AI, TruStage, and Serving Underserved Members

Most credit unions say they exist to serve the underserved. Very few can show, with data, how well they’re actually doing it.

Here’s the thing about member-centric banking in 2025: if you’re not pairing your purpose with data and AI, you’re leaving your middle‑market members exposed—and you’re leaving non-interest income on the table.

The TruStage story that Joe Boan shared on The CUInsight Network is a great example. TruStage is 100% focused on credit unions and the middle‑market consumers many big banks ignore. That’s noble, sure. But it’s also a strategy credit unions can scale with AI: serve the “financially fragile” better and grow.

This post looks at how that mindset—serving the underserved with protection products—intersects with AI for credit unions, and how you can turn member-centric intent into measurable outcomes.

---

Why the Underserved Middle Market Should Be Your AI Priority

Serving the underserved middle market with AI isn’t charity; it’s the most financially sound growth strategy most credit unions haven’t fully tapped.

Middle‑market and “thin file” members tend to:

• Carry higher day‑to‑day financial risk (job instability, medical costs, low savings)
• Be underinsured or unprotected (especially for life, disability, and income loss)
• Rely on the credit union as their primary or only financial partner

TruStage has built its business around this group, designing protection products—like life insurance and other coverage—around their real budgets and real behaviors. That’s exactly the segment where AI can have outsized impact, because their needs are often visible in data long before a human employee notices.

Why this matters for AI-driven credit unions

AI for credit unions shines when it’s applied to:

• Risk patterns: predicting which members are most exposed to income shocks
• Engagement gaps: spotting members who rarely interact but are financially vulnerable
• Offer matching: pairing each member with the right product at the right moment

Middle‑market members produce plenty of signals: paycheck timing, overdrafts, credit card utilization, missed utility payments, small-dollar loans. Translating those into proactive, personalized outreach—“Here’s a protection product that fits your situation and budget”—is where AI-driven member-centric banking becomes real.

The reality? The same AI tools that power fraud detection and loan decisioning can be tuned to answer a deeper question: Which members are one life event away from financial crisis, and what can we offer before that happens?

---

TruStage’s Focus on Protection Products Meets AI Personalization

TruStage’s model is simple: build products specifically for credit union members, especially underserved middle‑market consumers, and deliver them through credit unions as trusted partners. That’s perfectly aligned with a member-centric AI strategy.

“I really do believe what we're doing in the industry to help our members is noble, and TruStage wants to be a partner in that.” – Joe Boan

Where AI comes in is how you operationalize that partnership.

From generic promotion to targeted protection

Most credit unions still promote protection products in very generic ways:

• Posters in branches
• One‑size‑fits‑all email blasts
• Passive website banners

AI allows you to flip that model:

• Member-level propensity models: Predict which members are most likely to need, qualify for, and accept a protection product like life insurance.
• Context-aware triggers: Use life events (new baby, mortgage, auto loan, marriage, job change) to trigger tailored offers.
• Budget-sensitive recommendations: Offer coverage amounts that match each member’s cash flow, not some abstract “ideal” coverage.

For example, a basic AI model can segment members into:

• High-need, low-coverage: No existing protection, volatile income, dependents
• Moderate-need, some-coverage: Employer coverage only, new debt
• Low-need, well-covered: High savings, existing policies, stable income

Your outreach, messaging, and product recommendations should differ dramatically across those groups. That’s where TruStage’s product variety plus your AI-driven insight becomes a real member benefit.

Turning protection into sustainable income

Joe Boan makes a critical point: protection products don’t just help members; they also generate sustainable non‑interest income for the credit union.

AI can help you grow that income in a way that still respects your mission:

• Prioritize offers to members who both need and can responsibly afford coverage.
• Reduce manual outbound efforts with AI-driven outbound calling lists scored by likelihood to engage.
• Use AI‑assisted scripting in contact centers so staff can explain products in clear, member-friendly language.

Ethical line in the sand: if your AI model is purely optimizing for revenue per member instead of member resilience, you’re using the tools wrong. The credit union advantage is that your mission and your metrics don’t have to be at odds.

---

Applying AI Across the Member Journey for Underserved Segments

AI for credit unions adds the most value when it runs through the entire member journey, not just one-off projects.

1. Smarter onboarding for financially fragile members

From day one, you can use AI to identify which new members are likely to be underserved middle‑market consumers:

• Limited credit history
• Irregular direct deposits
• High housing cost relative to income

Instead of a generic onboarding email, they get:

• A tailored financial wellness plan
• Education on emergency savings and protection products
• A scheduled check‑in with a human advisor or virtual assistant

2. AI-driven financial wellness and coaching

Member-centric banking means you’re not just selling products; you’re coaching members into better outcomes.

AI-powered financial wellness tools can:

• Flag members at risk of future overdrafts or missed payments
• Suggest small, realistic actions (save $20 per paycheck, reduce a subscription, adjust repayment dates)
• Connect the dots between present behavior and future risk: “Based on your spending and debts, your family would face a shortfall of $X per month if you lost your income. Here are protection options that fit your budget.”

This is where TruStage-style protection solutions fit naturally into the story: not as add‑ons, but as answers to risks the AI has already surfaced.

3. Loan decisioning that doesn’t punish the underserved

The biggest danger with AI in lending is simply automating old biases faster.

Done right, AI-supported loan decisioning for underserved members should:

• Use alternative data (consistent rent, utilities, subscription payments) to assess reliability
• Focus on capacity and stability trends, not just FICO score snapshots
• Identify members who might not qualify for a traditional product but could safely handle a smaller, structured loan or line of credit

When you’re already integrating TruStage or similar products, you can design combined strategies:

• Offer lower‑rate loans paired with income protection
• Build automatic “safety rails” that keep members from slipping into predatory products outside your walls

4. Member service automation that keeps the human touch

Joe’s career path—from direct service roles to distribution leadership—highlights something AI can’t replace: trusted human relationships.

But AI can support those relationships:

• AI chat and voice assistants that answer basic questions about coverage, claims, or eligibility 24/7
• Agent assist tools that surface the right TruStage product and talking points while a member is on the phone
• Personalized next‑best‑action prompts inside your CRM, so your staff never miss a chance to protect a member when the moment is right

The goal isn’t to replace your people. It’s to make sure their time is spent on high‑value, high‑trust conversations, not on password resets and balance checks.

---

Governance: Keeping AI Aligned With the Credit Union Mission

AI for credit unions only works long-term if it stays aligned with your core purpose: improving members’ financial lives, especially for those who’ve been historically underserved.

Here’s what I’ve seen work inside purpose‑driven institutions:

Define “member-centric” in measurable terms

Don’t stop at slogans. Turn member-centric banking into metrics such as:

• Percentage of middle‑market members with some form of protection coverage
• Reduction in avoidable delinquencies or overdrafts after AI tools go live
• Net promoter score (NPS) or satisfaction specifically for lower-income households

Then ask a blunt question quarterly: Is AI improving these numbers, or just squeezing more revenue from the same members?

Build ethical guardrails into your models

When you roll out AI for fraud detection, loan decisioning, or product recommendations:

• Run bias audits across income, demographics, and geography
• Set explicit thresholds where offers are blocked if they’d increase a member’s financial fragility
• Give staff clear guidance: “If the model recommends X, and you see Y context, override it.”

The TruStage mindset—long‑term member security over quick commission—pairs well with this. If your product partner, your data team, and your compliance team are aligned, AI becomes a force multiplier for the mission, not a risk.

Prepare your people, not just your tech stack

Joe’s reflections on leadership (and yes, even his college baseball coach) point to something AI can’t teach: judgment.

Train your teams to:

• Treat AI outputs as decision support, not commands
• Ask “Does this offer genuinely improve this member’s resilience?”
• Use AI insights as conversation starters: “I’m seeing a pattern that tells me your family might be exposed if something happened to your income. Can we talk about options?”

That’s where member-centric banking feels different from a bank—even when AI is running in the background.

---

Turning Purpose Into a 2026 Roadmap

If your credit union wants to serve the underserved the way TruStage talks about—especially going into 2026—you’ll need both mission clarity and AI capability.

Here’s a practical short list:

1. Define your underserved segments clearly. Middle market, gig workers, thin‑file borrowers—write it down.
2. Audit your current protection coverage. How many at‑risk members have no life or income protection at all?
3. Stand up one AI use case focused on resilience, not revenue. For example: a model that flags at‑risk members and prompts staff to offer tailored protection or financial coaching.
4. Integrate with your product partners. If you work with TruStage or similar providers, loop them into your AI roadmap so products and models are aligned.
5. Measure outcomes relentlessly. Did delinquencies drop? Did more middle‑market members gain affordable coverage? Did satisfaction scores go up for lower‑income members?

Serving the underserved with AI isn’t about buying a shiny platform. It’s about doing what credit unions have always claimed to do—protect everyday people—using smarter tools and better data.

The next competitive edge in member-centric banking won’t come from who has the fanciest mobile app. It’ll come from who can look at a member’s real financial life, understand their hidden risks, and say: “We’ve got you”—backed by thoughtful AI and the right protection products.