TAMPA—When credit union leaders talk about artificial intelligence, much of the conversation tends to focus on chatbots, fraud detection or automation. But Paolo Teotino believes the technology’s most important impact may be far less visible—and far more transformative.
The executive director of Rise Analytics argues that the real opportunity for credit unions is not simply using AI to answer member questions or automate routine tasks. Instead, it is using AI to eliminate one of the industry's longest-running frustrations: the time it takes to turn data into decisions.
“The successful credit union of the future won’t be the biggest one, or the one with the most branches, or even the one with the most history,” Teotino said. “It will be the one that figures out how to turn member relationships into data, and then into personalization at scale.”
That idea is the driving force behind Finn, an AI-powered analytics agent launched this year by Rise Analytics, a wholly owned subsidiary of Trellance. The platform allows users to ask questions about institutional performance in plain language and receive immediate answers based on their credit union’s data, eliminating many of the steps traditionally required to generate reports and insights.
A Persistent Problem
Credit unions have never lacked data. Core systems, digital banking platforms, lending systems, card programs and member service channels generate enormous volumes of information every day.
The challenge has always been accessibility, Teotino said.
For years, credit unions have relied on teams of analysts, business intelligence specialists and reporting tools to translate raw information into actionable insights. That process often requires extracting data from multiple systems, building custom reports, validating results and distributing information to decision-makers.
According to Teotino, every department tends to want something different.
A branch manager may need performance metrics for employees. A lending executive may want insight into portfolio growth trends. A marketing leader may need information on member engagement or product adoption. Meeting those needs often requires customized dashboards and reports built through platforms such as Power BI or Tableau.
“The knowledge that many of our customers had was that each credit union needs something different,” Teotino said. “Each credit union needs a different view of its data. Historically, that meant developing custom reporting, which was really time consuming and expensive.”
The result is that many institutions possess the information they need but struggle to access it quickly enough to make timely decisions.
Shrinking Weeks Into Seconds
What makes generative AI different, Teotino said, is that it changes how users interact with data.
Rather than opening multiple dashboards or requesting reports from analysts, users can simply ask a question.
A branch manager might ask how transaction volume has changed over the past month. A marketing executive might request a dashboard highlighting member engagement trends. A CEO might want to identify product categories driving the strongest growth.
Instead of waiting days for answers, they can receive them almost immediately.
“With these tools, you can have the information that you need, the insights that you need, in a matter of seconds,” Teotino said. “That’s transformative for credit unions.”
The company’s April announcement described Finn as a conversational interface layered on top of Rise Analytics’ existing data platform, predictive analytics models and benchmarking capabilities. The system can provide answers, generate charts and dashboards, create downloadable datasets and offer recommendations based on institutional data.
In essence, Teotino said, the technology allows users to bypass many of the technical barriers that traditionally separated employees from the information they needed.
Early Lessons From the Field
The most compelling evidence may be how credit unions are already using the tool.
One institution was developing a premium member rewards program with multiple qualification tiers. Executives had assumptions about how many members would qualify for the highest level, Teotino explained.
Using Finn, they tested the proposed criteria against actual member data.
The result surprised them.
Only about 8% of members met the requirements for the top tier, significantly fewer than expected. Armed with that information, the credit union adjusted the program before launch.
Traditionally, Teotino noted, that type of analysis could have taken days to complete.
Another credit union encountered a different challenge, he said. Leaders wanted a detailed breakdown of transactions occurring across branches, but the volume of information made generating traditional reports difficult.
Using the AI tool, managers were able to obtain the information they needed by branch, transaction type and transaction subtype, allowing them to analyze performance without waiting for customized reporting.
“These are things that historically would have required a lot more effort,” Teotino said.
The Retention Opportunity
Perhaps the most interesting application involves member retention.
Credit unions have long used predictive models to identify members who may be at risk of leaving. The challenge has often been turning those insights into action.
Teotino said Finn can do more than identify potentially disengaged members. It can explain why those members are at risk and recommend strategies for re-engagement. Marketing teams can then generate target lists and move directly into campaign execution.
The process compresses what has traditionally been a multi-step workflow involving analysts, marketers and data teams, he said.
“This is a good example of using data to generate significant business outcomes,” Teotino said.
Democratizing Data
Beyond any single use case, Teotino believes the larger significance lies in democratizing access to information.
Historically, analytics capabilities have often been concentrated within a relatively small group of specialists. AI has the potential to place those capabilities into the hands of employees throughout an organization.
That means branch managers, marketing teams, executives and operations leaders can all interact directly with data using language they already understand.
The goal, Teotino said, is not simply to provide answers faster. It is to create an environment where better decisions can be made throughout the organization because information is no longer trapped behind technical barriers.
The company officially launched Finn in March and has already signed multiple paying credit unions while several others are testing the platform. Teotino said institutions that begin using the tool often expand access across additional departments as they discover new applications.
For an industry built on relationships, the next competitive advantage may not come from collecting more data. Credit unions already have plenty of that. The differentiator could be how quickly they can transform information into insight—and insight into action, Teotino explained.
Teotino closed by saying that the credit unions that thrive in the years ahead may not be those with the largest balance sheets or the most branches. They may be the institutions that learn to have a conversation with their data—and get an answer before the opportunity disappears.