Before You Optimise for AI Search Get Your Fintech Marketing Foundations Right

At Mixology, we work with Blue Train Marketing on AI visibility programmes for fintech and payments brands. Blue Train leads on search and digital foundations; we focus on earned media and the independent authority that sits outside a brand’s own channels. This piece, written by the Blue Train team, sets out why strong marketing foundations matter before any AI optimisation work begins and why that question is more relevant to fintech brands than most.

Ask an AI platform to describe your business, and you may not always like the answer.

For fintechs and payment companies, this could be an especially big problem due to the complexity of the ecosystem and its rainbow of jargon. In addition, players in this space may offer related services through third parties, which can further confuse LLMs and human readers. 

As a result, an LLM may describe you as a payment processor when you are actually an acquirer (who offers processing via a partner). It might focus on one legacy product and overlook the services you now want to grow. Or perhaps it gives a vague description that could apply just as easily to half a dozen of your competitors.

In May 2026, the number of website visits from across generative AI platforms was up 70% year-over-year, reaching 9.5 billion, data. With behavioural change of this magnitude, the immediate response is likely to be, “We need to optimise for AI.”

Between June 2025 and May 2026, generative AI platforms averaged 9.5 billion monthly web visits worldwide, up 70% year over year, according to Similarweb. Similarweb also found that users were two to four times more likely to visit a brand recommended by AI than a competitor that wasn’t. 

So the immediate response might be: “We need to optimise for AI.”

Sometimes you do. But before jumping into AI-specific optimisation, it is worth asking a more fundamental question:

Has AI misunderstood the business, or has the business made itself difficult to understand?

For fintech and payments companies in particular, poor AI visibility can expose marketing problems that existed long before generative search even arrived: unclear propositions, inconsistent terminology, fragmented content, weak differentiation, and too little evidence of genuine expertise.

In our experience, this is likely to happen to fintechs who have been established for a few years. The solution has evolved, and the target market has been refined, but not all the marketing materials, website copy, and external reference sources have kept up.

AI optimisation can make strong marketing signals easier to discover and interpret. It cannot manufacture clarity that is not there in the first place.

AI Visibility Can Expose an Existing Marketing Weakness

Generative AI systems build answers using the information available to them from multiple sources. Depending on the platform and query, they may retrieve information from the web as part of that process.

That creates an obvious problem for brands sending inconsistent signals.

Imagine your homepage calls the business a “payments platform”, your LinkedIn profile describes it as a “payment technology provider”, an old press release calls it a “processor”, and third-party articles mostly associate it with one particular gateway product.

Which description should an AI system choose? More importantly, would a potential buyer be any less confused?

These distinctions matter because not every AI visibility problem is an AI optimisation problem.

If the underlying information is already clear, accurate, and authoritative but the brand is not being surfaced, optimisation and external authority-building may help. That’s the quickest problem to fix.

But if different parts of the business describe the proposition differently, the first task is some good old marketing housekeeping. 

AI can make those inconsistencies much easier to spot. 

Define the Business Before Optimising It

One of the simplest tests of marketing clarity is whether an informed outsider can quickly answer five questions:

• Who are you?
• What do you provide?
• Who do you provide it to?
• Where do you operate?
• What outcomes do you help those customers achieve?

That may sound basic. But in fintech and payments, it often isn’t.

Payments terminology provides a good example.

“Acquirer”, “processor”, “gateway”, “payment service provider,” and “payments platform” are sometimes used almost interchangeably in marketing copy, despite describing different roles and capabilities.

There may be legitimate reasons why a company spans several categories. The problem arises when the terminology changes depending on which page, profile, salesperson, or press release somebody encounters.

Before trying to optimise how AI systems understand the organisation, establish a simple entity definition that everyone internally agrees is accurate.

For example:

[Company] is a [clearly defined type of business] providing [core products/services] to [primary audiences] in [markets], helping them [principal commercial outcome].

It will not necessarily appear word-for-word everywhere – nor should it.

But the core facts within it should remain consistent across your homepage, About Us page, product pages, social profiles, company descriptions, executive biographies, and other important digital properties. 

The objective isn’t repetitive copy. It is consistent meaning.

Decide What You Actually Want to Be Known For

Being understood is only one part of AI visibility. Being associated with the right expertise is another.

This is where fintech marketers need to resist the temptation to talk about everything. 

Every few months brings a new subject competing for attention: embedded finance, open banking, real-time payments, digital assets, AI, agentic commerce, stablecoins, identity, fraud, orchestration, and financial inclusion.

Joining every conversation does not automatically build authority. In fact, it can dilute it. 

The partnership model is well-established in the fintech ecosystem as a way of presenting prospective clients with integrated supporting services. But avoid blurring the lines between your core offering and what partners may offer, as this will also create confusion for LLMs. 

A company is more likely to establish a meaningful position if it identifies a focused set of subjects where three things overlap:

Commercial Relevance + Genuine Expertise + Audience Need.

Take each priority topic and test it.

Do you have people inside the organisation who genuinely understand it? Do you have practical experience, customer insight, proprietary data, or an informed point of view? Does the subject matter to the buyers you want to reach? And can you credibly sustain a conversation about it beyond one opportunistic blog?

If not, it probably shouldn’t be a cornerstone of your AI visibility strategy. This is where your AI visibility strategy should run parallel to your existing marketing content strategy. 

Authority is much easier to build around expertise the company actually possesses.

Turn Buyer Questions into Useful Content

Traditional SEO often put considerable emphasis on keywords and search queries. 

Keywords still matter, but AI-led discovery makes the questions behind those searches increasingly important.

Think about what a potential buyer needs to understand while researching, comparing, and shortlisting fintech providers.

They might ask:

• Who is this solution designed for?
• Which markets does it operate in?
• What exactly does it integrate with?
• How does it differ from another type of provider?
• What evidence is there that the solution works?
• Which regulatory or compliance requirements does it support?
• What are the alternatives?
• When would one approach be more appropriate than another?

These are not simply search terms. They are buying questions.

Create content that answers each point clearly.

Use precise definitions where terminology is complicated. Give the direct answer before adding detail. Structure longer content logically. Explain differences rather than assuming everybody understands the category. Support claims with evidence.

This helps humans scan and understand content, while also making important passages easier for search and retrieval systems to interpret.

If you have a solid marketing content strategy, you will notice a strong parallel with your AI visibility strategy. That’s because good content for AI search often looks remarkably similar to good content for people. 

Give the Brand Something Original to Say

Here is another uncomfortable truth about AI optimisation: making generic content easier for an AI system to retrieve does not suddenly make that content valuable.

Generative AI has dramatically reduced the effort required to produce competent-looking copy.

It has not created expertise. In other words, it’s raised the bar on bland, repetitive content we like to call AI slop. 

As we have argued previously at Blue Train, the important distinction is increasingly between content production and having something worth saying.

That something might come from proprietary research, customer experience, internal specialists, product data, first-hand observations, technical knowledge, or a well-supported opinion that challenges conventional industry thinking.

The strongest fintech content should therefore contain signals that could not simply have come from asking a generic AI tool to “write 1,000 words about payment trends”.

Name the specialists behind the insight. Use original evidence where possible. Connect claims to genuine experience. Allow knowledgeable people to express an opinion.

AI can absolutely support the process. It can help with research, structure, ideation, and drafting. But human judgement still needs to determine what is accurate, what matters, and what the business genuinely believes.

Accuracy, differentiation, and brand voice remain human responsibilities. Volume alone is not authority.

Make that Expertise Discoverable & Reinforce it Externally

Once the foundations are solid, optimisation becomes much more effective.

Different disciplines then play complementary roles:

• SEO (Search Engine Optimisation) helps relevant content become discoverable through traditional search and ensures the website provides a strong technical and content foundation.
• AEO (Answer Engine Optimisation) focuses on making information clear, structured, and easy for answer-based systems to identify. 
• GEO (Generative Engine Optimisation) is a broader industry term for improving how generative AI systems understand and surface a brand, its content, and expertise. 
• PR (Public Relations) builds credibility beyond the company’s website by connecting its experts, opinions, and insights with authoritative third-party sources. This is an area in which traditional marketing agencies tend to struggle. They don’t usually have the necessary media contacts and expertise to successfully place articles in the most cited outlets.  

However, many mid-size fintechs often don’t have the budget or need to hire a separate PR agency. Addressing this issue was the motivation for the partnership between Blue Train Marketing and Mixology.  

A company that clearly defines what it does, demonstrates useful expertise on its own channels, and earns credible reinforcement in relevant media creates a much stronger information environment around its brand.

Blue Train Marketing’s How to Build an AI Visibility Strategy for Payments & Fintech explores how these elements work together in considerably more detail.

The importance of external authority is explained by Mixology in From press coverage to AI visibility. What happens after the story is published?, which sets out how what credible third parties say about a brand increasingly forms part of the evidence surrounding its expertise.

But neither marketing optimisation nor earned authority works particularly well if the underlying proposition remains muddled.

AI Visibility Begins with Clarity

AI search has created new ways for fintech and payments companies to be discovered, researched, and compared. But it hasn’t removed the fundamentals of good marketing.

Before asking how to optimise your business for AI search, make sure there is something clear, distinctive, and authoritative for those systems to find.

Define the company. Decide what it should be known for. Answer the questions buyers genuinely ask. Give your experts something useful to say. Make that expertise easy to discover and support it with credible evidence beyond your own channels.

Then SEO, AEO, GEO, and PR have something meaningful to amplify.

A useful first step is surprisingly simple: compare the way you describe your business with the way search engines, third-party sources, and AI platforms describe it back to you.

If those versions do not match, an optimisation project is not the most important task on your list.