AI visibility for
fintech.

Financial questions are filtered under the same YMYL logic as medical ones. The model leans toward regulators, established institutions and outlets with visible editorial standards, and the resulting source set is extremely concentrated — 91% of the fintech citations we observe come from the top twenty source domains. The objective is not to rank inside a long list. It is to enter a short one.

Comparison answers in this vertical are assembled attribute by attribute: fee, FX spread, minimum balance, insurance or partner bank, API availability, settlement time. The model needs one value per attribute per vendor. If your value sits inside a PDF, behind a signup, or gets computed by a JavaScript calculator, that cell is filled from a third-party comparison site and you are described in their words rather than your own.

Entity resolution matters more here than anywhere else. Before a model attributes a financial claim to you it has to be confident which company 'you' is — which is what Organization schema with sameAs links, a consistent legal name and a crawlable trust page provide. Fintech brands running three name variants across their own site make this harder than it needs to be.

And a failure mode unique to this vertical: fraud and abuse teams frequently block non-browser user agents at the WAF. OpenAI documents its crawler identifiers publicly, so the block is almost always indiscriminate rather than targeted, and nobody in marketing finds out for a quarter. Check crawler access before you check anything else on this page.

Head-to-head queries with a qualifier — 'Mercury vs Brex for a seed-stage startup' — sit at the decision point, and the qualifier is the winnable part. The unqualified comparison is contested by every affiliate site in the category, while the segment-specific version is usually unwritten by anyone.

Cost-with-a-constraint queries convert mechanically. 'Cheapest way to send USD to EUR under $5k' is decided by whichever provider's fee structure is machine-readable at the moment of retrieval. This is the clearest example in any vertical of a query won by document format rather than by content quality.

Eligibility questions are the most underserved shape here. 'Can a non-resident open a US business account', 'what documents does a UK LLP need' — extremely high intent, purely factual, and answered clearly almost nowhere. One answer page per eligibility question is the highest-yield writing available in fintech.

Advice-shaped queries are unwinnable and should not be funded. 'Should I invest in X', 'best way to save for retirement' — the model hedges, refuses specifics, and cites regulators or educational institutions. No amount of content quality changes the outcome, because the constraint is a safety policy rather than a retrieval preference.

Trust queries sit in between. 'Is <brand> safe' is partly winnable through a crawlable security page carrying specific facts, and partly decided by regulator records and third-party discussion you do not control. Publish the facts once, properly, and then stop spending there.

Wise's position comes from publishing the actual number. When a composed answer needs a fee to fill a cell and only one provider has a crawlable, current figure, the citation is not a contest. Almost everything else Wise does in this space is downstream of that one decision.

Stripe and Plaid win through documentation architecture — one concept per URL with complete examples — so technical questions across payments and banking data resolve to their pages even when the person asking is not a customer. Mercury's advantage is linguistic: it describes itself the same way on every page, which makes the association between the entity and the segment unusually tight. Ramp writes comparisons that name competitors and concede specific dimensions, which is what makes them usable as neutral sources.

A twelve-person fintech can copy three of these directly: publish your numbers as HTML at a stable URL, split your developer documentation one concept per page, and write one self-description sentence and use it identically everywhere. What you cannot copy is institutional trust — but checkability is an acceptable substitute for it, and it is the only substitute that scales down.

PDF fee schedules and PDF disclosures. This is the defining mistake of the vertical. PDFs are not scanned by default, they carry no structured data, and the numbers inside them are the exact values a comparison answer needs. Publishing the same table as HTML at a permanent URL is a two-hour job that changes which queries you are eligible for at all.

Blanket bot blocking. Security teams protect the application and the marketing site inherits the rule. Since the major crawler identities are published, an allowlist for documented AI crawlers on the public marketing and documentation hosts is a targeted change rather than a security compromise.

Unverifiable superlatives. 'Award-winning', 'trusted by thousands', 'bank-grade security' contain no checkable value. In a category filtered for authoritativeness they occupy the space where a fact should be, and they are precisely the sentences a model will not repeat, because it cannot attribute them to anything.

Comparison pages where you win every row, which cost more here than in software because the filter is stricter. And interactive pricing calculators with no static fallback: the widget serves users well and leaves nothing to extract, so keep a plain table of the same rates on the same page.

Fintech benchmarks measure whether your facts are reachable and attributable. Every row below is effectively binary in practice — the number is either in crawlable HTML under a resolvable entity, or it is not, and there is very little middle ground.

Verify the crawler-access row before anything else, because every other row is worth zero while it fails. Fee-schedule format is second, and it is the row most likely to be costing you comparison citations today — a competitor's stale summary of your pricing is currently the canonical one.