How AI Is Reshaping Credit Union Operations

​Credit unions move rapidly from experimentation to operational deployment. That’s while navigating a careful balance between innovation, governance, and member trust.

By Marc Rapport, Contributing Editor

​Key Points

• Many credit unions adopt AI first in internal operations and lower-risk workflows.
• Governance and data controls remain central concerns for financial institutions.
• AI is improving employee productivity and automating repetitive tasks.
• Member-facing AI is emerging gradually, with institutions prioritizing trust and accuracy.
• The long-term opportunity lies in blending operational efficiency with stronger member experiences.

Artificial intelligence has quickly shifted from an emerging technology to a practical operational tool inside many credit unions. For them, what began as experimentation is now becoming part of everyday workflows.

Matt Phipps

Matt Phipps, chief marketing officer at Agent IQ, says the industry is also facing a reality that many leadership teams may not yet fully see: employees are already using AI tools.

“Employees increasingly expect modern tools that help them work faster and more effectively,” says Phipps, whose San Francisco-based company offers an AI-powered customer engagement platform to the financial services industry. “When those tools are prohibited without compliant alternatives, it negatively impacts productivity and job satisfaction.”

In fact, employees are often adopting AI faster than leadership realizes, Phipps adds, citing a McKinsey report that says “three times more employees are using gen AI for a third or more of their work than their leaders imagine.”

In other words, many workers are using tools like ChatGPT without formally telling their organizations. That can create a governance blind spot for highly regulated institutions like credit unions.

Create the bandwidth instead of a ban
Rather than banning AI outright, many institutions are beginning to bring AI usage into the open so they can monitor, guide, and control how it’s used.

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Joey Rudisill

At Central Willamette Credit Union,  Joey Rudisill, chief information officer at the $609 million Oregon shop, says the release of generative AI tools was a wake-up call.

“I realized that if we did not figure out how to use this technology effectively, then from a business operations perspective, very little else we were doing would matter as much in the long run,” Rudisill says.

He says CWCU is now applying AI primarily in internal productivity, communications, and knowledge work. “We’re also using it to automate selected internal workflows and to support small, targeted point solutions where applying intelligence makes specific tasks more efficient,” says Rudisill, who’s been CIO there since 2018.

Starting with practical, lower-risk use cases
Many other credit unions are not rushing headfirst into member-facing deployments, either. Instead, some say, they’re starting where the risk is manageable and the operational payoff is clear.

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Francis Boateng

Francis Boateng, SVP of information technology at Texas Trust Credit Union, say their early AI strategy focused on solving operational capacity challenges for the $2 billion Arlington-based cooperative.

“Our initial push toward AI was driven by capacity and efficiency challenges,” Boateng says. “Member expectations were rising, contact volumes were increasing, and our teams were spending too much time on repetitive, low-value tasks.”

That led the organization to prioritize use cases that could improve responsiveness while reducing routine workloads. Early deployments focused on automating repetitive questions, routing requests, and improving digital interactions.

And now fraud monitoring, anti-money laundering detection, and loan processing automation are emerging as must-have AI applications across credit union land.

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Todd Link

Todd Link of $3.6 billion Dupaco Community Credit Union in Dubuque, IA, says many credit unions are adopting AI first through existing vendors and platform capabilities.

“Like most credit unions in the U.S., Dupaco is using AI in ways similar to many of our peers,” says Link, who became chief member services officer in February 2025 after a decade as its chief risk officer. “We’re leveraging AI in the most common and prevalent use cases: fraud monitoring and detection, AML, reporting, loan processing, AI-driven phone transaction automation and routing.”

For many institutions, these early projects represent the safest way to gain experience with AI while delivering measurable operational improvements. Trusted partners help, too.

“What’s really changed in the past six to 12 months is the number of vendors selling products and services with AI components already built into their platforms,” Link says. “These platforms now enable meaningful efficiency gains by automating tasks that traditionally required human hands. This has led to stronger AI adoption across many credit unions.”

Governance, compliance, and data challenges
As promising as AI may be, credit unions remain largely cautious. Data privacy, regulatory compliance, and model accuracy are likely at the center of nearly every AI discussion.

Boateng says those concerns shaped his organization’s implementation approach from the beginning.

“Our biggest concerns were data privacy, regulatory compliance, model accuracy, and reputational risk,” the Texas Trust executive says. “To address these issues, we implemented strict data access and masking controls.”

Governance frameworks are frequently becoming just as important as the technology itself. Many credit unions are involving compliance, IT, and risk teams early in AI planning to ensure new tools align with regulatory expectations.

Data quality is another challenge.

Rudisill at CWCU says one early lesson was that generative AI systems do not always work well with traditional structured data formats.

“These models perform better when information is presented in a narrative, contextual way,” Rudisill says. “That forced us to rethink how we prepare and present data.”

That includes cleaning and organizing internal knowledge sources as an essential first step for many credit unions before deploying AI tools more broadly.

Changing how employees work
Even when members do not directly see AI, the technology is already reshaping how credit union teams operate internally.
Rudisill says AI is significantly improving productivity in knowledge-heavy tasks.

“Tasks that previously took a significant amount of time, such as gathering information, drafting documents, or preparing internal materials, can often be completed more quickly,” he says.

This shift is making employees more capable and allowing them to focus more on higher-value activities.

Many leaders believe this internal productivity boost will ultimately have a greater impact than any single AI application.

Link at Dupaco echoes that view, noting that automation can help staff spend more time serving members rather than performing manual tasks.

“I’m hopeful we can automate mundane back-office tasks so our service associates can spend more time interacting with members and helping them improve their financial lives,” he says.

That balance between automation and human interaction is becoming a central theme of credit union AI strategies.

The future of AI-powered member experience
While operational improvements are happening quickly, member-facing AI is advancing more cautiously.

Many credit unions want to ensure that new technologies improve experiences rather than create confusion or erode trust.

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Tim Pranger

Tim Pranger, founder and CEO of Appli, believes member-facing AI is where the biggest visible impact will ultimately occur. He’s an industry veteran who now heads up a 2024 startup that provides AI-powered, interactive financial calculators for credit unions to boost member engagement, personalized loan options, and conversion rates. 

“The fastest wins come from member-facing AI that modernizes service and removes friction,” Pranger says, pointing to tools such as chatbots, voice AI, and personalized digital experiences.

Intent matters, and that’s distinctly human
But Pranger also stresses that the intent behind these deployments matters.

“When AI is deployed to genuinely improve the experience – with clear paths to a human when needed – the response is overwhelmingly positive,” the longtime innovator says.

For many credit unions, the path forward involves careful experimentation, strong governance, and incremental progress.
AI is unlikely to replace the human relationships that define the credit union model. But when implemented thoughtfully, it may strengthen those relationships by making service faster, more personalized, and more accessible.

And for leaders across the industry, the question is no longer whether AI will play a role in operations.

It already is.

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