AI Search
for
Fintech.
ChatGPT, Perplexity, and Google AI Overviews are now the first stop for financial product research. The fintech brands being cited are not there by chance.
The shift
When a founder asks an LLM which business account to open, or a consumer asks which investment platform is best for beginners, the answer names two or three brands. No blue links. No comparison page. Just a recommendation.
The opportunity
Fintech is a category built on trust and word-of-mouth. LLMs are now the digital version of that recommendation layer. The brands positioning for this now will be structurally harder to displace as LLM usage compounds.
The new front door
to financial products.
How people used to research
A user Googled a query, compared comparison sites, clicked a few results, read reviews, and made a decision over several sessions. The fintech brand with the strongest SEO and best comparison-site presence won.
How they research now
A user asks ChatGPT or Perplexity directly. They get a synthesised recommendation that names specific products and providers. The comparison-site middleman is bypassed. The brand in the LLM answer is the brand that wins the consideration stage.
Where it happens in fintech
Business banking, personal current accounts, investment platforms, lending products, payment processing, FX, crypto on-ramps, savings accounts, and insurance. Wherever a financial decision is being researched, LLMs are increasingly answering the question before Google does.
What a citation is worth
A user who has received a specific product recommendation from an LLM arrives on your site with the most compressed sales funnel in digital acquisition. The research phase is done. The brand has been validated. All that is left is conversion.
How LLMs decide
which fintech to cite.
LLMs do not crawl the web in real time. They surface brands whose authority has been established across a wide body of text, news, reviews, regulatory announcements, forum discussions, and structured data. Fintech is a category where trust signals carry particularly heavy weighting.
01
Entity clarity
The LLM needs to understand precisely what your product does and who it is for. A neobank, a payment gateway, and a lending platform are very different entities. Ambiguous positioning produces ambiguous citation, or none at all.
02
Regulatory and FCA signals
In the UK, FCA authorisation is a credibility signal that LLMs have learned to weight. Your regulatory status, permissions, and FCA register listing are inputs the model uses to judge whether your brand is a legitimate financial services provider.
03
Press and publication coverage
Fintech brands covered consistently in TechCrunch, Forbes, the FT, City A.M., AltFi, and sector trade publications carry that authority forward into LLM training data. Volume and quality of press coverage are among the strongest citation signals.
04
Topical content depth
Brands that publish genuinely useful, accurate content on their product categories, such as explainers, guides and market commentary, are treated as subject authorities. Thin product pages and no editorial presence produces thin citation rates.
05
Customer trust signals
Trustpilot scores, App Store ratings, review counts, and third-party comparisons all feed LLM assessments of brand credibility. In financial services, a pattern of poor reviews or unresolved complaints is visible to the model.
06
Structured data and schema
Clear schema markup such as FinancialProduct, Organization, FAQPage and BreadcrumbList helps LLMs extract accurate product and entity information from your site. Models that cannot cleanly parse your offering are less likely to recommend it confidently.
From AI answer
to account open.
The journey from LLM recommendation to fintech conversion is the most underreported acquisition channel in financial services. Here is how it works and why it does not show up in your current analytics.
User journey
User asks an LLM: "What is the best business bank account for a startup?" or "Which investment platform should I use as a beginner?"
The model names two or three products by brand, with reasons. The shortlist is set here. If your brand is not cited, you are not in the consideration set.
The user Googles the recommended brand to read reviews, check the app, verify FCA status, and confirm the product fits their situation.
The visit registers as branded search or direct. There is no LLM attribution in standard analytics. The recommendation that drove the session is invisible.
The user signs up, opens an account, or begins onboarding with zero comparison-shopping mindset. The LLM already closed the top of the funnel.
What we build to get you cited
Entity definition
Sharpen how LLMs understand exactly what your product is, who it is for, and what category you compete in.
Press authority
Secure consistent coverage in the publications LLMs are trained on, fintech press, national business media, and sector analysts.
Content expertise
Build a library of topically authoritative content that signals product and category depth to both LLMs and search engines.
Trust signals
Grow and publicise the review scores, regulatory credentials, and third-party validations that LLMs use to assess financial brand credibility.
Structured data
Schema implementation that makes your products, FAQs, and organisation clearly parseable by language models.
Financial decisions.
Trusted source.
The referral effect
Fintech grew on word-of-mouth referrals. Monzo, Revolut, and Wise did not acquire their early user bases through paid search, they spread through trusted personal recommendation. LLM citations are the scaled, digital version of that dynamic. When the AI says "consider X for business banking," the psychology is the same as a trusted contact saying it.
The attribution gap
LLM-sourced users land on your site via Google branded search or direct. If your branded search volume is growing without a clear cause (no above-the-line campaign, no viral moment), some of that growth is AI search working in the background. The revenue is real. The source is invisible in standard analytics.
YMYL and trust weighting
Your Money Your Life content, covering financial products, investment advice and lending, is treated with greater scrutiny by both Google and LLMs. This is a moat for regulated, credible brands. FCA-authorised fintechs with strong trust signals have a structural advantage over unregulated or poorly-reviewed competitors in LLM citation patterns.
The CAC argument
Paid acquisition costs in fintech are high and rising. A user who arrives via an LLM recommendation has a cost of acquisition close to zero, and converts at a rate that rivals the best referral programmes. Building LLM visibility is not a brand exercise. It is a CAC reduction strategy with compounding returns.
Audit first.
Then build.
Every engagement starts with a clear picture of where you sit in AI search today. What gets built is determined by the gaps and opportunities the audit surfaces, not a templated service list.
Phase 1: The AI Search Audit
LLM citation audit
We query ChatGPT, Perplexity, Google AI Overviews, and Gemini with the financial product questions your customers are asking. We map citation frequency, brand framing, competitor share of voice, and the language models are using to describe you.
Entity and brand definition
We assess how clearly your brand is defined across the web, FCA register, Companies House, Google Knowledge Panel, Wikipedia presence, structured data, and the consistency of your brand description across authoritative sources.
Regulatory signal review
We check how your FCA status, permissions, and regulatory credentials are represented across the web and on your own site. In YMYL categories, regulatory credibility is a direct input to LLM trust weighting.
Press and offsite coverage
We audit your existing press footprint, which publications have covered you, how often, and how they describe your product. We identify the gaps between your coverage and that of the brands currently being cited ahead of you.
Content and topical authority
We review your on-site content against the questions LLMs are answering in your product category. Depth of coverage, accuracy, and breadth across the topic area all determine whether you are treated as an authority or a listing.
Trust signal baseline
We review your Trustpilot profile, app store ratings, review volume and recency, and any third-party awards or accreditations. We benchmark these against the brands currently getting cited in your target queries.
Phase 2: The Implementation Plan
Entity and regulatory clarity
Structured data implementation across your site, FCA register optimisation, Google Knowledge Panel corrections, and consistent brand description standardisation across all authoritative sources.
Fintech press programme
A targeted digital PR programme securing coverage in the publications LLMs draw on most heavily for fintech authority: national business press, specialist fintech media, and consumer finance titles. Coverage that sticks, not one-off mentions.
Content authority build
A content programme across your product categories that establishes topical depth. Guides, explainers, market commentary, and comparison content that answers the questions buyers are asking LLMs, and positions your brand as the authoritative source.
Schema and structured data
Full schema implementation for your product pages, FAQs, and organisation data. FinancialProduct, Organization, FAQPage, BreadcrumbList, and Review markup that makes your offering parseable and citable by language models.
Trust and review programme
A structured programme to grow Trustpilot review volume and recency, publicise industry accreditations, and build the third-party validation signals that LLMs use to assess credibility in financial services.
Measurement framework
LLM visibility requires a different measurement approach. We set up a citation monitoring cadence across the major models, track branded search trends as a proxy for LLM-driven traffic, and report on AI search share of voice against your competitors monthly.
Start with
the audit.
Book a call and we will run an initial LLM citation check on your brand and your closest competitors before we speak. You will see exactly where you sit in AI search going into the conversation.
Book a Call