Artificial intelligence is transforming how credit unions and community banks work with their data, but there's a critical truth many organizations overlook: AI is only as reliable as the data behind it. Before your institution can trust AI-generated insights, your data needs to be clean, consistent, and well-governed.
Join us for a practical look at what it takes to prepare your organization's data for AI, and why "garbage in, garbage out" is more relevant now than ever.
In this webinar, we'll cover:
- Why data quality determines AI reliability, and what happens when AI models are built on incomplete or inconsistent data
- What a semantic data model is and why it matters. Learn how standardizing terminology across systems (think "acct_holder," "customer," and "borrower" all becoming "Member") creates a shared language your whole organization can use
- How disparate data from core banking systems, CRMs, and loan platforms can be unified into a single source of truth
- What separates a "compelling" AI output from a trustworthy one, and how governance and data structure play into that difference
- Real-world questions to ask when evaluating whether your institution's data is ready to support AI tools
We'll use practical examples throughout, including a look at how Jeanne D’Arc Credit Union is using a governed semantic layer to enable a natural-language AI tool to accurately interpret quick metric business questions such as: “How many members do we have currently, and how does member growth compare to last year?” and get instant, trusted answers.