The Challenge:
Ad Hoc Data Request Draining IT Time
For Credit Union 1’s data team, the daily reality looked familiar to most credit unions. Departments across the organization – marketing, lending, member services – needed answers about their members, and they needed them yesterday. The standard canned reports in Keystone covered the basics, but they rarely matched the exact criteria a team was looking for.
What followed was predictable. Tickets piled up. Analysts wrote long, custom SQL queries. Reports were rebuilt in the reporting layer. Every “quick question” turned into hours of skilled, expensive work and employees across the credit union waited in line for insight that should have been instant.
“We have people staffed to answer tickets and work through writing very lengthy SQL queries,” Derek Rudolph, IT Leader at Credit Union 1, explained. “It’s all very, very time consuming.”
The Solution:
Conversational Data Access with Finn
Finn, Rise Analytics’ generative AI solution, gave the CU1 team a fundamentally different way to interact with their data, one that mirrors the way people actually think and ask questions.
Three Real Use Cases
1. The Bad Address Report
A request landed on Derek’s desk: how many members have bad addresses on file, with a six-month trend chart? Historically, that meant building a new ad hoc report, scheduling it to run monthly, and tracking the data forward in Excel.
With Finn, Derek simply asked the question. Within seconds he had a clean six-month chart, with month-over-month percentage changes included, a level of analytical polish the requesting team hadn’t even thought to ask for. “It went beyond my expectations,” Derek said. “It probably saved my team hours.”
2. Modeling New Membership Tiers
CU1 was developing more advanced membership tiers tied to perks like funds availability. Building those tiers without knowing how many members would actually qualify is risky, so Derek fed the proposed criteria for a top-tier “platinum” level into Finn and asked it to size the population.
Finn surfaced the answer in minutes: only 8% of members would qualify under the proposed criteria – likely too stringent. That single insight reshaped the conversation about how to design the tier.
3. Historical "What If" Analysis
Derek’s next step was even bigger: applying historical data to test what new policies would have done. If new tiers had been in place a year ago, how many manually approved checks could have been auto-approved? How would member relationships have shifted?
Finn turned those questions from multi-week research projects into prompts that could be answered in a sitting, turning the data warehouse into a true proof-of-concept engine for the credit union.
What's Next AT CU1
Derek’s vision goes well beyond ad hoc reporting. He sees Finn replacing the work that goes into Power BI dashboards that get glanced at once a month, opening direct, self-serve access to data for marketing and other front-line teams, and powering a future where prompt libraries and automated runs deliver recurring insights without anyone rebuilding the same dashboard twice.
“We could just save so much time and energy,” Derek said. “Finn can help transform the function my department serves within the organization from report generators to a revenue engine by helping the organization identify real business opportunities mined from their member data. The future is kind of endless.” Derek Rudolph, IT Leader at Credit Union 1
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About Rise Analytics
Rise Analytics empowers credit unions with cutting-edge analytics, AI and machine learning to turn data into actionable strategies. With deep industry expertise and innovative data solutions, Rise Analytics helps credit unions make smarter decisions, drive meaningful outcomes and achieve long-term success. Their powerful combination of technology and advisory services ensures insight leads to impact. Learn more at RiseAnalytics.com