AI, Brand, and Data: How Credit Unions Actually Grow

Most credit unions are sitting on more member insight than they think—and using less of it than they should.

That tension sits at the heart of a recent conversation between CUInsight’s Lauren Culp and Ben Stangland, President and COO at Strum. Strum grew out of Weber Marketing Group and now operates as two businesses: Strum Agency (brand and growth strategy) and Strum Platform (financial marketing analytics and AI for credit unions).

Here’s the thing about AI for credit unions: the technology only matters if it deepens member relationships and strengthens your brand. If AI-driven analytics don’t show up as better experiences for real people—on your site, in your app, in your branches—it’s just noise.

This post walks through what Ben’s approach gets right and how you can apply it:

• Why brand, data, and AI have to move together
• How to use AI and analytics to stay truly member-centric
• Practical steps for breaking data silos and empowering your team
• What rebranding and renaming really look like in a data-driven world

This fits squarely in our “AI for Credit Unions: Member-Centric Banking” series: the focus is not tools for their own sake, but using intelligence to serve members more personally, fairly, and consistently.

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Brand, Data, and AI Have to Work as One System

Credit union growth works best when brand, data, and AI aren’t separate projects—but one connected system.

Ben’s team at Strum structures this into two sides:

• Strum Agency – naming, branding, marketing, and growth strategy
• Strum Platform – cloud-based analytics, segmentation, and financial marketing AI

That split is helpful for thinking about your own roadmap.

Brand sets the promise, AI proves it daily

Your brand is basically a promise: who you’re for, what you stand for, how you show up. AI and analytics test whether you’re actually living that out.

For example:

• Your brand says you prioritize financial wellness.
• Your data shows your members are paycheck-to-paycheck and under-insured.
• Your AI can predict which members are most at risk and nudge your team or your digital channels to offer help at the right moment.

If those three pieces aren’t aligned, members feel it. You can’t claim “member-centric banking” while blasting generic offers to everyone with a pulse.

Focus beats “more campaigns” every time

Ben’s point about creating focus in growth strategy is one more credit unions need to internalize. Most institutions don’t lack data or marketing output. They lack clarity about who their target member is and what they actually need next.

AI can help narrow that focus:

• Identify your highest-value segments (by lifetime value, engagement, or share of wallet)
• Spot lookalikes in your broader membership and community
• Predict next-best-product or next-best-action for each segment

The result isn’t more noise—it’s fewer, smarter touches that feel personal, not pushy.

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Member-Centric AI Starts With Knowing Who You Serve

AI for credit unions only works when it’s grounded in a clear understanding of your target member and mission.

Ben emphasizes staying true to the credit union’s purpose while sharpening who you’re really for. I strongly agree: vague “everyone in our charter” definitions lead to vague data, vague offers, and vague growth.

From demographic to behavioral understanding

Traditional segmentation leans hard on age, income, or location. Useful, but shallow. Modern AI-driven credit union analytics go deeper:

• Behavioral data – transaction patterns, digital channel use, product mix
• Life-stage signals – first paycheck deposits, rent payments, childcare expenses
• Engagement signals – log-in frequency, contact center calls, branch visits

When you blend these with your brand strategy, you move from:

“We serve young professionals in our county”
to
“We serve early-career teachers who are overwhelmed by student debt, value community, and prefer digital tools with occasional in-person support.”

That level of clarity is what turns generic AI models into member-centric decision engines.

Examples of AI-powered, member-aware experiences

Once you’ve defined your target members, AI can help you:

• Personalize onboarding – New member joins from an educator SEG? Cue a tailored welcome series with student loan guidance and direct deposit setup.
• Predict financial stress – Machine learning models flag members whose cash-flow volatility is rising, triggering proactive outreach with counseling or short-term relief options.
• Support financial wellness journeys – Recommend savings nudges, credit score coaching, and goal tracking based on each member’s behavior and history.

Notice the pattern: this isn’t about selling more products. It’s about helping members progress, which is exactly where cooperative financial institutions should excel.

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Break the Data Silos: “Empower Employees With Information”

Ben’s quote from the episode is blunt and accurate:

“The more information you can empower employees with, the better the whole organization is.”

Data silos kill member-centric banking. If your contact center, branches, lending, marketing, and digital teams are all looking at different versions of the member, AI can’t help you much.

What a unified data view actually looks like

A truly member-centric data environment means:

• A single, trusted member profile that aggregates core, LOS, CRM, digital banking, and card data
• Clear governance so people know what they can use and how
• Role-based access instead of blanket “no” policies that keep insight locked up

Cloud-based computing and modern data warehouses make this more achievable today than even five years ago, but tools alone don’t fix culture.

Practical steps to start sharing data wisely

You don’t need a massive transformation to move in the right direction. I’ve found these steps work well for credit unions of all sizes:

1. Pick 1–2 “member journeys” to improve (e.g., first 90 days after joining, first mortgage, collections outreach).
2. Map what data each team sees at each step—and where they’re blind.
3. Stand up a lightweight shared dashboard (even if it’s basic) with the must-have fields for those journeys.
4. Train front-line staff on how to read the data and what actions they can take.
5. Measure one clear outcome (NPS, product adoption, roll rate, digital engagement) and iterate.

The goal isn’t a perfect enterprise data lake on day one. The goal is to prove the value of shared insight in ways your team can feel quickly—then scale from there.

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Rebranding, Renaming, and the Role of Analytics

Strum’s own journey—from Weber Marketing Group to Strum Agency + Strum Platform—is a perfect example of using data and brand strategy together.

Credit unions considering renaming or rebranding face real risk. Done well, a brand update unlocks new growth. Done poorly, it confuses members and staff.

When should a credit union consider a rebrand?

From what Ben shared (and what I’ve seen in the field), there are a few strong signals:

• Your charter or field of membership has expanded beyond the current name.
• Your brand perception scores lag your actual member experience.
• Your name or identity conflicts with your digital ambitions (e.g., local-only references when you’re serving members statewide or nationally).

AI and analytics can make this less subjective by providing hard evidence:

• Sentiment analysis of member feedback
• Market awareness and confusion metrics
• Simulation of how different target segments might respond to new positioning

How AI helps de-risk a rebrand

Here’s how a member-centric AI approach supports renaming and rebranding:

• Identify core vs. edge members – Understand which members are most loyal, which are at risk, and how brand changes might affect them.
• Model impact on growth segments – Use predictive analytics to see which brand directions align with your highest-potential audiences.
• Personalize the rollout – Tailor communication to different segments: long-time members get one narrative, new digital-only members get another.

Strum’s dual structure—agency + platform—basically bakes this in. Your credit union can mirror that mentality: brand work informed by data, and data work guided by brand.

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AI for Credit Unions: Where to Start in 2026

AI in credit unions doesn’t have to be a massive, abstract initiative. The smartest leaders I see follow a simple rule: start with one real member problem and one measurable business outcome.

Here are three practical starting points that match Ben’s philosophy and the broader AI for Credit Unions: Member-Centric Banking theme.

1. Smarter, fairer loan decisioning

Use AI models (with strong governance) to:

• Improve speed-to-yes for qualified members
• Reduce manual touches on straightforward applications
• Flag edge cases for human review, not auto-decline

Member benefit: faster answers, more consistent decisions, clearer reasoning.
Credit union benefit: higher throughput, better risk segmentation.

2. Proactive fraud and risk detection

Machine learning models can spot unusual patterns—new devices, atypical geographies, strange spend spikes—far faster than manual rules.

To keep this member-centric:

• Pair alerts with clear, empathetic communication
• Use channel preference data to contact members the way they actually respond
• Track and report false positive rates so you’re not frustrating your best members

3. Intelligent member service automation

AI-powered chat and call assistants shouldn’t replace your people; they should filter and augment.

• Let AI handle routine requests (balances, hours, basic FAQs)
• Route complex or emotional situations to human agents with context filled in
• Use conversation data to spot emerging member needs and update products or policies

Done well, this is where your brand really shines: members feel like the credit union “knows” them, even in a digital interaction.

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The Credit Union Advantage in an AI-Driven Future

The credit union movement has a built-in edge: a member-first purpose that big banks can’t fake. Ben’s message from Strum is essentially this: use AI and data to make that purpose real in every interaction.

That means:

• Aligning brand, analytics, and AI so they tell the same member story
• Sharing data across teams instead of guarding it in silos
• Using machine learning and cloud-based tools to understand members as people, not just accounts

If your leadership team is talking about AI for credit unions but struggling to connect it to brand and member experience, that’s your opening. Start small, pick one journey, empower staff with better information, and prove the value.

The next few years will make it very clear which credit unions used AI to become more human—and which used it to feel more like everyone else. Which side do you want your members to experience?