Original research · 2026-07 edition

AI SEO Statistics: Community Banks (2026-07 edition)

15 questions · 45 AI responses · 3 models · measured 2026-07-06

See how AI talks about YOUR businessFull data table

The question bank

The questions we tested — sampled from real buyer journeys in community banks.

Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.

What are the actual benefits of switching from a big national bank to a local community bank for my personal checking?
Is it easier to get a small business startup loan approved at a community bank if I have a solid local reputation but limited collateral?
How do I verify that a small community bank is FDIC insured and financially stable before I deposit my house down payment?
Do community banks typically offer better interest rates on CDs and savings accounts compared to the major big-box banks?
I travel for work often; if my bank only has local branches, will I get hit with massive out-of-network ATM fees?
Are local banks usually more flexible with credit score requirements for a first-time homebuyer mortgage?
What are some red flags I should look for when reviewing a community bank's fee schedule for business accounts?
Can I expect a community bank to have a modern mobile app with mobile check deposit, or is their tech usually behind the times?
Show all 15 questions
If I need a quick turnaround on a commercial real estate loan, are community banks faster because they make decisions locally?
How does the customer service at a community bank compare to a credit union when it comes to resolving fraudulent charges?
I want my money to stay in my city; how can I tell if a bank actually reinvests in local small businesses and non-profits?
Is it worth the effort to move all my direct deposits and automatic bill pays just to get a more personal banking experience?
Do community banks offer specialized accounts for non-profit organizations that might have lower balance requirements?
What happens to my accounts if my local community bank gets bought out by a much larger financial institution?
Can I get a person on the phone at a community bank without going through a long automated menu or a foreign call center?

Model by model

24-point average divergence: which AI you ask changes the answer.

The divergence index is the average gap between the most and least likely model per behavior. Higher = the models disagree more about community banks buyers.

Behavior rates across 15 community banks buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional20%7%0%80%
Suggests DIY first40%27%20%60%
Names specific providers7%20%33%53%
Gives price or cost info0%7%20%73%
Tells to check reviews13%27%7%73%
Tells to verify credentials7%20%13%87%
Mentions case studies / portfolio7%0%0%93%
Mentions local proximity53%73%53%60%
Gives selection criteria60%73%20%33%
Warns about red flags20%20%7%80%
Asks a clarifying question40%67%0%27%
Recommends multiple quotes33%33%0%53%

By model

How each assistant handled Community Banks questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same community banks questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 20% (ChatGPT) down to 0% (Gemini), a 20-point gap on an identical question set.

Across the 15 community banks answers it produced, ChatGPT recommended hiring a professional in 20% of them and suggested a DIY approach first 40% of the time. It named a specific provider in 6.7% of answers (about 0.3 distinct providers per answer) and included price or cost information 0% of the time. ChatGPT asked a clarifying question before answering in 40% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 6.7%, averaging 567 words per answer. On the remaining cues it told the buyer to check reviews in 13.3%, pointed to case studies or a portfolio in 6.7%, and framed the choice around local proximity in 53.3%; a selection-criteria checklist appeared in 60% of its answers and a recommendation to gather multiple quotes in 33.3%.

Across the 15 community banks answers it produced, Claude recommended hiring a professional in 6.7% of them and suggested a DIY approach first 26.7% of the time. It named a specific provider in 20% of answers (about 1.1 distinct providers per answer) and included price or cost information 6.7% of the time. Claude asked a clarifying question before answering in 66.7% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 20%, averaging 317 words per answer. On the remaining cues it told the buyer to check reviews in 26.7%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 73.3%; a selection-criteria checklist appeared in 73.3% of its answers and a recommendation to gather multiple quotes in 33.3%.

Across the 15 community banks answers it produced, Gemini recommended hiring a professional in 0% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 33.3% of answers (about 1.3 distinct providers per answer) and included price or cost information 20% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 13.3%, averaging 266 words per answer. On the remaining cues it told the buyer to check reviews in 6.7%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 53.3%; a selection-criteria checklist appeared in 20% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a community banks buyer to a professional (20%) and Gemini the least (0%). ChatGPT produced the longest answers, at 567 words on average. Specific providers were named most often by Gemini (33.3%) — even there, roughly one answer in 3 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 23.7 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a community banks buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 66.7% (Claude) — a 67-point spread.
  • Gives selection criteria: from 20% (Gemini) to 73.3% (Claude) — a 53-point spread.
  • Recommends multiple quotes: from 0% (Gemini) to 33.3% (ChatGPT) — a 33-point spread.
  • Names a specific provider: from 6.7% (ChatGPT) to 33.3% (Gemini) — a 27-point spread.
  • Recommends hiring a professional: from 0% (Gemini) to 20% (ChatGPT) — a 20-point spread.

The widest single gap — asks a clarifying question, 67 points — means a community banks buyer can receive materially different guidance on the same question depending only on which assistant they happen to open, so any visibility strategy built on a single model's behavior describes only part of the community banks market.

Where they agree

The points of near-consensus in Community Banks.

On other behaviors the three models move almost in lockstep — the points of near-consensus for community banks, where all three landed within a few points of each other:

  • Mentions case studies or portfolio: 0%–6.7% across all three (a 7-point spread).
  • Tells the buyer to verify credentials: 6.7%–20% across all three (a 13-point spread).
  • Warns about red flags or scams: 6.7%–20% across all three (a 13-point spread).
  • Recommends hiring a professional: 0%–20% across all three (a 20-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "mentions case studies or portfolio" (identical coding in 93.3% of questions) and least consistently on "asks a clarifying question" (26.7%).

Every behavior, measured

All twelve coded behaviors for Community Banks, averaged across the three models.

The behaviors AI models reproduce most often for community banks are mentions local proximity (60% on average), gives selection criteria (51.1%) and asks a clarifying question (35.6%); the rarest are mentions case studies or portfolio (2.2%), gives price or cost information (8.9%) and recommends hiring a professional (8.9%). Each figure below is the share of a model's 15 answers in which the behavior appeared at least once, averaged across the 3 models with the full per-model range in parentheses:

  • Mentions local proximity: 60% on average (ChatGPT 53.3%, Claude 73.3%, Gemini 53.3%) — a 20-point spread.
  • Gives selection criteria: 51.1% on average (ChatGPT 60%, Claude 73.3%, Gemini 20%) — a 53-point spread.
  • Asks a clarifying question: 35.6% on average (ChatGPT 40%, Claude 66.7%, Gemini 0%) — a 67-point spread.
  • Suggests a DIY approach first: 28.9% on average (ChatGPT 40%, Claude 26.7%, Gemini 20%) — a 20-point spread.
  • Recommends multiple quotes: 22.2% on average (ChatGPT 33.3%, Claude 33.3%, Gemini 0%) — a 33-point spread.
  • Names a specific provider: 20% on average (ChatGPT 6.7%, Claude 20%, Gemini 33.3%) — a 27-point spread.
  • Tells the buyer to check reviews: 15.6% on average (ChatGPT 13.3%, Claude 26.7%, Gemini 6.7%) — a 20-point spread.
  • Warns about red flags or scams: 15.6% on average (ChatGPT 20%, Claude 20%, Gemini 6.7%) — a 13-point spread.
  • Tells the buyer to verify credentials: 13.3% on average (ChatGPT 6.7%, Claude 20%, Gemini 13.3%) — a 13-point spread.
  • Recommends hiring a professional: 8.9% on average (ChatGPT 20%, Claude 6.7%, Gemini 0%) — a 20-point spread.
  • Gives price or cost information: 8.9% on average (ChatGPT 0%, Claude 6.7%, Gemini 20%) — a 20-point spread.
  • Mentions case studies or portfolio: 2.2% on average (ChatGPT 6.7%, Claude 0%, Gemini 0%) — a 7-point spread.

Trust signals

How well the models protect the community banks buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the community banks buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 15.6% of answers on average. Verifying credentials or certifications appeared in 13.3%. Warning about red flags or scams appeared in 15.6%.

On structuring the decision, a selection-criteria checklist showed up in 51.1% of answers on average and a recommendation to gather multiple quotes in 22.2%. The single least-reproduced protective signal for community banks is "tells the buyer to verify credentials" at 13.3% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.

Referral behavior

Do AI models name Community Banks providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 community banks answers, a specific provider was named in 20% of responses on average — roughly 0.9 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for community banks: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Community Banks questions cover.

The 15 questions behind every percentage on this page were drawn from real community banks (financial services; buyer hiring decisions for this specific service) buyer journeys. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact community banks question set — not a general prior or a hand-picked subset. The full list is shown earlier on this page; the coded percentages are what those specific questions produced.

How to read this

A note on the numbers.

A percentage here is the share of a model's 15 answers in which the behavior appeared at least once — not a confidence score. Because each model answered every question exactly once on 2026-07-06, the figures describe this specific community banks question set and snapshot rather than a general prior. The full protocol and coding rubric are documented in the study methodology.

Methodology

A controlled snapshot, documented end to end.

15 standardized buyer questions per industry, one response per model per question (ChatGPT (gpt-5-mini), Claude (claude-sonnet-5), Gemini (gemini-3-flash-preview)), collected 2026-07-06, coded against a fixed 12-behavior rubric with human QA. AI outputs vary with model version, location and time — figures describe this sample and window, and are refreshed each edition. Read the full methodology →