Artificial Intelligence is transforming the credit union landscape, but to truly unlock its potential, we must teach AI to “speak CU” – to understand the unique language, data and business priorities of credit unions. Here’s how organizations can set the stage for agentic, business-focused AI that advances the CU movement.
1. Standard Ingestion: Laying the Foundation
Credit unions need to leverage vendors for many of their business processes. Therefore, they are forced to work with over seven decades of different database technologies. From Oracle databases to PDF files, from SQL Server to greenbar reports, credit unions struggle to unlock their data from archaic storage systems. Standardizing how data is ingested is essential as source systems are not designed for analytics and often do not store historical data in a standard way.
Leveraging data warehouse technology ensures that data is ingested in a standard way across all sources feeding it. A common approach to ingesting and storing data is critical for AI success. When data from core systems, digital channels, loan origination and member interactions are standardized, it becomes easier to aggregate, analyze and leverage. This consistency ensures that AI models are trained on reliable, high-quality data, reducing errors and accelerating insights. Standard ingestion is the bedrock for scalable, trustworthy AI in CUs.
2. Semantic Layer: The Single Source of Truth
Once your data is being ingested in a standardized way, the data must be combined to ensure a 360-degree picture about your credit union (and its members) is established. Combining the data into a single source of truth establishes a foundation for end users leveraging AI to get accurate and relevant output. A semantic layer provides a unified view of data definitions, relationships and business logic. By establishing a single source of truth, credit unions ensure that all stakeholders – and AI systems – interpret data the same way. This clarity eliminates ambiguity, streamlines analytics and empowers AI to deliver accurate, context-aware recommendations tailored to CU needs.
With AI, as well as in human communications, it is never “just semantics.” Data must always be understood within the context of the business (for the audience the communication is being created for) and especially the business use cases that AI will be trained for.
3. MCP Server: Empowering Agentic AI for CUs
Even with a well-established semantic layer, AI must learn via context. For example, just teaching a baby words like “apple, orange, car, ball” is only the beginning. You must teach it how all these words interact within context (i.e., the apples and oranges spill over inside the car if it has to swerve to avoid a ball in the road). Context is the glue between AI and how end users work with it to bring greater success to the credit union.
An MCP (Model Context Protocol) Server acts as the orchestrator for agentic AI, enabling intelligent agents to interact with CU data, processes and workflows. By centralizing access and control, the MCP Server empowers AI to automate tasks, personalize member experiences and drive operational efficiency – all while maintaining compliance and security. The great news is that MCP Servers are now easy to interact with via an LLM (such as Co-Pilot or Claude).
4. Business-Focused AI Use Cases: Advancing the CU Movement
AI must be trained on CU-specific use cases – like member onboarding, loan origination, fraud detection and personalized financial guidance. By focusing on business outcomes, credit unions ensure that AI solutions are relevant, impactful and aligned with their mission to serve members and communities.
With all the AI buzzwords swirling around, the most important thing we need to focus on is teaching AI the way of the CU. Every CU has a different culture and context for how they started, what members they serve and their mission for the credit union movement.
Teaching AI to speak CU requires a holistic approach: standardizing data ingestion, building a semantic layer, leveraging the MCP Server and prioritizing business-focused use cases. Together, these pillars enable credit unions to harness AI’s power and advance the movement for member-centric financial services.
For practical ways to leverage AI in your credit union, please watch the on-demand webinar that Rise Analytics recently conducted: Making AI Work: The Platform Behind Practical AI.
