How can a bank or fintech get recommended by AI assistants?

More than half of American adults now use AI when they make personal finance decisions, and most still want a human to make the final call. TD Bank's AI Insights Report, a survey of 2,504 US adults run on 18–25 February 2026, found that 55% use AI to help with financial management decisions, up from 10% a year earlier. Only 18% would trust AI to make a financial recommendation on its own. The assistant builds the shortlist; the person picks from it.

Money is YMYL — Your Money or Your Life, in Google's term — and assistants handle it the way they handle health and law: questions about safety, regulation and how products work go to regulators and established publishers. A bank’s explainer on how deposit insurance works competes with the scheme’s own site and with reference publishers that already rank for it; we do not put a market-wide figure on that layer, because our own run below did not include safety prompts, and a figure we cannot verify against a primary source does not belong on this page.

Banks and fintechs can compete on questions about choosing a provider: who the product suits, what it costs and which licence covers it. That is the focus of GEO for financial services. General explanations of concepts such as APR face competition from established publishers like Investopedia.

The prompts a bank or fintech can win are the ones where the answer is a named provider with a checkable fact attached: a fee, a rate, a segment, a licence. “How does deposit insurance work in the EU” is answered from regulators and reference sites. “Best business bank account for a UK startup”, “lowest FX fees for transfers to India”, “which neobank is safe for freelancers in Spain” are answered from provider pages, comparison publishers and the occasional press article, and that is where the competition is open.

Your own page matters even when publishers dominate the citations. OpenAI’s help page on ChatGPT search says the model “typically rewrites your query into one or more targeted queries” and may send follow-up queries after the first results; in our run below, those retrievals landed on provider fee and terms pages in nine of nine answers. Ahrefs found in December 2025 that 43.8% of the source links in ChatGPT answers to “best X” prompts pointed to “best X” lists (750 prompts, 26,283 source links). In our page-types study, a classification of 569 links cited across 120 answers by ChatGPT, Perplexity, Google AI Mode and the Gemini app on 14–15 September 2026, 48% pointed to vendors’ and agencies’ own sites, 16% to directories and comparison sites, 10% to forums and 4% to media; on questions about choosing a provider, the fintech prompts among them, 33% of links went to providers’ own pages and 25% to directories and comparison sites. The assistant looks for the official fee page and a ranking that corroborates it; if either is missing, it quotes whatever a comparison site said last.

We ran the three example prompts below in ChatGPT on 14 September 2026, signed out with web search on, three clean sessions each (nine answers). Provider pages were cited in nine of nine; comparison sites in three; press in three; Reddit, Wikipedia and regulators in none. The pages the model took from providers were the ones with a checkable fact on them: service-quality survey results (Starling, Monzo), deposit protection (Revolut, N26), segment terms and fee pages (N26 autónomos, Tide). The third column of the table is that log, not an estimate. On 15 September we ran the same prompts twice each in Perplexity (signed in, personalisation off) and Google AI Mode (signed out). Across the six commercial answers per service, Perplexity cited provider pages in five and comparison sites in all six. AI Mode cited provider pages in three and comparison sites in five. These categories overlap: one answer can cite both. The engines differ enough that the table stays ChatGPT-only.

Assistants cite what they can verify against a second source and lift without rewriting, and for a financial product that means dated numbers and regulatory facts in plain page text. A fee table in a PDF, or a rate that appears only after the visitor picks a country in a calculator, gives the model nothing to quote, so it quotes NerdWallet instead.

Corroboration — confirmation of your facts by pages you do not control — carries more weight in finance than in most categories. If your fee, licence number and product name differ between your site, your Wikipedia entry and your Bankrate or MoneySavingExpert listing, the model has no consistent entity — a single, unambiguous organisation — to recommend. Community threads belong to that layer too. In our six commercial Google AI Mode answers on 15 September, provider pages appeared in three and comparison sites in five; press and Reddit also appeared among the sources. Categories overlap, so this does not mean comparison sites replaced provider pages.

The entity problem has a specific form for neobanks. Many hold customer money through a partner bank or an e-money licence, and a page that does not say which is which leaves the model to guess between “fintech” and “bank” on exactly the prompts where the distinction decides the answer. In our run, the provider pages ChatGPT took from Revolut and N26 were their deposit-protection pages. The fix is a page, and matching third-party entries, that state who holds the deposits and which scheme protects them up to what amount, in the regulator's own words.

A page written to be quoted by an assistant is still a financial promotion, and advertising rules apply to it in full. FINRA Rule 2210 covers communications with the public by member broker-dealers, and a public web page will normally be a retail communication under it. Content must be “fair and balanced”; no “false, exaggerated, unwarranted, promissory or misleading statement” is allowed; a material fact may not be omitted where the omission would mislead; information may sit in a footnote only where that does not inhibit understanding. Communications may not predict or project performance, comparisons must disclose all material differences, and a registered principal must approve each retail communication before use. This guide is not legal advice: route every page through compliance before it goes live.

In the United Kingdom, the FCA regulates the promotion of loans, investments, cash savings and bank accounts, mortgages, payment services and e-money, and qualifying cryptoassets. All financial promotions must be “clear, fair and not misleading regardless of the media type”, and a website counts as a promotion. Product rules sit in the Handbook (BCOBS for banking, COBS 4 for investments, CONC for credit), and the Consumer Duty requires firms to put customers' needs first. SEC rules for investment advisers, and ESMA and MiCA for EU fintech and crypto firms, add their own standards; check with compliance for each market you publish in.

For GEO the constraint is useful. What a regulator forbids — “the safest neobank”, projected returns, a headline fee without its conditions — is what an assistant cannot verify and tends to discount. What the rules require — the fee with its conditions, the licence with its register entry — is what a model can lift into an answer with a source attached.

The same prompt gets a different shortlist in each country, from different sources. In our 14 September run, the UK startup prompt was answered from Starling, Tide and Monzo pages with MoneySavingExpert, Forbes Advisor and the FT as the publisher layer; the Spain freelancer prompt was answered from N26 and Revolut's Spanish product pages with Cinco Días and EFE as the press layer, and none of the five providers cited for the UK prompt appeared. The publisher layer fragments along national lines, and so does the brand set — three prompts, one day, so treat it as a map rather than a scoreboard.

So a bank or fintech competes market by market, page by page. A single English product page does not answer “best business account for a startup in Spain” or “welche Bank für Freelancer in Deutschland”; the assistant needs a page for that market, in that language, with that market's regulator, fees and protection scheme, and matching entries on the sites it cites there. Our guide on multilingual GEO covers the structure, and our US guide explains why the Spanish-speaking segment is a separate baseline.

We start from a prompt set — a fixed list of 30–50 buyer questions per language, built from product × segment × country × constraint — rather than a keyword list: “business account UK startup”, “FX fees India transfer”, “neobank freelancer Spain safe”. We run each prompt in clean sessions (logged out, or temporary chat with memory off) on ChatGPT, Perplexity and Gemini, with repeats, and log every answer with date, engine and screenshot.

Citation rate is the share of prompts on which the assistant cites one of your pages. Share of voice is the share of provider mentions that are yours among the providers named on the same prompts. The baseline is re-run every two to four weeks in a pilot and monthly on a retainer; if neither metric has moved after 90 days we say so first, and the prompt set, logs and reports stay with you. In this vertical, every page change we propose goes to your compliance team before publication, and the log records which approved version was live at each measurement.

The free audit is the first run of that protocol: ten prompts in one language where you are currently invisible, today's answers with the sources cited, the page changes that would give assistants something to quote, and a check that AI crawlers can reach your product pages.