Building AI-Driven Trust in Member-Centric Credit Unions

“I focus on contributing and winning—and it's not winning for me—it's winning for everyone on my team.” – Pete Hilger, CEO, Allied Solutions

Trust isn’t a soft metric for credit unions. It’s the balance sheet. It’s fraud losses avoided, members retained, loans priced correctly, and AI systems that members actually believe are working in their best interest.

That’s why Pete Hilger’s story matters for anyone leading AI initiatives in credit unions. His focus on ground-up understanding, shared wins, and radical transparency is exactly what separates credit unions that use AI to deepen member relationships from those that quietly erode trust with every automated decision.

In this post, part of the AI for Credit Unions: Member-Centric Banking series, we’ll connect leadership lessons from Allied Solutions’ approach to practical ways credit unions can deploy AI—for fraud detection, loan decisioning, member service automation, and financial wellness—without sacrificing the cooperative values that make this movement different from big banks.

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Trust Is the Real AI Strategy for Credit Unions

If you’re a credit union leader, your AI strategy either builds trust or burns it. There isn’t much middle ground.

Hilger built Allied Solutions into a firm with a $3 billion annual impact on the credit union industry by doing something deceptively simple: understanding the business from the ground up and treating every partnership as long-term, data-open, and transparent.

That mindset translates directly to AI in three ways:

1. AI has to be explainable, not mystical.
   Members should understand why a decision was made—especially on loans, fraud flags, and fees.

2. Data sharing should feel like a partnership, not surveillance.
   When members share data, they’re offering trust. You have to show them what they get in return.

3. Wins must be shared across the ecosystem.
   AI projects that only reduce back-office cost and ignore member value rarely succeed for credit unions.

Most institutions focus on technology first—models, vendors, platforms. The better starting point is: What would a member-centric, trust-building AI experience actually look like? Then work backward.

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Ground-Up Leadership Meets Ground-Up AI Design

Hilger didn’t step straight into a C-suite. He co-founded a document destruction business with his brother, learned what it meant to run something real, then worked his way up through the family company before becoming president and ultimately CEO.

That experience—knowing how the machine works from the bottom—maps almost perfectly to responsible AI deployment.

Why “ground-up” matters for AI

The credit unions doing AI well share one trait: their leaders can explain the member journey end-to-end. They know where friction lives and where trust breaks.

For AI, that means:

• Understanding how a loan application actually flows through underwriting
• Seeing how members interact with online banking and the contact center in real life
• Knowing where fraud teams struggle with manual reviews and false positives
• Recognizing front-line staff pain points when they have to justify a system decision

Then, instead of just bolting on an AI tool, they ask:

“Where does AI reduce friction, increase fairness, or deepen advice—without turning us into a black box?”

A practical way to start “from the ground up”

If you’re mapping an AI initiative for 2026, run this simple exercise:

1. Pick one member-centric outcome.
   For example: faster small-dollar loan decisions for thin-file or gig workers.

2. Gather a cross-functional group.
   Underwriting, branch staff, call center, compliance, IT, and someone who talks to members daily.

3. Walk the current journey step by step.
   Where does the process slow down? Where do members feel judged or confused? Where do staff override the system?

4. Identify one place AI can help first, like:

   • Pre-qualifying members using alternative data
   • Explaining decisions in plain language
   • Predicting who needs a proactive touch before they abandon the application

5. Define the trust tests:

   • Would a member say, “That felt fair and clear”?
   • Could a front-line staffer explain the decision confidently?
   • Can you show your board how it improves member outcomes?

If you can’t pass those trust tests, you don’t have a member-centric AI use case—yet.

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Building Transparency Into AI: The Allied Approach Applied

One of the most interesting details from Hilger’s approach is how Allied Solutions openly shares financial details with credit union partners to build long-term relationships. No mystery margins. No black-box pricing.

That same posture is exactly what AI in credit unions needs.

Explainable AI for lending and risk

AI-driven loan decisioning and risk scoring can absolutely help members—especially those with thin credit files or non-traditional income patterns. But only if the system can be explained.

A member-centric AI lending program should be able to:

• Clearly list the top 3–5 drivers of an approval or denial
• Provide specific, actionable steps the member can take to improve (e.g., “Reducing utilization on revolving accounts below 30% would meaningfully improve your odds of approval.”)
• Offer a way for humans to review and override when appropriate

If your vendor can’t give you transparent decision factors and override controls, you’re not buying AI—you’re renting opacity.

Radical transparency inside the organization

Trust in AI isn’t just external. Staff have to trust the system too.

Some practices I’ve seen work well in credit unions:

• Monthly AI performance reviews with business leaders, not just IT
• Side-by-side comparisons of AI decisions versus legacy rules for a period of time
• Clear guardrails: what AI can decide on its own, what requires human review
• Front-line scripts so staff know how to explain AI-driven decisions in member-friendly language

All of this mirrors Allied’s philosophy: if you want durable, high-value relationships, you share how the engine works.

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AI for Fraud, Collections, and Fee Income—Without Losing the Member

Allied Solutions supports credit unions with non-interest fee income, asset recovery, and risk services. Those are also the hot zones where AI can either protect members or alienate them.

Smarter fraud detection that doesn’t punish good members

AI-powered fraud systems can spot patterns humans miss: device behavior, transaction velocity, merchant anomalies. The risk is obvious: too many false positives, and members start to feel like suspects.

A member-centric fraud AI program should:

• Combine AI with behavioral profiles built over time for each member
• Use tiered responses: step-up authentication first, hard declines only when highly confident
• Track false-positive rates as a core KPI, not a side metric
• Offer simple ways for members to confirm or dispute flagged transactions in app or via text

If fraud AI generates fewer losses but a spike in member complaints, you didn’t win. You just moved the cost.

Asset recovery and collections with empathy

Hilger’s background in asset recovery management pairs well with where AI is going in collections: more predictive, more personalized, and ideally more humane.

AI can help credit unions:

• Predict which members are likely to fall behind before they default
• Tailor outreach timing and channels (SMS vs. email vs. phone)
• Suggest personalized payment plans based on cash flow patterns

The difference between a member-centric program and a cold collections engine is the posture:

• “We saw a pattern that worries us. Can we help you stay on track?” vs.
• “Our system flagged you. Pay now.”

Same data. Very different trust outcomes.

Non-interest fee income that actually feels valuable

Allied helps credit unions generate non-interest income through protection products and services. AI can support this in ways that don’t feel like nickel-and-diming:

• Recommending high-relevance products based on life events (new car, new baby, new mortgage)
• Identifying members who are over-insured or under-insured and suggesting right-sizing
• Surfacing timely financial wellness nudges that build goodwill before any offer

When AI is used to match the right protective product to the right member at the right time—with clear value—fee income becomes part of the relationship, not a tax on it.

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Culture, Gratitude, and the Human Side of AI

Toward the end of his conversation, Hilger talked about his admiration for his mom, the importance of gratitude, and staying positive while balancing intense leadership demands.

That might sound far from AI, but I’d argue it’s the core.

AI in credit unions will only stay member-centric if the culture stays member-centric. You can’t bolt that on after you deploy models.

Here’s what I’ve seen work at credit unions that keep AI aligned with their mission:

• Clear principles: short, internal guidelines like “AI augments, humans decide” or “Members can always ask for a human review.”
• Regular gratitude rituals: sharing stories of how AI helped a member avoid fraud, get a fairer loan, or reduce financial stress.
• Ethics checkpoints: brief, structured reviews before any AI feature goes live: Who benefits? Who’s at risk? How will we know if we’re drifting from our values?
• Ongoing education: helping staff at all levels understand what AI is doing, so they don’t feel replaced—they feel empowered.

The reality? Most AI missteps in financial services aren’t technical failures. They’re cultural ones. Someone optimizes a model for efficiency and forgets to optimize the organization for empathy.

Credit unions are uniquely positioned to resist that drift—if they treat AI not as a cost-cutting engine, but as a trust multiplier.

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Where Credit Unions Go Next With Member-Centric AI

Hilger’s story is a reminder that scale and impact don’t have to come at the expense of values. A $3 billion industry impact can still be built on trust, transparency, and shared wins.

For credit union leaders planning AI investments for the next 12–24 months, here’s a simple roadmap grounded in that mindset:

1. Pick one high-trust use case as your flagship:

   • Transparent loan decisioning
   • Member-friendly fraud protection
   • Proactive financial wellness coaching

2. Design it from the ground up with members and staff in the room. Map the journey, define the trust tests, set clear success metrics beyond cost savings.

3. Demand explainability and transparency from every AI partner. If they can’t show you how decisions are made and how they’ll report outcomes, keep looking.

4. Train your people, not just your models. Equip front-line staff to explain AI outcomes and advocate for members when the system gets it wrong.

5. Tell the story to your members. Share how you’re using AI to protect them, serve them faster, and treat them more fairly. Trust grows when people understand your intent.

This series, AI for Credit Unions: Member-Centric Banking, has one core belief: credit unions shouldn’t copy the big-bank AI playbook. You have a different mission, a different ownership structure, and a deeper relationship with your communities.

Use AI to amplify that difference, not erase it.