AI only matters for credit unions if it improves member experience, risk, and efficiency. Here’s how to make AI practical, measurable, and member‑centric.
How Credit Unions Can Make AI Actually Work for Members
Most credit unions aren’t struggling with AI because of the tech. They’re struggling because they’re trying to bolt AI onto broken processes and outdated data.
That’s why Steve Kass’s line from the CUInsight Network episode stuck with me:
“We help credit unions elevate their operational outcomes with AI powered solutions.”
Here’s the thing about AI for credit unions: if it doesn’t reduce friction for staff and clearly improve the member experience, it’s just another shiny project. The real opportunity is using AI to increase efficiency, manage risk, and deliver member‑centric banking at scale.
This post builds on the conversation with Steve Kass, Chief Services Officer at Trellance, and connects it to the broader AI for Credit Unions: Member‑Centric Banking series. We’ll walk through what actually works, where to start, and how to avoid the common traps that hold credit unions back.
1. The Real Business Case for AI in Credit Unions
AI in credit unions only matters if it ties directly to measurable outcomes: growth, risk, and member experience.
Three questions every CEO should ask about AI
Before funding any AI initiative, leadership should be able to answer:
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What specific outcome are we targeting?
Examples: reduce call center volume by 25%, cut fraud losses by 30%, improve loan approval speed from days to minutes. -
How will members feel the difference?
Faster approvals, fewer fraud scares, clearer financial guidance, 24/7 support. -
How will staff feel the difference?
Less time on repetitive tasks, fewer manual workarounds, more time for complex member needs.
If you can’t map AI to clear answers for those three, you don’t have an AI strategy. You have a pilot for the sake of a pilot.
Where AI already proves its value
From what I’ve seen across the industry (and what Steve Kass described), the strongest early wins are in:
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Operational efficiency
- AI‑assisted call routing and knowledge search for MSRs
- Automated document classification and data extraction for lending and back office
- Predictive models for staffing and workload
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Risk management and fraud detection
- Real‑time transaction monitoring with anomaly detection
- Dynamic fraud risk scoring instead of static rules only
- Early‑warning models for delinquency and charge‑offs
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Member experience and personalization
- Smart digital assistants that hand off cleanly to humans
- Next‑best‑offer and next‑best‑action models based on behavior
- Proactive financial wellness nudges instead of generic campaigns
The reality? You don’t need a huge innovation lab to start. You need a few clearly defined use cases, clean data, and a partner who understands how credit unions actually operate.
2. Evolving From Hype to Practical AI in Financial Services
AI in financial services has moved from “someday” to “quietly everywhere.” The shift now is from experimentation to embedded capability.
The maturity curve: where most credit unions are
Most credit unions I talk to fall into one of four stages:
- Curious – Reading articles, listening to podcasts like CUInsight Network, maybe running vendor demos. No production use yet.
- Exploring – A chatbot pilot here, a basic analytics model there. Limited integration with core systems.
- Operational – AI models actively used for fraud monitoring, targeting, or support. Governance and monitoring in place.
- Strategic – AI is treated as core infrastructure. Use cases are prioritized like any other business investment.
Steve Kass and the Trellance team focus on moving credit unions from stages 1–2 to 3–4: less talk about algorithms, more about outcomes, integration, and adoption.
Why “AI projects” often stall in credit unions
Most AI projects don’t fail because the model is bad. They fail because:
- Data is siloed across the core, card processor, LOS, CRM, and digital channels
- IT and business teams haven’t agreed on a shared problem statement
- Staff weren’t trained, so they don’t trust or use the tool
- There’s no clear metric to define success
The better way is to treat AI like any other strategic initiative:
- Start from a business problem (e.g., “our fraud losses increased 18% in 12 months”)
- Quantify the current impact in dollars and member experience
- Pick the smallest high‑value use case that can show visible results in 90–180 days
- Build a simple roadmap around that use case, not around “AI” as a monolith
3. Where to Start: Practical AI Use Cases for Member‑Centric Banking
If you’re at the beginning of your AI journey, the best entry points are repetitive processes that frustrate members and staff.
3.1 Member service automation that doesn’t feel robotic
AI‑powered virtual assistants can handle a big chunk of routine questions if they’re designed around member intent, not just keywords.
Well‑designed assistants can:
- Answer common questions: balances, due dates, routing numbers, card disputes
- Guide members step‑by‑step through tasks: card activation, address changes, password resets
- Hand off to humans with full context when things get complex
The member sees faster answers. Your staff sees fewer repetitive calls and chats.
What works:
- Start with the top 20–30 reasons members contact you
- Train the assistant on your actual call/chat transcripts
- Measure containment rate and member satisfaction, not just “number of chats”
3.2 Smarter fraud detection that protects trust
Fraud is where AI quietly earns its keep. Rules alone can’t keep up with evolving patterns, especially on cards and digital channels.
AI‑driven fraud models can:
- Flag unusual behavior based on a member’s normal patterns, not just generic thresholds
- Score each transaction in real time and prioritize investigations
- Reduce false positives so members aren’t constantly annoyed by declined transactions
For member‑centric banking, this balance matters. Members remember every false decline. They rarely see the cases you prevent.
3.3 Faster, fairer loan decisioning
AI doesn’t have to replace underwriters. Used well, it acts like a highly consistent assistant.
Examples of value:
- Pre‑qualifying members based on internal behavior, not just a bureau score
- Prioritizing files that need human review vs those that follow clear patterns
- Surfacing alternative structures (different terms, collateral, or products) the member might still qualify for
Your goal isn’t “fully automated lending at any cost.” It’s faster approvals for straightforward cases so your team can focus on complex or nuanced decisions.
4. Building the Foundation: Data, Governance, and Culture
Every strong AI program in a credit union is built on three pillars: data quality, clear governance, and a culture that’s open to change.
4.1 Data: from scattered to strategic
AI is only as good as the data underneath it. Trellance and similar providers have built their businesses on one core reality: credit union data is rich, but scattered.
A realistic path forward:
- Unify key data sources: core, cards, lending, digital, contact center
- Standardize definitions: what’s a “member,” a “household,” an “active card,” etc.
- Centralize access so analytics and AI models aren’t built in isolation
I’ve seen credit unions achieve big gains just by getting a single, trusted member data view in place before they add advanced models.
4.2 Governance that keeps regulators comfortable
Regulators aren’t anti‑AI. They’re anti‑black‑box.
Governance structures that work typically include:
- A cross‑functional AI/analytics committee (business, IT, risk, compliance)
- Model documentation: what data goes in, what the model predicts, how it’s monitored
- Regular performance reviews: accuracy, bias checks, override rates
- Clear policies on how staff should interpret and use AI‑based recommendations
If you can explain your models clearly to your board and your examiners, you’re on safer ground.
4.3 Culture: from fear to partnership
Staff often worry that AI will replace them. In practice, the strongest credit union programs use AI to elevate staff.
What this looks like:
- Member service reps use AI to surface answers and next steps faster
- Lenders use AI‑driven insights to have richer conversations with members
- Marketing teams use predictive segments to send fewer, more relevant campaigns
Steve Kass also talked about work‑life integration and experiences. That mindset matters here: AI done right gives people more time for impactful work and less time on mind‑numbing tasks.
5. A Simple 90‑Day AI Starter Plan for Credit Unions
If you’re serious about AI for member‑centric banking but not sure where to begin, here’s a practical starter roadmap.
Step 1: Pick one high‑value, low‑complexity use case
Good first candidates:
- Call center assistant to help staff find answers faster
- Transaction‑based alerts for potential fraud on debit/credit cards
- Propensity model to identify members likely to accept an auto refinance offer
Step 2: Define success in numbers
For that single use case, align on:
- Current baseline (e.g., average handle time, fraud loss rate, campaign response rate)
- Target improvement (e.g., 15–25% reduction or lift)
- Timeframe (90 days to see directional impact)
Step 3: Choose your partner and data path
You don’t need to build everything in‑house. Providers like Trellance specialize in helping credit unions:
- Connect to core and ancillary systems
- Normalize and enrich the data
- Deploy pre‑built models tuned for credit union use cases
The key is selecting a partner who speaks credit union, not just “financial services” in general.
Step 4: Train staff and communicate clearly
Before launch:
- Explain what the AI tool does and doesn’t do
- Show staff how it makes their day easier, not harder
- Build feedback loops so front‑line teams can report what’s working and what isn’t
Step 5: Review, refine, then expand
After 60–90 days:
- Compare metrics against your baseline
- Collect member and staff feedback
- Adjust the model or workflows where needed
- Decide whether to scale that use case and which one to tackle next
This disciplined, outcome‑driven approach is exactly how credit unions move from “experimenting with AI” to running on AI‑assisted operations.
Where AI for Credit Unions Goes Next
For credit unions, the most promising future isn’t about having the flashiest AI tools. It’s about quietly building member‑centric banking where every interaction feels faster, smarter, and more personal.
Steve Kass summed up Trellance’s role as supporting credit unions as their AI capabilities evolve. That’s the right frame: AI isn’t a one‑off project, it’s a capability you grow over time.
If you’re leading a credit union right now, the next moves are clear:
- Choose one or two focused AI use cases tied to real business pain
- Invest in your data foundation and governance
- Work with partners who understand both AI and the credit union model
The credit unions that act now won’t just “keep up with technology.” They’ll set a new bar for what members expect from a financial partner.
The question isn’t whether AI will shape member expectations. It’s which credit unions will decide to shape that future instead of reacting to it.