Original research · 2026-07 edition

AI SEO Statistics: Fintech (2026-07 edition)

15 questions · 45 AI responses · 3 models · measured 2026-07-04

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The question bank

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

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

I'm tired of manually tracking my business expenses in Excel, what's the best automated tool for a small agency owner?
Is it actually worth paying a 0.25% management fee for a robo-advisor versus just buying a total market ETF myself?
What are the biggest red flags I should look for when choosing a new digital-only bank for my primary checking?
I have $50k sitting in a low-interest savings account; what are the safest fintech platforms for high-yield cash management right now?
How can I tell if a financial app is truly FDIC insured or if they just have a partnership that might be risky?
I need to set up a business account for my LLC by tomorrow morning, which platforms have the fastest approval process?
What's the typical cost for a digital financial planning service that includes a human advisor for a yearly check-in?
Compare the pros and cons of using an AI-driven credit builder app versus getting a traditional secured credit card.
Show all 15 questions
Are there any fintech services specifically designed for freelancers to automate their quarterly tax withholdings?
I'm moving my family's investments to a new platform; what's the easiest way to do an ACATS transfer without selling my positions?
Which personal finance apps allow for a 'joint' view for couples without actually merging our individual legal accounts?
I'm an expat living in Spain but earning USD; what's the cheapest way to handle currency exchange and local bill pay?
If a fintech company goes bankrupt, what happens to the stocks and cash I have sitting in my brokerage account with them?
What features should I prioritize in a payroll provider if I plan on hiring international contractors next year?
I want to start micro-investing with my spare change, but I'm worried about high monthly subscription fees eating my returns.

Model by model

22-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 fintech buyers.

Behavior rates across 15 fintech buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional33%27%13%67%
Suggests DIY first47%33%7%47%
Names specific providers47%33%73%40%
Gives price or cost info40%40%40%73%
Tells to check reviews13%7%0%80%
Tells to verify credentials20%7%13%73%
Mentions case studies / portfolio0%0%0%100%
Mentions local proximity13%0%0%87%
Gives selection criteria53%53%27%33%
Warns about red flags20%7%13%73%
Asks a clarifying question47%47%0%40%
Recommends multiple quotes7%0%0%93%

By model

How each assistant handled Fintech questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same fintech questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 33.3% (ChatGPT) down to 13.3% (Gemini), a 20-point gap on an identical question set.

Across the 15 fintech answers it produced, ChatGPT recommended hiring a professional in 33.3% of them and suggested a DIY approach first 46.7% of the time. It named a specific provider in 46.7% of answers (about 2.1 distinct providers per answer) and included price or cost information 40% of the time. ChatGPT asked a clarifying question before answering in 46.7% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 20%, averaging 640 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 13.3%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 6.7%.

Across the 15 fintech answers it produced, Claude recommended hiring a professional in 26.7% of them and suggested a DIY approach first 33.3% of the time. It named a specific provider in 33.3% of answers (about 1.5 distinct providers per answer) and included price or cost information 40% of the time. Claude asked a clarifying question before answering in 46.7% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 6.7%, averaging 336 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 0%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 15 fintech answers it produced, Gemini recommended hiring a professional in 13.3% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 73.3% of answers (about 1.9 distinct providers per answer) and included price or cost information 40% 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 13.3%, averaging 200 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 0%; a selection-criteria checklist appeared in 26.7% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a fintech buyer to a professional (33.3%) and Gemini the least (13.3%). ChatGPT produced the longest answers, at 640 words on average. Specific providers were named most often by Gemini (73.3%) — even there, roughly one answer in 1 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 21.9 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a fintech buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 46.7% (ChatGPT) — a 47-point spread.
  • Suggests a DIY approach first: from 6.7% (Gemini) to 46.7% (ChatGPT) — a 40-point spread.
  • Names a specific provider: from 33.3% (Claude) to 73.3% (Gemini) — a 40-point spread.
  • Gives selection criteria: from 26.7% (Gemini) to 53.3% (ChatGPT) — a 27-point spread.
  • Recommends hiring a professional: from 13.3% (Gemini) to 33.3% (ChatGPT) — a 20-point spread.

The widest single gap — asks a clarifying question, 47 points — means a fintech 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 fintech market.

Where they agree

The points of near-consensus in Fintech.

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

  • Gives price or cost information: 40% across all three models.
  • Mentions case studies or portfolio: 0% across all three models.
  • Recommends multiple quotes: 0%–6.7% across all three (a 7-point spread).
  • Tells the buyer to check reviews: 0%–13.3% 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 "gives selection criteria" (33.3%).

Every behavior, measured

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

The behaviors AI models reproduce most often for fintech are names a specific provider (51.1% on average), gives selection criteria (44.4%) and gives price or cost information (40%); the rarest are mentions case studies or portfolio (0%), recommends multiple quotes (2.2%) and mentions local proximity (4.4%). 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:

  • Names a specific provider: 51.1% on average (ChatGPT 46.7%, Claude 33.3%, Gemini 73.3%) — a 40-point spread.
  • Gives selection criteria: 44.4% on average (ChatGPT 53.3%, Claude 53.3%, Gemini 26.7%) — a 27-point spread.
  • Gives price or cost information: 40% on average (ChatGPT 40%, Claude 40%, Gemini 40%).
  • Asks a clarifying question: 31.1% on average (ChatGPT 46.7%, Claude 46.7%, Gemini 0%) — a 47-point spread.
  • Suggests a DIY approach first: 28.9% on average (ChatGPT 46.7%, Claude 33.3%, Gemini 6.7%) — a 40-point spread.
  • Recommends hiring a professional: 24.4% on average (ChatGPT 33.3%, Claude 26.7%, Gemini 13.3%) — a 20-point spread.
  • Tells the buyer to verify credentials: 13.3% on average (ChatGPT 20%, Claude 6.7%, Gemini 13.3%) — a 13-point spread.
  • Warns about red flags or scams: 13.3% on average (ChatGPT 20%, Claude 6.7%, Gemini 13.3%) — 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 local proximity: 4.4% on average (ChatGPT 13.3%, Claude 0%, Gemini 0%) — a 13-point spread.
  • Recommends multiple quotes: 2.2% on average (ChatGPT 6.7%, Claude 0%, Gemini 0%) — a 7-point spread.
  • Mentions case studies or portfolio: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).

Trust signals

How well the models protect the fintech buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the fintech 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 44.4% of answers on average and a recommendation to gather multiple quotes in 2.2%. The single least-reproduced protective signal for fintech is "recommends multiple quotes" at 2.2% 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 Fintech providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 fintech answers, a specific provider was named in 51.1% of responses on average — roughly 1.8 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for fintech: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Fintech questions cover.

The 15 questions behind every percentage on this page were drawn from real fintech (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 fintech 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 fintech 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 2026-07-04, 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 →