AI won’t save credit unions—collaboration will. Here’s how CUNA Strategic Services and smart AI adoption can turn member-centric strategy into real results.
Why collaboration is the real AI strategy for credit unions
Most credit unions aren’t short on ideas. They’re short on capacity.
AI for credit unions is a perfect example. Leaders know they should be using AI for fraud detection, smarter loan decisioning, and 24/7 member service. But they’re also dealing with margin pressure, compliance workloads, core conversions, staffing gaps, and a hundred other fires. Standing up an AI roadmap from scratch? Tough.
That’s why what Barb Lowman and the team at CUNA Strategic Services (CSS) are doing matters: they’re turning “we should do AI” into “here’s the vetted solution, the partner, and the path” for credit unions of every size.
This post takes inspiration from Barb’s conversation on The CUInsight Network and connects it directly to our AI for Credit Unions: Member-Centric Banking series. The focus: how collaboration, curated partners, and intentional strategy can turn AI from a buzzword into real member value.
CSS’s role: turning complex problems into actionable options
The core value CSS provides is simple: they help credit unions find solutions to their challenges, then scale those solutions across the system.
“We help credit unions find solutions to their challenges.” – Barb Lowman
In practice, that means three things that are especially relevant for AI and member-centric banking:
1. Translating pain points into solution categories
Most executives don’t wake up saying, “We need an NLP-driven conversational AI layer.” They say:
- “Members are frustrated with long call wait times.”
- “Our fraud losses are creeping up and manual reviews can’t keep up.”
- “We’re losing indirect borrowers after the first loan.”
CSS excels at mapping plain-language problems to the right type of solution:
- Long call waits → AI-powered member service automation and intelligent routing
- Rising fraud losses → AI fraud detection and anomaly monitoring
- Poor indirect retention → Predictive analytics and targeted financial wellness journeys
That translation step is where many projects stall. The reality? It’s often the missing link between “we know the problem” and “we can see the path forward.”
2. Curating and vetting solution providers
Credit unions don’t have time to sort the serious AI providers from the hype. CSS does that homework:
- Checking security posture and data privacy
- Validating credit union experience and integrations
- Assessing regulatory awareness and documentation
- Confirming pricing models that work for smaller institutions
For AI specifically, a curated partner list saves months of RFPs and vendor demos. Instead of starting from a blank page, you start with a short list of solutions that already understand your world.
3. Making big solutions accessible to credit unions of all sizes
One of Barb’s recurring themes is scale. CSS serves large and small credit unions by:
- Negotiating system-wide agreements that improve pricing
- Encouraging shared learning through webinars and peer stories
- Helping smaller CUs plug into proven solutions without custom builds
For AI adoption, that’s huge. You don’t need a data science lab to use AI for fraud, loans, or member engagement anymore. You need solid partners, clear guardrails, and a strategy aligned to member needs.
Safeguarding your CU’s future with AI and smart risk management
CSS launched the “Safeguarding Your Credit Union’s Future” webinar series based on direct feedback from executives. That phrase—safeguarding your future—is exactly how credit union leaders should be thinking about AI.
The point isn’t to chase shiny tools. The point is to:
- Reduce risk
- Strengthen resilience
- Protect and deepen member trust
Here’s how AI fits into that picture.
AI for fraud detection and security
AI is particularly good at pattern recognition. For fraud and cybersecurity, that’s a big win:
- Real-time transaction monitoring flags out-of-pattern behavior before losses snowball
- Behavioral biometrics distinguish between a real member and a bad actor using stolen credentials
- Adaptive rules update based on new fraud typologies instead of waiting for a manual rule change
Most mid-sized credit unions can’t build this from scratch. Through relationships like CSS’s provider network, they don’t have to. They can adopt proven AI fraud solutions with:
- Pre-built integrations to card processors and cores
- Tunable risk thresholds aligned to board and ALCO expectations
- Clear reporting to support auditors and regulators
AI as a control, not a compliance risk
Some boards still see AI as a risk to be minimized. I’d argue it’s often the opposite: properly governed AI is a risk control.
Used well, AI can:
- Provide consistent decisioning that reduces human bias
- Log explainable factors in credit decisions and fraud alerts
- Standardize monitoring and exception handling
CSS’s emphasis on “safeguarding” is critical here. The right partners don’t just sell software; they help you build the policies, procedures, and documentation that make AI adoption exam-ready.
Collaborative AI: member-centric by design, not accident
The best AI for credit unions doesn’t start with the technology. It starts with member needs and works backward.
Barb talks about CSS building programs based on direct executive feedback. That’s exactly how AI initiatives should be framed: member and staff pain points first, tools second.
Where AI directly improves the member experience
Here are four practical areas where collaborative AI solutions are already reshaping member-centric banking:
1. Member service automation that feels human
Modern AI-powered chat and voice tools can:
- Answer common questions 24/7 (“What’s my routing number?” “Did my direct deposit hit?”)
- Complete simple actions (“Freeze my card,” “Transfer $200 to savings”)
- Hand off to humans with full context when things get complex
The key is design and training. A partner who understands credit union culture will:
- Use member-friendly language (not corporate jargon)
- Prioritize accessibility and inclusive design
- Route sensitive topics (loss of job, hardship, disputes) to live staff quickly
This is where collaboration shines. The tech provider brings the AI engine; the credit union brings the empathy and member understanding.
2. Smarter, fairer loan decisioning
AI-powered decisioning models, used responsibly, can:
- Analyze thousands of data points faster than legacy scorecards
- Identify thin-file or near-prime members who are actually good risks
- Shorten approval times from days to minutes
Done well, that means more approvals for good members and a better member experience during life’s big moments—cars, homes, consolidating debt.
But this only works when:
- Models are auditable and explainable
- Fair lending controls are baked in from day one
- There’s a human escalation path for exceptions
Again, this is where a CSS-style model helps: you’re not experimenting alone; you’re drawing on shared experience across the system.
3. Personalized financial wellness, at scale
Member-centric banking isn’t only about solving problems. It’s about helping people make better decisions before there is a problem.
AI-enabled tools can:
- Flag early signs of financial stress (rising utilization, missed utilities)
- Prompt proactive outreach—budgeting help, skip-a-pay, counseling
- Tailor financial education based on actual behavior, not generic segments
I’ve seen smaller credit unions punch way above their weight here by pairing a simple analytics platform with intentional member outreach—phone calls, emails, in-app messages. The AI surfaces the right members; the humans bring the relationship.
4. Competitive intelligence that informs strategy
Your members are constantly being targeted by fintechs and big banks. AI analytics can help you:
- See which products are leaking to competitors
- Identify local market gaps in lending or deposits
- Benchmark digital engagement against peers
CSS’s system-level vantage point is powerful here. They see what’s working—and what isn’t—across dozens or hundreds of institutions. When that insight is paired with AI data inside your own credit union, your strategic planning gets sharper fast.
Strategy, rebranding, and the culture side of AI
Barb mentioned that CSS is in the process of rebranding and reimagining their strategy to keep serving credit unions well into the future. That mindset—continual reinvention—is exactly what AI adoption demands from leadership.
AI success is mostly culture, not code
From what I’ve seen, credit unions that succeed with AI share a few traits:
- Curiosity at the top. Boards and CEOs asking, “How could this help members?” instead of “How do we avoid it?”
- Cross-functional squads. IT, operations, lending, marketing, and compliance solving one defined problem at a time.
- Intentionality. Clear success metrics, not “we implemented a chatbot, so we’re innovative now.”
Barb also talks about being intentional and spreading kindness. That might sound soft next to topics like fraud analytics and machine learning, but it’s actually a competitive advantage.
AI can handle more of the repetitive work. Done thoughtfully, that frees your people to do what credit unions do best:
- Have real conversations
- Show empathy during member stress
- Build trust over years, not just transactions
Reimagining member-centric banking with AI
AI isn’t a replacement for the credit union model; it’s a way to amplify it.
Think about what happens when you combine:
- A curated ecosystem like CSS’s provider network
- A member-obsessed culture inside your CU
- A clear focus on risk, resilience, and kindness
You get a version of AI that’s very different from what big tech is selling. It’s not about maximizing clicks or fees. It’s about:
- More accurate decisions
- Less friction in daily banking
- Earlier support for members struggling financially
That’s member-centric AI in practice.
Where to start: a practical AI roadmap for credit unions
If you’re looking at 2026 planning and thinking, “We can’t do everything,” you’re right. You don’t need to. You need a focused, collaborative roadmap.
Here’s a simple starting framework:
- Pick one member-centric problem.
- Example: “Reduce call center abandon rate by 30% in 12 months.”
- Talk to peers and partners.
- Tap into networks like CSS, leagues, and existing vendors: who’s solved this already?
- Select a proven AI-enabled solution.
- Prioritize explainability, CU experience, and integration over flashy features.
- Define clear guardrails.
- Compliance review, data governance, escalation paths, and member impact checks.
- Pilot, measure, iterate.
- Start small, track outcomes, gather member and staff feedback, then expand.
The worst AI strategy is aiming for a massive, three-year transformation that never really starts. The best strategy is a series of deliberate, member-focused wins that build momentum.
The next chapter of AI for credit unions is collaborative
CSS’s story—connecting, collaborating, and providing solutions across the credit union system—is exactly the model AI adoption needs right now.
You don’t have to:
- Build your own data science team
- Evaluate every vendor alone
- Carry all the implementation risk by yourself
You do need to:
- Anchor every AI initiative in member-centric banking
- Choose partners who understand the cooperative model
- Treat AI as part of safeguarding your credit union’s future, not a side project
The credit unions that will win over the next five years won’t be the ones with the flashiest tools. They’ll be the ones that combine collaboration, discipline, and genuine care for members—using AI as one more way to live that out.
The question isn’t whether AI is coming to credit unions. It’s who you’ll choose to build with, and how intentionally you’ll shape it around your members.