AI SEO Statistics: Bank (2026-07 edition)
15 questions · 45/45 expected AI responses · 3 models · measured 2026-07-04
The question bank
The questions we tested — a frozen buyer-intent benchmark for bank.
The question set was curated from a predefined buyer-intent taxonomy and held constant for this edition. Each model received the same wording. These are the prompts behind every percentage on this page.
Show all 15 questions
Model by model
23% question-level model disagreement.
This rate is the average pairwise disagreement between binary behavior codes across questions and behaviors. It is not the gap between the highest and lowest aggregated model percentages.
Behavior prevalence across 15 bank benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 6.7% | 6.7% | 20% | 11.1% |
| Suggests DIY first | 40% | 13.3% | 6.7% | 20% |
| Names specific providers | 26.7% | 40% | 60% | 42.2% |
| Gives price or cost info | 6.7% | 13.3% | 66.7% | 28.9% |
| Tells to check reviews | 13.3% | 6.7% | 0% | 6.7% |
| Tells to verify credentials | 20% | 13.3% | 6.7% | 13.3% |
| Mentions case studies / portfolio | 0% | 0% | 0% | 0% |
| Mentions local proximity | 33.3% | 13.3% | 20% | 22.2% |
| Gives selection criteria | 53.3% | 60% | 60% | 57.8% |
| Warns about red flags | 13.3% | 13.3% | 13.3% | 13.3% |
| Asks a clarifying question | 53.3% | 66.7% | 0% | 40% |
| Recommends multiple quotes | 13.3% | 13.3% | 0% | 8.9% |
Question-level agreement
How often all measured models received the same binary code.
Agreement is calculated question by question for each behavior. A high value can coexist with a low behavior prevalence; it means the models usually agreed on whether the behavior appeared.
All-model binary agreement by behavior across 15 benchmark questions.
| Behavior | All-model agreement |
|---|---|
| Recommends hiring a professional | 73.3% |
| Suggests DIY first | 53.3% |
| Names specific providers | 33.3% |
| Gives price or cost info | 40% |
| Tells to check reviews | 86.7% |
| Tells to verify credentials | 86.7% |
| Mentions case studies / portfolio | 100% |
| Mentions local proximity | 80% |
| Gives selection criteria | 60% |
| Warns about red flags | 80% |
| Asks a clarifying question | 20% |
| Recommends multiple quotes | 73.3% |
By model
How each assistant handled Bank questions.
Reading the 45 answers model by model shows how differently the three assistants treat the same bank questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 20% (Gemini) down to 6.7% (ChatGPT), a 13-point gap on an identical question set.
Across the 15 bank answers it produced, ChatGPT recommended hiring a professional in 6.7% of them and suggested a DIY approach first 40% of the time. It named a specific provider in 26.7% of answers (about 0.6 distinct providers per answer) and included price or cost information 6.7% of the time. ChatGPT asked a clarifying question before answering in 53.3% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 20%, averaging 604 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 0%, and framed the choice around local proximity in 33.3%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 13.3%.
Across the 15 bank answers it produced, Claude recommended hiring a professional in 6.7% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 40% of answers (about 1.5 distinct providers per answer) and included price or cost information 13.3% of the time. Claude asked a clarifying question before answering in 66.7% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 13.3%, averaging 300 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 13.3%; a selection-criteria checklist appeared in 60% of its answers and a recommendation to gather multiple quotes in 13.3%.
Across the 15 bank answers it produced, Gemini recommended hiring a professional in 20% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 60% of answers (about 2.4 distinct providers per answer) and included price or cost information 66.7% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 6.7%, averaging 258 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 60% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, Gemini is the assistant most likely to route a bank buyer to a professional (20%) and ChatGPT the least (6.7%). ChatGPT produced the longest answers, at 604 words on average. Specific providers were named most often by Gemini (60%) — even there, roughly one answer in 2 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
Question-level model disagreement is 23% — the average pairwise rate at which models received different binary codes across questions and behaviors. The observed rate spreads below are a separate measure showing where which assistant a bank buyer happens to ask matters most:
- Asks a clarifying question: from 0% (Gemini) to 66.7% (Claude) — a 67-point spread.
- Gives price or cost information: from 6.7% (ChatGPT) to 66.7% (Gemini) — a 60-point spread.
- Suggests a DIY approach first: from 6.7% (Gemini) to 40% (ChatGPT) — a 33-point spread.
- Names a specific provider: from 26.7% (ChatGPT) to 60% (Gemini) — a 33-point spread.
- Mentions local proximity: from 13.3% (Claude) to 33.3% (ChatGPT) — a 20-point spread.
The widest single gap — asks a clarifying question, 67 points — means a bank 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 bank market.
Where they agree
The points of near-consensus in Bank.
On other behaviors the three models move almost in lockstep — the points of near-consensus for bank, where all three landed within a few points of each other:
- Mentions case studies or portfolio: 0% across all three models.
- Warns about red flags or scams: 13.3% across all three models.
- Gives selection criteria: 53.3%–60% across all three (a 7-point spread).
- Recommends hiring a professional: 6.7%–20% across all three (a 13-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 100% of questions) and least consistently on "asks a clarifying question" (20%).
Every behavior, measured
All twelve coded behaviors for Bank, averaged across the three models.
The behaviors AI models reproduce most often for bank are gives selection criteria (57.8% on average), names a specific provider (42.2%) and asks a clarifying question (40%); the rarest are mentions case studies or portfolio (0%), tells the buyer to check reviews (6.7%) and recommends multiple quotes (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:
- Gives selection criteria: 57.8% on average (ChatGPT 53.3%, Claude 60%, Gemini 60%) — a 7-point spread.
- Names a specific provider: 42.2% on average (ChatGPT 26.7%, Claude 40%, Gemini 60%) — a 33-point spread.
- Asks a clarifying question: 40% on average (ChatGPT 53.3%, Claude 66.7%, Gemini 0%) — a 67-point spread.
- Gives price or cost information: 28.9% on average (ChatGPT 6.7%, Claude 13.3%, Gemini 66.7%) — a 60-point spread.
- Mentions local proximity: 22.2% on average (ChatGPT 33.3%, Claude 13.3%, Gemini 20%) — a 20-point spread.
- Suggests a DIY approach first: 20% on average (ChatGPT 40%, Claude 13.3%, Gemini 6.7%) — a 33-point spread.
- Tells the buyer to verify credentials: 13.3% on average (ChatGPT 20%, Claude 13.3%, Gemini 6.7%) — a 13-point spread.
- Warns about red flags or scams: 13.3% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 13.3%).
- Recommends hiring a professional: 11.1% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 20%) — a 13-point spread.
- Recommends multiple quotes: 8.9% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 0%) — a 13-point spread.
- Tells the buyer to check reviews: 6.7% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 0%) — a 13-point spread.
- Mentions case studies or portfolio: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).
Trust signals
How well the models protect the bank buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the bank buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 6.7% of answers on average. Verifying credentials or certifications appeared in 13.3%. Warning about red flags or scams appeared in 13.3%.
On structuring the decision, a selection-criteria checklist showed up in 57.8% of answers on average and a recommendation to gather multiple quotes in 8.9%. The single least-reproduced protective signal for bank is "tells the buyer to check reviews" at 6.7% 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 Bank providers?
For service providers the decisive question is whether these systems name anyone at all. Across 45 bank answers, a specific provider was named in 42.2% of responses on average — roughly 1.5 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for bank: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 15 Bank questions cover.
The 15 questions behind every percentage on this page form a frozen bank (financial services; buyer hiring decisions for this specific service) buyer-intent benchmark. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact bank 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 2026-07-04, the figures describe this specific bank 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 frozen benchmark questions, one expected response per model per question (ChatGPT API (gpt-5-mini), Claude API (claude-sonnet-5), Gemini API (gemini-3-flash-preview)), collected 2026-07-04 and coded against a fixed 12-behavior rubric. The pipeline validates the schema, recomputes aggregates and reports consistency issues. AI outputs vary with model version, location and time, so the figures describe this edition's exact sample and measurement window. Read the full methodology →
Citation
Cite this edition.
Authority Specialist. “AI SEO Statistics: Bank (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/financial/bank