experiences That Adapt In Real Time
Members now expect payment experiences that “just work” across contactless, mobile wallets, P2P, and e-commerce. The opportunity for AI in payments goes beyond fraud and rewards to real-time experience orchestration.
Where AI Can Quietly Improve Every Transaction
Here are a few high-value, often overlooked areas:
- Smart authorization routing. AI can weigh risk, interchange cost, and likelihood of approval to optimize authorizations for both member experience and margin.
- Context-aware messaging. If a transaction is declined, AI can immediately tailor the reason and next steps: “Travel alert: confirm this purchase?” versus a generic “declined” message.
- Fee transparency driven by behavior. If a member is trending toward overdraft or late payments, AI can prompt proactive alerts and autopay suggestions before fees hit.
These are small moments, but they add up. Members don’t remember your policy manual. They remember whether paying with you felt smooth and fair.
Combining Payments AI With Member Service Automation
In this topic series we’ve talked about chatbots and virtual assistants. Tie those to your payment AI and you get experiences like:
- A member asks, “Why was my card declined?” and the assistant can see the specific fraud score and walk them through options.
- A member wants to know, “Where am I earning the most rewards?” and the assistant surfaces personalized insights, not a generic FAQ.
That’s how AI becomes a member-centric banking layer, not just a back-office tool.
Leadership Under Pressure: Building An AI Roadmap Without Losing Your Soul
Matt Good’s quote hits home for a lot of credit union leaders:
“When you’re in tough times, you really see the leaders rise.”
AI and payments are exactly where that leadership is being tested. Members are anxious about fraud, overwhelmed by economic uncertainty, and tired of feeling like a number to big institutions. At the same time, boards want growth and efficiency, and your team is stretched thin.
Here’s the leadership play that works: use AI to deepen your cooperative values, not replace them.
A Simple, Honest AI Roadmap For Payments
I’ve found that the most effective credit union roadmaps are brutally straightforward:
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Pick 2–3 priority use cases. Common picks:
- Fraud detection and transaction monitoring
- Dynamic, member-centric rewards
- Contact center support for payment questions
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Define what “better for members” means in numbers. For example:
- 30% reduction in false fraud declines
- 15% lift in active card usage
- 20% reduction in call volume on “where’s my payment / why declined?”
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Be transparent with members. Tell them when you’re using AI around payments, what data you’re using, and how it benefits them. This is where credit unions can absolutely outperform big banks on trust.
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Train your people, not just your models. Your member-facing staff need simple language to explain AI decisions (especially around fraud and declines) without hiding behind jargon.
Most companies get this wrong by buying too many tools and never aligning them with member outcomes. Focus beats complexity every time.
How This Fits Into A Member-Centric AI Strategy
Payments are where fraud detection, loan decisioning, member service automation, and financial wellness intersect in real life:
- A flagged transaction can trigger both a fraud alert and a financial health check-in.
- Spending behavior captured through payments can feed personalized credit offers or debt consolidation suggestions.
- Real-time payment data can power budgeting nudges, such as alerts when a member is on pace to overshoot their food delivery budget.
This matters because the institution that connects these dots first—fraud, rewards, wellness, and service—wins member loyalty for years, not months.
Credit unions are uniquely positioned for this. Members already see you as a trusted partner, not a faceless brand. If you pair that trust with smart AI in payments and transparent communication, you don’t just stay competitive—you become the standard your community compares everyone else to.
So here’s the next step: pick one payments-focused AI project and commit to shipping it in the next 6–12 months. Maybe that’s an AI-powered fraud model, a smarter rewards engine, or an assistant that can actually answer card and payment questions accurately.
Members have already changed how they pay. The question now is how you’ll show up in that world—reactive and rules-bound, or proactive and intelligently member-centric.