AI is reshaping how credit unions grow, manage risk, and serve members. Here’s a practical playbook for using AI to protect members and boost non-interest income.
Most credit union leaders I talk to are wrestling with the same knot: How do we grow, protect members, and stay relevant when margins are thin and risks are rising?
That tension sits at the heart of Mark Bugalski’s work as Executive VP and Chief Growth Officer at Allied Solutions. On The CUInsight Network, he talked about something that’s especially relevant right now: using technology-based solutions to grow non-interest income, mitigate risk, and support members through any economic cycle.
Here’s the thing about growth in 2025: you won’t get there with rates alone. The real engine is data, automation, and smart AI stitched into everyday operations. For credit unions, that doesn’t mean copying big banks. It means using AI to stay member-centric while improving efficiency and resilience.
This post connects the dots between Allied’s growth mindset and the core theme of this series, AI for Credit Unions: Member-Centric Banking. We’ll walk through practical ways AI can:
- Strengthen risk management
- Grow sustainable non-interest income
- Protect and deepen member relationships
- Keep your credit union competitive, even in a choppy economy
Innovation as a Growth Strategy, Not a Gadget
Growth for credit unions isn’t about chasing every new tool. Real growth comes from designing services around member needs and using AI to make that scalable, consistent, and low-friction.
Mark Bugalski framed Allied Solutions’ model simply: help credit unions grow, protect their business and members, and adapt regardless of economic conditions. AI fits that model perfectly when you apply it to specific outcomes, not buzzwords.
Here’s how I’d define a healthy AI strategy for credit unions:
- It protects members first (fraud, privacy, fair decisions)
- It supports staff, not replaces them
- It creates measurable financial impact (non-interest income, lower losses, higher engagement)
- It adapts to cycles – good when volume is high, essential when volume is low
If your AI roadmap doesn’t check those boxes, it’s probably a distraction.
Using AI to Navigate a Tough Economic Landscape
Credit unions are operating in a world of margin compression, higher credit risk, and more demanding members. AI won’t fix the economy, but it can radically improve how you respond to it.
1. Risk Mitigation: From Back-Foot to Forward-Foot
Risk management is where AI earns its keep fastest. AI models can spot emerging risk patterns weeks or months before they show up as charge-offs.
Practical applications:
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AI credit risk scoring
- Go beyond traditional scores by using more granular behavioral data: payment patterns, utilization trends, account activity
- Example: Flag members whose cash flow is deteriorating (e.g., 3 consecutive months of lower deposits + rising card utilization) and proactively offer restructuring or counseling
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Early warning systems for portfolios
- Use AI to segment your auto, credit card, and personal loan portfolios by probability of stress over the next 90–180 days
- Feed those insights directly into collections and member outreach strategies
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AI-powered collections optimization
- Predict which members are likely to self-cure vs. those who need personal outreach
- Recommend the right contact channel (text, call, email) and timing
This isn’t theoretical anymore. Credit unions using predictive models in collections often see:
- 10–20% faster recovery cycles
- Lower roll rates into later delinquency buckets
- Higher member satisfaction because outreach feels timely and helpful, not harassing
2. Fraud Detection That Actually Keeps Up
Fraudsters iterate faster than manual rule-writing. AI fraud detection gives your credit union a moving target defense instead of a static fence.
Use cases that work:
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Real-time transaction monitoring
- Behavioral models learn what “normal” looks like for each member and flag anomalies (new device, unusual location, atypical merchant types)
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Account takeover detection
- AI watches logins, device fingerprints, and navigation patterns to spot suspicious behavior before money leaves the account
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Application fraud screening
- Score applications for synthetic identity risk using cross-source patterns, not just simple rule checks
The member-centric angle matters here: good AI doesn’t just block fraud; it reduces false positives. Fewer declined legitimate transactions = more trust and fewer angry calls.
AI and Non-Interest Income: Growing Without Gouging Members
Bugalski highlighted something more credit unions should talk about out loud: optimizing non-interest income is a survival skill, not a dirty word. The trick is doing it in a way that aligns with member value.
AI can help you grow fee-based and value-add revenue by making services more targeted, relevant, and outcomes-driven.
1. Smarter Cross-Sell, Not Harder Cross-Sell
Spray-and-pray campaigns are a tax on member goodwill. AI-driven member analytics can change that.
Concrete moves:
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Propensity models for product offers
- Predict which members are most likely to benefit from:
- GAP and protection products
- Extended warranties
- Credit monitoring and ID protection
- Insurance solutions
- Trigger offers only when context makes sense: new auto loan, large purchase, new home, or life event signals
- Predict which members are most likely to benefit from:
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Personalized pricing and packaging
- Bundle products and services that speak to real needs: young families, gig workers, retirees
- Test different configurations and use AI to learn which bundles perform best over time
This approach strengthens non-interest income while still feeling consultative, not predatory.
2. AI-Enhanced Protection Products
Allied Solutions focuses heavily on risk and protection services. AI can elevate those solutions:
- Dynamic risk-based pricing for protection products
- Smarter claims analytics to speed up valid payouts and spot suspicious activity
- Real-time eligibility checks to present the right coverage at the right time in digital channels
When these tools are tuned well, you get three wins at once:
- Members feel better protected
- Staff spends less time on manual checks
- The credit union grows sustainable, recurring non-interest income
Member-Centric AI: Service, Not Surveillance
The fastest way to kill an AI initiative is to make members feel watched instead of supported. Member-centric AI is about anticipation and assistance, not intrusion.
AI in Member Service: Augment the Human, Don’t Replace Them
AI-powered digital assistants and contact center tools can take pressure off your team while actually raising service quality.
High-value applications:
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24/7 AI member assistants
- Handle routine questions (balances, hours, card freezes) instantly
- Escalate gracefully to humans for complex or emotional issues
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AI-assisted agents
- Surface relevant account data and suggested actions during live calls or chats
- Summarize calls and notes automatically so staff can focus on the member
A good rule of thumb: the more personal the situation (hardship, fraud, complaints), the more AI should support humans, not substitute for them.
Financial Wellness Tools That Actually Get Used
Most financial education content underperforms because it’s generic. AI can turn financial wellness into something personal, proactive, and measurable.
Examples that work:
- Personalized financial health scores with clear, simple explanations
- Contextual nudges: “Your subscriptions increased by $37 this month” or “You’re $150 away from your monthly dining-out average”
- Goal-based savings recommendations driven by spending and income patterns
Credit unions that use AI-powered financial wellness tools not only see higher engagement, but often noticeable improvements in:
- Average savings balances
- Reduced overdraft incidents
- Member retention, especially among younger segments
This is the core theme of this series – AI for Credit Unions: Member-Centric Banking – in action: using intelligence to make members feel understood, not just scored.
Turning Strategy Into Action: Where to Start
Most organizations stall because they try to “do AI” as one huge initiative. The reality? You’ll move faster by picking a growth problem and solving it with AI, not the other way around.
Here’s a practical sequence credit unions can follow:
Step 1: Clarify the Growth Problem
Pick one or two focus areas:
- Reduce fraud losses by 20%
- Increase non-interest income per member by $X
- Improve loan approval speed from days to minutes
- Cut contact center handle time by 15% while raising satisfaction
If you can’t define the problem in one line, the project’s not ready.
Step 2: Measure Where You Are
You don’t need perfect data, but you do need:
- Baseline metrics (loss rates, fee income, NPS, approval times)
- Clear data ownership (who’s responsible for what)
- Agreement on how success will be measured
Step 3: Choose Partners Who Understand Credit Unions
This is where organizations like Allied Solutions earn their keep. You want partners who:
- Already work with credit unions and know the regulatory and cultural realities
- Offer AI-enabled tools that plug into your existing cores and workflows
- Focus on risk, non-interest income, and member value, not tech for tech’s sake
Step 4: Design for Staff Adoption
If your frontline staff doesn’t trust the models, they won’t use them.
Make adoption easier by:
- Involving staff early in design and testing
- Explaining why the AI makes certain recommendations, not just what they are
- Training managers to use AI insights in coaching and performance reviews
Step 5: Start Small, Iterate Fast
Run pilots:
- Limited member segment
- Single product line
- One or two branches or a subset of agents
Then scale up once you’ve proven:
- Clear financial impact
- No unacceptable bias or compliance issues
- Positive member and staff feedback
The Strategic Edge: Innovate on Behalf of Your Members
Mark Bugalski summed it up simply: “Innovation is a game changer.” In the credit union context, that shouldn’t mean chasing shiny objects. It means thoughtfully using AI and technology to:
- Anticipate member needs
- Protect them from risk and fraud
- Grow sustainable revenue
- Keep your institution resilient through the next economic swing
For this AI for Credit Unions: Member-Centric Banking series, the pattern is clear: the credit unions winning right now are the ones that treat AI as a member service strategy wrapped in technology, not the other way around.
If your next planning session is coming up, ask one direct question: Where could AI help us better protect members, grow non-interest income, and free staff to do higher-value work?
Answer that honestly, and you’ll know exactly where to focus your next wave of innovation.