From Reactive to Proactive: AI Member Experience

Most credit unions don’t lose members because of rates. They lose them because the experience feels harder than it should.

That’s the tension Kent Zimmer, CEO of CU NextGen, keeps coming back to:

“Being reactive is no longer an option if you want to remain relevant.”

This matters because member expectations aren’t inching forward anymore; they’re leaping. A member who orders groceries with one tap or gets instant support from a chatbot at midnight won’t accept a clunky, 12-step loan application or a “we’ll get back to you in 3–5 business days” message from their credit union.

Here’s the thing about AI for credit unions: it isn’t just a technology play. It’s a member experience strategy. The credit unions that win over the next few years will be the ones that use AI, no‑code tools, and automation to move from reactive service to proactive, personalized, member‑centric banking.

This post builds on themes from Kent’s conversation on The CUInsight Network and connects them to practical AI strategies you can put to work in 2025.

From Reactive to Proactive: What Member-Centric Really Means

A member-centric credit union doesn’t wait for members to complain before fixing friction. It anticipates needs, removes steps, and tailors experiences in real time.

Most credit unions still operate in a reactive pattern:

• NPS drops → launch a survey
• Call center volume spikes → hire temps
• Digital reviews slip → start a redesign project

By the time the response lands, member expectations have already shifted again.

A proactive, AI-enabled model flips that:

• You see patterns in member behavior before they turn into complaints
• You automate routine tasks before they clog up staff time
• You offer relevant products the moment a member’s data suggests a need

AI, RPA, and no-code platforms aren’t magic. But they are the practical tools that make this level of responsiveness possible without burning out your team or blowing up your budget.

Three Pillars of an AI-Driven Member Experience

An effective AI strategy for credit unions usually rests on three pillars: digital ease, intelligent automation, and personalization.

1. Digital Ease: Make Every Interaction Frictionless

The baseline expectation now is simple: everything should be as easy as the member’s favorite consumer app.

For credit unions, that means:

• Shorter, smarter forms using no-code workflows
• Mobile-first design for key journeys (account opening, card controls, loan applications)
• Contextual help, powered by AI, built into the journey—not hidden in a FAQ page

Practical example:

A member starts a loan application on their phone during a lunch break. They drop off halfway. An AI-enabled experience can:

1. Auto-save their progress
2. Trigger a gentle, personalized reminder via their preferred channel
3. Offer a one-click option to pick up where they left off

No one on your team has to watch dashboards to make that happen. Once configured on a no-code platform, that journey runs reliably in the background.

2. Intelligent Automation: Free Staff From Repetitive Work

The best member experience projects I’ve seen don’t start with chatbots. They start with back-office pain.

Robotic process automation (RPA) and workflow tools can:

• Move data between your core and ancillary systems
• Auto-verify documents and flag exceptions
• Trigger approvals or route tasks when conditions are met

Where this shows up in real life:

• New member onboarding checklists that update automatically
• Card dispute workflows that pre-fill data and assign tasks
• ACH or wire exception handling routed directly to the right specialist

Every repetitive task you automate means:

• Faster turnaround for members
• Fewer errors
• More time for staff to build relationships instead of chasing paperwork

3. Personalization: Right Offer, Right Moment, Right Channel

“Personalization” used to mean adding the member’s first name to an email. That’s table stakes.

AI lets you:

• Predict which members are likely to respond to a HELOC offer
• Spot early signs of financial stress and proactively offer help
• Tailor digital content based on real behavior, not just broad segments

For a member, personalization should feel like this:

• “You noticed I’m getting a lot of overdrafts and showed me a simple way to avoid them.”
• “You reached out about refinancing my auto loan right when I was actually frustrated with my payment.”

When done well, AI-driven personalization doesn’t feel creepy. It feels considerate.

Where AI Delivers Real Value for Credit Unions

If you’re leading a credit union, the real question isn’t “What can AI do?” It’s “Where does AI actually move the needle for our members and our team?”

Here are four areas where I’ve seen the strongest impact.

1. Smarter Member Service Automation

AI-powered virtual assistants and chatbots can handle the bulk of tier-1 questions:

• Password resets
• Balance and transaction questions
• Card activation and card freeze/unfreeze
• Branch hours, routing numbers, basic product info

Two keys to getting this right:

• Tight scope to start. Don’t try to automate everything at once. Pick 10–20 common questions and nail those.
• Easy escalation. Members should be able to jump to a human without repeating themselves.

The payoff:

• Shorter wait times on phones and chat
• 24/7 support for basic needs
• Staff freed up to handle complex or emotional conversations

2. Better Fraud Detection and Risk Decisions

AI models can spot anomalies in transaction patterns faster than manual reviews or basic rules engines. For example:

• Unusual location or device behavior
• Rapid small transactions that suggest card testing
• Patterns linked with account takeover attempts

When tied into your fraud and risk workflows, AI can:

• Trigger step-up authentication in real time
• Flag high-risk activity for human review
• Reduce false positives that frustrate members

Result: stronger protection without constant “card declined” headaches.

3. Faster, Fairer Loan Decisioning

AI-assisted underwriting (used responsibly and with strong governance) can:

• Pull and standardize data from multiple sources
• Surface risk signals earlier in the process
• Make consistent recommendations based on your policies

Your underwriters stay in control, but they work with better information and less manual work. Members see:

• Quicker decisions
• Clearer reasons when a decision is delayed
• Smarter pre-approvals for products that actually fit their profile

4. Financial Wellness and Proactive Guidance

This is where AI really ties into the credit union mission.

Instead of just showing balances and transactions, an AI engine can:

• Spot patterns like recurring overdrafts or rising credit card utilization
• Forecast potential cash shortfalls before they happen
• Suggest small, realistic actions—round-ups, payment changes, savings nudges

Examples:

• A push notification that says, “You’re on track to dip below $0 by Thursday. Here are two ways to avoid a fee.”
• A personalized recommendation in the app: “If you increase your car payment by $27, you’ll save about $430 in interest.”

That’s member-centric banking in practice: practical guidance, delivered at the moment it matters.

The CU NextGen Lesson: Flexibility Beats One-Size-Fits-All

CU NextGen, as Kent describes it, exists for a simple reason: most credit unions don’t want to become software companies, but they do want modern, flexible digital experiences.

Their approach (and I think this is the right one) is built around:

• No-code application development so credit unions can adjust workflows without waiting months for vendors
• RPA to connect old and new systems without a full core replacement
• AI to infuse intelligence into member interactions and staff tools

The key idea: your credit union’s differentiation should live in configuration, not custom code.

When you can quickly tweak:

• How your onboarding flow works
• Which members see which offers
• How exceptions route to the right staff

…you stay proactive. You respond to what members are actually doing this quarter, not what they were doing two years ago when your last big project launched.

Kent’s warning about being “reactive” is really a call to build this kind of flexibility into your tech stack now, not someday.

How to Get Started Without Overwhelming Your Team

You don’t need a seven-figure AI budget to start moving in this direction. You do need focus.

Here’s a simple path I’ve seen work for credit unions of all sizes.

Step 1: Pick One Member Journey That Hurts

Ask your team:

• Where do members complain the most?
• Where does staff feel the most friction?
• Which process creates the most manual rework?

Typical candidates:

• New membership onboarding
• Small-dollar loan applications
• Card disputes
• Address or contact info changes

Start there—not with a generic “AI strategy” deck.

Step 2: Map the Process and Identify Automation Targets

Whiteboard (or digitally map):

• Every step the member takes
• Every internal step staff takes
• Every system the data touches

Highlight:

• Repetitive data entry
• Simple rule-based decisions
• Manual handoffs between systems or teams

These are your first automation or AI opportunities.

Step 3: Use No-Code and RPA Before Custom Development

Before you write a line of code, ask:

• Can we configure this in our existing platforms?
• Can a no-code tool handle this workflow reliably?
• Can RPA bridge systems without a major integration project?

This is exactly the gap CU NextGen and similar CUSOs are trying to fill—giving you building blocks instead of bespoke, brittle one-offs.

Step 4: Add AI Where It Actually Improves Decisions or Experience

Good early AI use cases:

• Routing and triage (Which queue should this go to?)
• Knowledge retrieval (What does our policy say about this?)
• Personalized messaging (Which offer fits this member’s behavior?)

Avoid starting with:

• Complex credit models without a strong risk team
• “We want a chatbot that can do everything” projects

You want quick, visible wins to build trust—both with members and with your internal stakeholders.

Where This Fits in the AI for Credit Unions Series

Across this AI for Credit Unions: Member-Centric Banking series, a pattern keeps showing up: the credit unions that thrive blend mission with modern tools.

Member-centric banking isn’t a slogan. It’s a design principle:

• Use AI to listen at scale
• Use automation to remove friction
• Use data to show up with help before the member asks

If Kent Zimmer is right—and I think he is—being reactive is a slow path to irrelevance. The good news: the tools to be proactive are now accessible even to mid-size and smaller credit unions, especially through CUSOs and configurable platforms.

If your next planning session still treats AI as a “future trend,” you’re already behind. Treat it as a practical toolkit for fixing one member journey at a time, and you’ll start to see what a truly proactive member experience can look like.