Artificial intelligence (AI) has moved quickly from novelty to necessity across financial services, and credit unions (CUs) are no exception. 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, 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. As member demand accelerates faster than organizational readiness, CUs 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.

• The AI Moment: Members Invite CUs to Lead

• Where AI Delivers Value for Credit Unions

• Why AI Struggles to Scale at CUs

• From Pilot to Practice: Making AI Work for Credit Unions

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The AI Moment: Members Invite CUs to Lead

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.

Consumer behavior signals that AI is no longer experimental for large segments of the population. According to Velera research, 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.

These 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. 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.

63%

of CU members say they would be likely to attend educational classes on AI if offered by their CUs.

Members want guidance—and trust credit unions to provide it.

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 is rising, but enterprise adoption lags.

As member demand accelerates, internal readiness remains uneven. According to a CULytics survey, while 50% of credit union leaders described themselves as somewhat familiar with AI applications, just 17% said they were very familiar. 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. Credit unions are aware of AI’s potential and are testing use cases, but scaling still demands foundational change.

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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.

AI is enhancing personalization at scale.

92%

Net increase in AI fraud prevention investment among credit unions in 2025

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. 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.

AI is reshaping member service across digital and assisted channels.

Member-facing tools are among the most visible AI applications at credit unions. Unlike earlier generations of scripted chatbots, modern virtual agents can understand natural language, access member data and resolve more complex inquiries.

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. This reflects where AI delivers the most immediate value for CUs: in service-facing roles where trust, responsiveness and relationship depth define competitive advantage.

Fraud prevention is where AI becomes non-optional.

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%.

With fewer buffers between frontline experience and reputational impact, fraud losses and false declines are often felt more acutely at credit unions than at larger FIs. As a result, AI-powered fraud management has become both a defensive necessity and a service strategy—enabling faster intervention while minimizing friction for legitimate members.

AI is streamlining operations and accelerating decisions.

Beyond service and security, AI is increasingly applied to core operations. Inclind research highlights its role in automating reconciliation, underwriting and analytics for CUs across transactional, behavioral and demographic data. By reducing manual work, AI frees staff to focus on higher-value activities such as advisory services and strategic planning. In areas such as lending, AI helps reduce bottlenecks and error rates while promoting faster decision-making.

These gains are no longer theoretical. CULytics finds 50% of credit unions using AI in credit underwriting and marketing. When asked about AI’s greatest opportunities, responses were evenly split among improving member experience, increasing operational efficiency and accelerating lending decisions. Cornerstone Advisors reinforces this, showing lending (46%) as CUs’ third-most common AI function after call centers and fraud management—far ahead of banks, where lending ranks much lower.

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Why AI Struggles to Scale at CUs

AI doesn’t stall because credit unions lack interest—it stalls when data isn’t ready, decisions aren’t explainable and systems can’t connect.

Data foundations remain the biggest constraint.

Despite growing interest, structural challenges continue to slow AI adoption. Q2 research, citing SRM’s Mark Sievewright, points to ongoing policy development, conservative risk postures and limited data readiness as primary constraints. As Sievewright noted, “A lot of credit unions do not have a data strategy; a lot of them don’t have their data in a place where it can be accessed readily. And so, no matter how much AI we apply to that, it won’t do much good if we don’t have our fundamentals in place around data.”

11%

of credit unions rate their data strategy as very effective.

Cornerstone Advisors’ self-assessments reinforce this gap: 23% of credit unions rate their data strategy as not effective, while only 11% consider it very effective. Similar weaknesses appear in data governance and the use of data to enhance member experience and operational efficiency.

Trust and explainability determine whether AI can scale.

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. In highly regulated, relationship-driven environments, “black box” models and fragmented data can undermine confidence among members, regulators and internal teams alike.

According to PYMNTS Intelligence, breaking down data silos to leverage linked or consortium analysis strengthens both accuracy and explainability in AI for credit unions. For example, by aggregating intelligence across more than 4,000 credit unions, Velera enables institutions to combine historical patterns with real-time signals, making AI decisions more transparent, defensible and auditable. Its recent launches, including its Risk Mitigation Ecosystem and real-time account validation, demonstrate how integrated data and governance allow AI to scale in ways that are explainable and aligned with member trust.

Integration challenges keep many AI initiatives in pilot mode.

Even when governance and data foundations improve, execution barriers persist. CULytics research shows that 83% of credit unions cite integration with existing systems as a major obstacle to AI adoption, while one-third point to limited internal expertise. These constraints underscore the importance of phased deployment, shared service models and partner ecosystems that provide integrated data, governance frameworks and operational expertise out of the box.

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From Pilot to Practice: Making AI Work for Credit Unions

CUs face a pivotal moment in AI’s evolution across financial services. As AI moves into the mainstream of banking, meeting rising member demand for these tools is not merely an imperative but also an opportunity to extend credit unions’ service and advisory missions in new ways—grounded in trust, education and human judgment.

PYMNTS Intelligence recommends the following actionable roadmap for credit unions seeking to move AI from potential to practice:

• 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.

As members increasingly express confidence in credit unions to lead responsibly, this is an AI moment CUs cannot afford to let pass. By acting deliberately now, they can differentiate themselves while advancing their cooperative mission—delivering personalized, trustworthy AI-powered guidance and assistance in an increasingly digital financial landscape.

AI isn’t about replacing the human touch—it’s about amplifying it. For credit unions, the real opportunity lies in using AI to remove friction from everyday processes while deepening personalization and trust. When we pair automation with human expertise, we free teams to focus on what matters most: member engagement and innovation. The key is starting with high-impact, low-risk use cases and building transparency into every step. Done right, AI becomes not just a tool but a strategic advantage that strengthens the cooperative mission in a digital-first world.”

Jason Swan

Vice President, Advanced Analytics, Velera

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