Payment trends aren’t product trivia—they’re AI fuel. Here’s how credit unions can use mobile wallets, BNPL, and generational habits to power member‑centric banking.
Most credit unions now see over 70% of member interactions happening through digital channels, but their payment strategies are still built for plastic cards and branch visits.
That mismatch is where growth is leaking.
Here’s the thing about payment trends: they’re no longer just a “products and services” topic. They’re the front door to member relationships, data, and ultimately whether your credit union stays relevant in a world of real‑time, mobile‑first money movement. When I listen to leaders like Tom Pierce at PSCU talk about payments, I don’t hear card processing. I hear member behavior, risk signals, and opportunities for AI-driven credit union services.
This post connects the dots between what Tom discussed on CUInsight’s Payment Trends – PSCU episode and the broader theme of AI for Credit Unions: Member‑Centric Banking. We’ll look at how generational payment habits, mobile wallets, BNPL, crypto, and even “financial aesthetics” can feed smarter models for fraud detection, loan decisioning, and member engagement.
Payment trends are now an AI problem (in a good way)
Payment trends matter for AI because every tap, swipe, and tokenized transaction is training data.
Credit unions that treat payments as a commodity get trapped competing on price and interchange. Credit unions that treat payments as a behavior signal can build AI systems that:
- Detect fraud faster and with fewer false positives
- Price and decision loans more accurately for each member
- Automate service with context (not generic scripts)
- Deliver proactive financial wellness nudges at the right moment
Tom Pierce summed it up well:
“Leverage all the research and thought leadership that’s out in the payments space.”
I’d go one step further: don’t just read the research — feed it into your AI strategy. PSCU’s data on mobile wallet usage, BNPL, crypto curiosity, and generational differences gives you a roadmap for where to invest.
Generational payment habits: a blueprint for AI personalization
If you want credible “member‑centric banking,” you can’t treat Gen Z, Millennials, Gen X, and Boomers as one blob. Their payment preferences are wildly different — and AI systems should reflect that.
Gen Z and Millennials: mobile wallets and financial aesthetics
Younger members are leading the adoption of:
- Mobile wallets (Apple Pay, Google Pay, etc.) as default, not backup
- P2P platforms as their “everyday money” layer
- BNPL for both essentials and discretionary spend
- Financial aesthetics — apps and experiences that look and feel good, not just function
For AI, this is gold:
- Behavioral features: frequency of mobile wallet use, average ticket size, merchant types
- Risk signals: stacking BNPL with high utilization on revolving credit
- Engagement signals: how often they open your app; which tiles they tap; whether they customize dashboards
An AI‑enabled credit union can:
- Surface card‑on‑file and token management tools right in the mobile app
- Offer real‑time budgeting tips when BNPL usage spikes
- Present personalized product recommendations based on wallet and P2P behavior, not guesswork
If your models ignore mobile payment data, you’re essentially blind to how younger members actually live financially.
Gen X and Boomers: trust, stability, and hybrid behaviors
Older members often:
- Keep using physical cards and checks more than younger cohorts
- Adopt digital slowly, but stick with it once they trust it
- Care more about security signals and human backup
For AI, this means:
- Fraud models can weigh unusual digital behavior more heavily for this group
- Service automation should be more conservative: clear handoffs to humans, no surprise changes
- Financial wellness tools can focus on retirement, preservation, and simplified views
Member‑centric AI doesn’t stereotype, but it absolutely uses age‑cohort trends as priors — then refines its understanding from each member’s actual transactions.
Mobile wallets: from “nice feature” to AI training data engine
Mobile wallet usage is no longer a fringe stat on a slide deck; it’s a core input to your AI models.
What mobile wallet behavior tells you
Every wallet transaction can feed:
- Device and token data (for fraud models)
- Merchant detail (for lifestyle segmentation)
- Velocity patterns (for risk and affordability models)
For example:
- A member who almost exclusively uses tap‑to‑pay at grocery stores and transit is a different risk and product profile than someone using mobile wallet at luxury retail and travel.
- Sudden device changes or new geographies are early warnings for fraud models, especially if they don’t match historical behavior.
How AI can turn wallet data into value
Practical plays credit unions can run:
- Smarter fraud detection: use ensemble models that blend token data, device fingerprints, and historical merchant behavior.
- Dynamic credit line management: adjust pre‑approved limits based on real‑time spending health, not a 12‑month‑old bureau file.
- Personalized coaching: push just‑in‑time nudges like, “You’ve hit your dining budget for the week” based on actual wallet transactions.
Most credit unions already have access to this data through partners like PSCU. The gap isn’t data; it’s using it in AI workflows.
BNPL, crypto, and new rails: signal, not sideshow
A lot of boards still treat BNPL and crypto as distractions. That’s a mistake. You don’t have to offer them directly for them to matter.
Buy now, pay later: instant affordability signals
BNPL activity gives underwriting and financial wellness models insight that a FICO score never will:
- How often members use BNPL and for what categories
- Repayment behavior vs. traditional card or loan products
- Seasonal stress points (holidays, back‑to‑school, emergencies)
AI‑driven credit unions can:
- Flag when BNPL usage is a distress signal rather than a preference
- Offer consolidation options or structured installment loans tailored to that behavior
- Build affordability models that respect BNPL obligations, not ignore them
BNPL isn’t just competition; it’s a window into whether your lending and card products are actually fitting members’ lives.
Crypto curiosity: a proxy for risk tolerance and education needs
Whether or not you offer crypto services, your members are:
- Moving money to exchanges
- Asking questions about digital assets
- Consuming content on “alternative” finance
From an AI perspective, those behaviors can:
- Feed risk‑tolerance profiles for investment‑adjacent products
- Trigger education journeys in your app or outbound campaigns
- Inform fraud and AML models about patterns linked to scams and high‑risk venues
Smart credit unions don’t have to become crypto platforms. They just need AI systems that recognize crypto‑related patterns and respond with protection plus education.
“Financial aesthetics”: why UX now impacts data quality
Tom Pierce calls out a trend that too many credit unions underestimate: financial aesthetics. Members, especially younger ones, care about how their financial life looks on screen.
This isn’t vanity. It’s data quality.
Better design → more engagement → better AI
Clean, modern, intuitive experiences:
- Increase login frequency
- Encourage members to categorize transactions
- Make it more likely they’ll connect external accounts
All of that does two things:
- Improves member satisfaction and loyalty
- Feeds richer data into your AI models
If a member never opens your app, your AI can’t learn. If they open it daily, adjust budgets, and tag goals, your AI has a living, breathing picture of their financial life.
Practical moves for credit unions
Here’s what tends to work:
- Treat your digital banking UI as a primary data collection channel, not just a feature.
- Use micro‑interactions (e.g., “Name this goal,” “Tag this expense”) that feel fun but serve model training.
- Build explainable AI features into the experience: show why a spending insight or loan offer is appearing. That transparency builds trust and encourages more engagement.
Member‑centric banking isn’t just about the model; it’s about whether the member likes the “face” you put on it.
Turning payment insights into concrete AI projects
It’s easy to nod along with “AI and payments” as strategy talk. The real test is whether you can convert trends like the ones Tom Pierce highlights into actual projects in 2025.
Here’s a practical roadmap many credit unions can follow.
1. Start with fraud detection on digital payments
Impact is high, risk is manageable, and vendors are mature.
- Focus on card‑not‑present, mobile wallet, and P2P transactions.
- Combine network data (from partners like PSCU) with your own member history.
- Use supervised models trained on labeled fraud events plus anomaly detection for emerging threats.
Measure success with:
- Fraud loss rate
- False positive rate
- Member friction (e.g., unnecessary declines)
2. Layer in AI‑assisted loan decisioning
Use payment behavior as a second brain for underwriting:
- Wallet and P2P transaction patterns
- BNPL obligations and repayment behavior
- Deposit volatility and income stability
Start with:
- Augmented decisioning (AI as a recommendation engine, human as final approver)
- Clear fair‑lending controls and explainability requirements
You’re not replacing judgment. You’re giving lending teams a smarter risk map.
3. Build AI‑driven member service and wellness
Use payment and engagement data to:
- Route members to the right support (chatbot vs. human vs. specialist)
- Trigger proactive alerts about cash flow crunches or unusual activity
- Offer hyper‑relevant content: “We see more BNPL usage — here’s how to avoid debt traps.”
Tie this back to your brand promise as a credit union: acting in members’ best interest, not just pushing products.
Where this fits in your AI for Credit Unions journey
The payment trends Tom Pierce talks about aren’t side notes for the marketing team. They’re the raw material for member‑centric AI across fraud, lending, service, and financial wellness.
If your 2025 plan includes “AI initiatives” but doesn’t mention mobile wallets, BNPL, or generational payment behaviors, you’re missing the center of gravity. Payments are where you see members’ real lives — not just their balances.
So the next strategy conversation shouldn’t be, “Should we do AI?” It should be:
- “How do we use our payments CUSO and partners to expose richer data?”
- “Which member journeys — fraud protection, lending, or wellness — should we improve first with AI?”
- “How do we design digital experiences that members actually want to use daily, so our models keep learning?”
Credit unions that answer those questions well won’t just keep up with payment trends. They’ll turn those trends into long‑term member loyalty — and a clear competitive edge against big banks and fintechs.
If your team is mapping its AI roadmap right now, start with payments. The data is already there. The opportunity is whether you decide to use it.