Choosing the Right LLM for Your Credit Union: A No-Nonsense Guide for Ohio CU Leaders

Folks always ask, “How do I know which AI model’s right for our credit union?” And I get it. Between ChatGPT, Gemini, Claude, and that new one called Gronk (who sounds more like a fullback than a chatbot), it’s enough to make your head spin.

But here’s the truth: You don’t need to understand every line of code. You just need to know what you need and how to match that to what these Large Language Models (LLMs) actually do. Because AI isn’t about flash. It’s about fit.

So if you’re a credit union leader – and you’re juggling compliance, member service, and the occasional phishing scare – this guide’s for you.

What’s an LLM, Really?

Let’s start with the basics. A Large Language Model is a kind of artificial intelligence that can understand and generate human language. It can write emails, answer member questions, summarize board reports, and even spot patterns in your fraud logs.

But not all models are the same. Some are chatty. Some are smart researchers. Some are quiet, steady analysts that shine behind the scenes.

Your Credit Union’s Needs Come First

Before you even peek at the vendor list, ask yourself:

· Do we need better fraud detection?

· Is our call center overwhelmed?

· Are we spending hours rewriting policies or training manuals?

· Do our members need faster, smarter self-service tools?

Once you’ve nailed down your top priorities, you can match the right model to the job.

Let’s Talk Models (Plain English Edition)

ChatGPT (OpenAI)

Best for: General productivity, member chatbots, writing policies, summarizing meetings

This one’s your Swiss Army knife. ChatGPT is good at everything – but make sure you get the enterprise version if you want to keep member data private.

Microsoft Copilot

Best for: Teams that use Microsoft 365 (Word, Outlook, Excel, Teams)

It’s like hiring an intern who lives inside your apps. Writes memos, drafts emails, and even tidies up spreadsheets.

Google Gemini (formerly Bard)

Best for: Credit unions using Google Workspace

Fast, connected to live web results, and ideal for marketing or research. Less secure out of the box than Microsoft, though.

Anthropic Claude

Best for: Policy-heavy tasks, long documents, member compliance training

The “teacher’s pet” of the LLM world. Smart, careful, and ethical. Won’t do anything dumb – but might take its time.

Meta LLaMA

Best for: IT teams building in-house tools

Open source, which is code for “do it yourself.” Great if you’ve got devs on staff. Not so great if you’re flying solo.

Cohere

Best for: Searching internal documents, answering member service questions

Think of it like a librarian with a memory like a steel trap. Doesn’t “chat” much, but it’ll fetch the right answer every time.

Observe.ai

Best for: Call centers, contact compliance, coaching agents

If you’re drowning in call volume, Observe can flag angry callers, coach reps, and keep you on the right side of NCUA and FFIEC.

Gronk

Best for: Simpler tasks, first-time AI users, quick content

Gronk’s not a genius, but he shows up on time and doesn’t complain. Lightweight, easy to use, and surprisingly handy for social media or email drafts.

Don’t Forget the “CU Stuff”

Ohio credit unions aren’t your average business. You’ve got regulatory hoops, tight budgets, and members who still pay their loans at the teller line. So whatever model you choose, make sure it:

· Keeps data private. You’ve got to stay compliant with GLBA, NCUA, and PCI-DSS.

· Integrates easily. If it doesn’t play nice with your core or CRM, you’ll waste more time than you save.

· Talks to humans. Karen Meyers doesn’t want to be talked down to – she wants to understand what she’s buying.

Ask these questions when evaluating:

· Is our member data ever used to train this model?

· Does it integrate with Symitar, CU*Answers, or our core?

· Can my team use it without a PhD?

· Will we have a real person to call when things break?

Start Small, Win Big

Don’t try to AI-ify everything overnight. Pick one department, like marketing or member service, and test an LLM there. Watch how it performs, gather feedback, and then expand.

Just like you wouldn’t roll out a new core system without testing, treat AI with the same care. Crawl, walk, run.

What Ohio Credit Unions Are Doing

Plenty of Ohio CUs are already testing LLMs in small ways:

· A CU in Akron used ChatGPT to rewrite 40 outdated HR policies in a week.

· One down in Chillicothe used Observe.ai to coach their call center – and member satisfaction shot up 12 points.

· And I know a shop near Toledo using Copilot to summarize board packets. Now the CEO actually reads ’em.

You don’t need a million-dollar innovation lab. You just need a partner who knows your world and can help you take that first step.

Don’t Buy AI. Build Confidence.

AI shouldn’t scare you. It should support you. It’s not here to replace your team, it’s here to strengthen it.

The right model, used the right way, will help you:

· Serve members faster

· Keep data safe

· Give your overworked staff a break

· And maybe—just maybe—get home before dark for once

Want to Talk This Through?

If you want to know which LLM fits your CU—or you’re just tired of the noise and want real
answers—reach out. Contact Corporate Technologies Group today at info@ctgusa.net or call 330-655-8144.

We’ll walk beside you, not ahead of you. That’s how trust gets built. And in this business, trust is everything.