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Metro Credit Union's Path to Core Independence and AI Readiness

Metro Credit Union reduced reliance on the core, improved decision making, and built an AI-ready data foundation with Snowflake. You can too.

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Metro Credit Union’s Business Challenge

In 2024, Metro Credit Union was operating with a complex web of third-party data extracts that introduced multiple single points of failure. Daily, weekly, and monthly extract cycles made data movement difficult to sustain and nearly impossible to scale. As data passed through multiple systems, integrity issues began to surface in critical finance processes, including reconciliation and reporting.

At the core of the problem was a data strategy tightly coupled to the core system. While the core handled transactions well, it was not designed to support modern analytics, flexible reporting, or emerging AI use cases. This made it harder for Metro to innovate and harder for teams to execute reliably with confidence in the numbers.

Rather than replacing systems or attempting a broad transformation all at once, Metro took a deliberate approach. Working with Passerelle, leadership focused on decoupling analytics and decision support from the core and sequencing modernization around business value. The goal was simple: establish trusted data, reduce operational friction, and create a foundation that could support future growth without adding risk.

Metro Credit Union’s Priorities

Metro Credit Union aligned its modernization effort around clear business outcomes:

  • Reduced dependence on the core system for analytics and reporting
  • Faster, more consistent decision making across the organization
  • Less manual effort required to produce trusted data
  • Stronger governance to support growth and regulatory expectations
  • A practical foundation for future AI initiatives

These outcomes guided both the architecture and the sequence of work.

Snowflake gives us one centralized, trusted source for information. We can strategically bring applications to connect to trusted data, versus continuing the process of pulling data out, massaging it, and sending it off to third parties."

Traci Michel, Chief Strategy Office and COO at Metro Credit Union

Modernization Roadmap – The First Year

Rather than pursuing a broad transformation, Metro focused on sequencing work around a business use case that mattered most. Working with Passerelle, Metro identified an initial use case where improved data consistency and faster decision-making would have immediate impact. This approach allowed the team to demonstrate value quickly while maintaining operational stability. That focus helped Metro:

  • Build confidence in shared data early by resolving long-standing reconciliation issues and establishing a single, trusted view of core metrics
  • Reduce manual processes without disrupting operations, eliminating recurring extracts, handoffs, and rework that slowed teams down
  • Align business and technology teams around the same goals, shifting conversations from data accuracy to business outcomes
  • Establish repeatable patterns that could be reused as the data estate expanded to new teams and use cases

Snowflake provided the scalability and governance needed to support this approach, giving Metro a centralized data foundation while still allowing flexibility in how data is activated across tools and teams.

 

Case Study Metro CU

With that foundation in place, Metro expanded into a high-impact marketing use case. By centralizing deposit and member data in Snowflake, the marketing team can define audiences once and use them consistently across campaigns and channels with Snowflake applications brought directly to the data. By removing manual audience creation and shortening campaign timelines, Metro Credit Union could provide more personalized outreach, while measuring performance and attribution with confidence.

From Trusted Data to AI in Practice

Today, Metro is using this trusted data foundation to operationalize how AI can support faster insight, better prioritization, and more informed decision-making without adding complexity or risk. At the center of this evolution is the Data Rocket Banking Assistant, which gives decision-makers conversational access to insights across operations, marketing, and performance. Built on governed, reliable data, the Banking Assistant enables leaders to explore questions in real time, understand context, and move from Data to Action without waiting on reports or analyst queues.

Read the Full Case Study Here

Your Data Can Be A Strategic Asset

Metro Credit Union has shown that credit unions do not need to accept the limits of bank in a box platforms and brittle hub and spoke architectures. As new use cases emerged, Metro chose to reduce reliance on the core, simplify its data environment, and stop adding point solutions that increased complexity without improving outcomes. The result was more consistent data, faster decision making, and lower operational risk as the organization continued to grow.

You can take the same approach. Metro Credit Union’s journey shows how to modernize in a controlled way, starting with business priorities rather than technology replacement. By building a flexible data foundation outside the core, credit unions can support new use cases as they arise, improve how teams use data, and create a practical path to AI readiness without repeated rework. Are you ready to get started? Book a complimentary consultation today.

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