The AI Visibility Index: how financial institutions read when AI describes them.

What the benchmark shows.

As of September 2026, the benchmark spans nearly 600 AI visibility evaluations across more than 230 unique websites, the majority of them credit unions and banks, with a smaller set of reference brands for comparison. A few patterns hold across the whole set:

• Most institutions sit in the middle. Strong in some areas, exposed in others, with clear room to move.
• A meaningful share of sites are hard to read. An answer engine struggles to parse, trust, or cite them at all.
• Accessibility is the weakest area. The structure that assistants and screen readers both depend on is where institutions most often fall short.
• Search foundations and reputation signals trail the rest. Content structure and technical health tend to be stronger.

Where institutions stand, by dimension.

Every site is read across the same set of dimensions: content structure, search foundations, technical health, accessibility, and external reputation signals. Across the benchmark, technical health and content structure tend to be the strongest areas. Accessibility is consistently the weakest, with search foundations and reputation signals in between. The spread within any one institution is often wider than the spread between institutions, which is why a specific read beats a general grade.

The fixes that come up most.

The same handful of gaps appear again and again. In order of how often they are prescribed across evaluated sites:

1. Add structured data so engines can read what a page is about.
2. Fix technical performance issues such as image and script loading.
3. Add accessibility labels (ARIA) and real form labels.
4. Set a viewport meta tag for clean mobile rendering.
5. Set canonical URLs so pages do not compete with themselves.

None of these require rebuilding a website. They are structure and hygiene, which is why AI visibility moves faster than most teams expect once the work starts.

Methodology.

What we measured. How well a financial institution website can be read, trusted, and cited by AI answer engines, assessed across content structure, search foundations, technical health, accessibility, and external reputation signals.

Sample. As of September 2026, nearly 600 evaluations across more than 230 unique websites, the majority credit unions and banks, with a smaller set of reference brands for comparison. The benchmark grows as new institutions are evaluated, so the picture updates over time.

Measurement period. Rolling, with the current read stated as of September 2026.

Method update. As of October 2026, the Index reports the patterns that hold across the benchmark and the fixes that move the result. It no longer publishes a single aggregate grade. The underlying read still covers the same dimensions, and a free scan shows you exactly where your own institution stands today.

Limitations. The read reflects what is publicly visible at the time of each evaluation. Sites change, and a single evaluation is a snapshot. Reference brands are included for comparison and are not weighted as financial institutions. This page reports aggregate patterns only. No individual institution is identified.

Source: the Atlas Instinct AI Visibility benchmark. Findings are illustrative of the current dataset and are updated as the research grows.