How Credit Unions Can Improve Their Visibility in AI LLMs

A recent study by Geear found that consumers under 45 use Large Language Models (LLMs) as the second-most-used channel when researching financial institutions, trailing only search engines. As Gemini and ChatGPT become daily research tools, credit union and bank marketers are naturally asking: “How do we ensure our brand appears in these results?”

Optimizing your brand’s presence in an LLM is known as Generative Engine Optimization (GEO) or Answer Engine Optimization (AEO).

While SEO has 25 years of established data, GEO is still in its infancy. Methods can seem "fuzzy," but our experiments at Geear are revealing what actually moves the needle for AI visibility. Here is how the strategy is evolving.

Defining “AI Visibility”

The difference between SEO and GEO begins with how we define success.

In traditional SEO, we track rankings and traffic. We use tools like Google Search Console and Semrush to find an average search position for a credit union or bank, then measure how many visitors clicked through to their website. Because we can track a direct path from a search to a website visit, SEO is considered a performance channel.

In GEO, visibility is different for three reasons:

1. Results are non-repeatable: LLM rankings are more like a raffle than a fixed list. If 1,000 people ask the same question, the recommendations may stay consistent, but the order will likely change every time.
2. Mentions are consistent: While the order varies, being mentioned is highly repeatable. If your brand appears in 900 out of 1,000 queries, you have high visibility, regardless of whether you were listed first or third.
3. Low Click-Through Rates: Users rarely click out of an LLM to a website. Geear’s own research shows that for every single visit from an LLM, there are approximately 1,000 visits from a search engine. This means LLMs should be thought of more as a branding channel versus a performance channel.
   

Tactics that Work for Both SEO & GEO

We’ve established that LLMs are a popular tool for researching a credit union or bank. We also know that despite this popularity, they don’t send a lot of traffic directly to your website, but that they’re becoming a meaningful “word of mouth” engine. So, how do you make sure your FI gets mentioned in an LLM’s results?

If you’ve been investing in producing high-quality content on your website and ensuring you have a reputable brand online, you're off to a good start. These are all positive signals to LLMs.

Additionally, investing in good technical SEO will always be in your and your user’s best interest. For example, using structured data (JSON-LD) will reduce “hallucinations” in LLMs. If your interest rates are in a standard HTML table, an AI might misread them. If they are in Schema.org markup, they become "facts."

That said, what ranks well in Google is in no way an indication of what will show in an LLM. This dispels the myth that good GEO is simply good SEO. But it does mean that good SEO is a strong foundation to build off of.

So what, specifically, is effective for appearing reliably in LLMs?

GEO Tactics Specific to Large Language Models

This is where we start to get more technical. If these are things you’re not comfortable doing yourself, your digital marketing firm or Geear can help.

1. Understand and write for query fan outs. When you run a prompt in an LLM like ChatGPT, ChatGPT then “fans out” your query into 10 other queries. This allows ChatGPT to expand the content you gave it so it can provide a very rich and “thought out” response. This means if you want to maximize visibility for one query, you really need to consider optimizing for several. For example, if someone asks, "Should I get a car loan from a credit union or a bank," the fan out queries might include:
1. "Credit union vs. national bank auto loan rates 2026"
2. "Member reviews of [Your CU] vs. [Big Bank] car loans"
3. "Credit union car loan hidden fees vs. dealer financing"
4. "Minimum credit score for credit union auto loans"
5. "Local credit unions in [Your City] with fastest loan approval"
   
   
2. Eliminating 499 statuses on your webpages. Since LLMs may access your website in real time versus pulling from an index (as search engines do), a 499 code basically says your site is taking too long to load and will no longer be considered in the LLM's response
   
   
3. Writing longer, more descriptive URLs. URL slugs act as a signal describing what the page is about. So, instead of .../product?id=123, use .../best-auto-loan-rates-in-denver-for-young-consumers
   
   
4. Writing strong meta descriptions. Search engines stopped relying on meta descriptions many, many years ago, but LLMs use them to determine if a page should be fetched and used in its answer.
   
   
5. Uploading content to YouTube. YouTube is the second most cited source in LLMs just behind Reddit. A robust YouTube channel showcasing your credit union’s community involvement, educational content, and product information can help boost visibility.
   
   
6. Implementing "Chunking" in your content. Some credit union webpage content can be dense or buried in "walls of text.” Instead, break your content into "atomic units." Use clear H2s/H3s to create "passages" that are self-sufficient. An LLM should be able to pull a 200-word "chunk" about "First-time homebuyer grants in [Your City]" without needing the context of the entire page. But be careful here, because you still want to write for humans first and foremost.
   
   
   

How Can Credit Unions and Banks Track Their Visibility in LLMs?

Tools like Gumshoe and Profound will ping various LLMs with pre-set prompts and track your brand’s visibility over time. While it won’t provide a comprehensive analysis of how much visibility you have, it will establish a decent baseline for understanding if your efforts are having an impact.

LLMs are becoming the new “word of mouth” for financial brands. If you want to increase how often your institution is recommended, Geear can help you build and execute a GEO strategy.

Talk with our team →