BOSTON–A new report examines where AI isdelivering for credit unions and why it struggles to scale.
The report, from PYMNTS Intelligence and Velera notes that more than half of consumers already use AI for financial planning and budgeting, and four in 10 say they would feel comfortable using it to complete transactions—”trends that are most pronounced among younger generations.”
At the same time, the report states, most credit unions remain in early or selective stages of AI adoption, with only a small minority having deployed the technology broadly across their organizations. What all that means, according to the report, is that as member demand accelerates faster than organizational readiness, credit unions face a defining moment: how to scale AI swiftly while preserving trust, mission and operational integrity.
“Fortunately, this moment also offers a clear path forward: focusing on high-trust use cases, strengthening data and governance, and scaling through integration and partnership rather than disruption,” according to the report, titled The AI Moment: Members Invite CUs to LeadWhere AI Delivers Value for Credit Unions: Why AI Struggles to Scale at CUs From Pilot to Practice: Making AI Work for Credit Unions
The AI Moment: Members Invite CUs to Lead
According to PYMNTS, AI use is already mainstream for younger members, and trust in credit unions creates a rare opportunity: to guide adoption responsibly rather than simply react to it.
“Younger members already expect AI—and are ready to transact,” the analysis says.
The report cites research from Velera that found 30% of consumers use AI tools multiple times per week, and 55% already rely on AI for financial planning or budgeting.
“Importantly, 42% say they would feel comfortable using AI to complete financial transactions,” the analysis adds.
The research further reveals the figures rise dramatically among younger cohorts, with 80% of Gen Z and younger millennials using AI for financial planning.
“Their comfort with agentic AI is nearly as high, at 78% and 77%, respectively—versus just 13% among baby boomers and older consumers,” according to PYMNTS and Velera. “This generational divide presents both urgency and opportunity. Credit unions must serve AI-ready members now while continuing to support members who prefer traditional channels, creating parallel paths rather than forcing abrupt change.
Members Want Guidance from Their Credit Unions
Rising AI adoption does not mean consumers want to navigate new technologies alone. Velera reports that nearly six in 10 consumers (57%) say they would be likely to use educational classes or resources on AI if offered by their financial institutions (FIs). This figure rises to 63% among credit union members, representing a substantial jump from 51% in 2023. At the same time, 85% of consumers say they see credit unions as good sources of financial advice.
This combination of demand and trust positions credit unions uniquely. AI adoption can become an extension of the CU advisory model—helping members understand how AI works, how their data is used and how to avoid emerging fraud risks—rather than simply rolling out new tools without context.
Awareness Up, Adoption Lags
PYMNTS and Velera said the research has found awareness is rising, but enterprise adoption lags.
The companies cited a CULytics survey that found while 50% of credit union leaders described themselves as somewhat familiar with AI applications, just 17% said they were very familiar.
That research further found about 42% reported implementing AI in specific areas of their operations, but only 8% said AI is used across multiple facets of the organization.
“This gap between experimentation and enterprise deployment defines the current moment,” the analysis observes. “Credit unions are aware of AI’s potential and are testing use cases, but scaling still demands foundational change.”
Where AI Delivers Value for Credit Unions
From personalization and member service to fraud prevention and operations, credit unions are applying AI where it most directly impacts trust, experience and performance, according to the report.
“Credit unions have long differentiated themselves through close member relationships, but delivering personalized experiences consistently across digital channels has become increasingly difficult. AI is helping bridge that gap enabling personalization at scale that manual processes cannot match” according to PYMNTS and Velera. “Machine learning models can analyze transaction data, behavioral signals and life-stage indicators to tailor offers, messaging and product recommendations in real time—moving beyond static segmentation to respond dynamically to evolving member needs.”
Additional Findings
Additional findings in the research include:
- At 58% adoption, CULytics finds chatbots and virtual assistants to be the leading credit union AI application, while Cornerstone Advisors shows deployment more than doubling within three years to 45% in 2025—far outpacing 26% for banks.
- Fraud management has emerged as one of the most critical AI use cases for credit unions. Cornerstone Advisors identifies it as the second-most common generative AI application (48%), behind only contact centers (74%), while Alloy reports a 92% net increase in AI fraud prevention investment among credit unions in 2025. By contrast, fraud management ranks lower among banks’ AI priorities, at 39%, the companies said.
All About Trust
The research posits that as AI adoption expands, trust becomes the “gating factor—not because credit unions distrust technology, but because they must be able to explain, govern and stand behind AI-assisted decisions.”
Making AI Work
The researchers are offering these recommendations:
From Pilot to Practice: Making AI Work for Credit Unions
- Prioritize high-trust, high-impact use cases—without forcing abrupt change. “Focus first on AI applications that enhance personalization, strengthen member service and protect against fraud, while maintaining traditional channels for members who prefer them. Parallel paths enable adoption without alienation.”
- Strengthen data readiness and governance early. “AI effectiveness depends on accessible, reliable data and clear accountability. Establish data strategies, governance frameworks and human oversight to ensure AI-assisted decisions are explainable, auditable and aligned with cooperative values.”
- Leverage partners to simplify integration and scale responsibly. “Credit union service organization (CUSO) partners and shared intelligence models can reduce integration complexity, accelerate deployment and improve outcomes—particularly in fraud prevention and account validation.”
- Lead through education and transparency. “Meet rising member demand for AI guidance by explaining how tools work, how data is used and how risks are managed—reinforcing trust while supporting adoption.”
