I. The deceptively simple question
Imagine a potential customer opening ChatGPT and typing: “Which bank would you recommend for a mortgage in Belgium?”
The model responds. It names names. It provides context. Maybe your brand is in there, maybe not. Maybe what it says is accurate, maybe not entirely.
What most marketers think at that point: “Then we need to score better in those answers. Just like we did in Google.”
What they underestimate: financial brands play this game by different rules. Not slightly different — fundamentally different.
This is not an excuse. It’s a diagnosis. Because you can only solve a problem once you understand why it’s harder than it looks.
II. The compliance paradox: your content doesn't disappear — your brand does
Language models are trained on text that people write and read naturally. Wikipedia articles. Blogs. News reports. Forum posts. Those are the sources from which LLMs draw their understanding of the world.
Financial product communications are not written in that language.
A bank website writes: “This product is subject to market risks. The value of your investment may fall. Past performance is no guarantee of future results. This does not constitute investment advice.”
Legally necessary. Communicatively deadly. And for an LLM? Difficult to synthesise.
But here’s an important nuance that’s often overlooked: a model doesn’t ignore that content — it abstracts it. It extracts the usable information, but drops something along the way: your brand name. The result is a correct but generic answer, without attribution.
The problem isn’t that your content is being ignored. The problem is that your content loses its brand in translation. Legal language is designed to eliminate all ambiguity, resulting in rigid, unnatural sentence structures. For an LLM, this reads as noise — the core message carries less weight, and the brand name is the first thing to disappear.
III. Your real competitor is called Wikifin
In traditional search engines, you knew who your competitors were. Belfius. ING. Argenta. You benchmarked on the same keywords, the same SERP positions.
In AI answers about financial products, that landscape shifts entirely.
The sources LLMs rely on most heavily for financial information in Belgium are not the websites of banks or insurers. They are independent, neutral sources: Wikifin.be, Test-Aankoop, Spaargids.be, the FSMA, journalistic pieces from De Tijd or Knack.
That’s structurally logical. AI models are optimised to limit hallucinations and give “defensible answers.” A government site or consumer organisation scores higher on that front than a brand that by definition has a commercial interest. A bank that only talks about itself on its own domain stays trapped in a commercial bubble that the model more readily filters out as less reliable for objective advice.
The problem: those sources often don’t name specific brands. Or they compare them in ways you as a brand have little influence over. Your marketing budget, your campaign strategy, your positioning documents — they have no grip on it.
Being present in AI answers about financial products therefore requires a different strategy than being visible in Google. Not pushing harder on your own brand, but becoming more relevant in the sources that models trust.
IV. Present but interchangeable — the real problem
I want to pause here on something I consistently see in recent measurements, and which makes the picture more nuanced than “financial brands are invisible.”
They’re not invisible. They are recognised.
But in almost half of all AI answers about financial products and services in Belgium, no brand name appears at all — even when the question directly asks for one. The model answers correctly, completely, usably. But anonymously. And when a brand does appear, it averages barely two. Not a shortlist of five. Not a broad landscape. Two names, sometimes one.
The difference between a question where your brand is explicitly asked about versus a general consumer question is dramatic. In the first case, you’re almost always included. In the second — the question a user asks when they’re genuinely orientating — the chance of being mentioned drops by more than half.
That’s the real problem. Not absence, but interchangeability. A model that knows your brand but doesn’t mention it when it matters is strategically more dangerous than a model that doesn’t know you at all.
V. Product complexity and citation logic don't mix
LLMs like to cite clear, verifiable facts they can include in an answer without having to introduce too much nuance.
“The Eiffel Tower is 330 metres tall.” A model cites that effortlessly.
“A mortgage at Bank X has a variable rate of 3.45% over 25 years, depending on your EPC score, the size of your down payment, your employment contract and the actuarial calculation of your mortgage protection insurance” — that a model does not cite. The more variables, the lower the model’s confidence score to name a specific figure. When in doubt, AI chooses safety — generic information — over precision. Your specific offer disappears in the simplification.
The consequence is predictable: for complex financial products, models more often give generic descriptions without a brand name, without specific rates, without distinguishing features. Simpler products — a savings account with one interest rate, a basic package for daily banking — perform structurally better in AI answers than complex products such as investment insurance, branch 23 products or business credit.
VI. Brand identity is not self-evident for AI
Mergers, rebrands and complex group structures are more the rule than the exception in the financial sector. And AI models struggle with that complexity.
When a single financial group operates under multiple brands — regional, language-oriented or historically grown — a model often treats those brands as separate, standalone entities. The legal connection between those brands doesn’t exist in the information layer on which LLMs operate. What this means in practice: a group that is the market leader as a whole can be significantly less visible per individual brand than a competitor playing with one strong brand.
A bank with one name, one site, one entity has a structural advantage here. Not because they’re better — but because they’re simpler for a model trying to summarise the world to understand.
This is one of the few points that resolves itself over time. Retrieval-Augmented Generation — where models consult the web in real time before answering — improves entity resolution faster than the other structural challenges in this piece.
But RAG is also an opportunity. Where a model previously relied exclusively on training data — and your brand was correctly or incorrectly represented in it with nothing you could do about it — a model with live web access today cites your own content directly. Control over your attribution comes back, partly, provided that content is findable, clear and machine-readable.
What this means in practice: a model that scans your website and hits a wall of compliance language still falls back on a Spaargids.be summary. But a model that finds a well-structured FAQ page that answers exactly the question the user asked — that cites you. RAG raises the stakes — it increases both the opportunity and the risk simultaneously.
VII. The model matters more than you think
One observation I want to add because it’s practically relevant: not all AI models behave the same way towards financial brands.
The spread between models is significant. Google AI Mode and ChatGPT mention brand names considerably more often than Perplexity or Claude for identical questions. That difference isn’t random — it has to do with how models handle live web data, how they weight sources and how cautious they are about making recommendations.
What this means for strategy: your target audience doesn’t use one model. And your visibility differs significantly per model. A brand that scores well in ChatGPT can be virtually invisible in Perplexity — on the exact same question.
That makes monitoring across multiple models not a luxury but a basic requirement.
VIII. Compliance slows down what AI visibility requires
AI visibility requires content. Specifically: content that is clear, answer-oriented, written conversationally and published quickly.
The reality in financial institutions: every text that goes external passes through a compliance and legal filter. That’s not unjustified — the sector is heavily regulated, and the consequences of an incorrect communication are real. But it creates a structural disadvantage compared to sectors where content can be published faster and more freely.
A fintech startup can publish ten blog posts about personal finance in a week, all optimised for the questions people ask AI. An established bank needs that same week to guide one text through the approval process.
In a world where AI learns from volume and variation, and where questions evolve continuously, that is a structural disadvantage. Not cultural, not temporary — structural.
IX. The real paradigm shift
Most financial brands have invested for years in authority via backlinks. AI requires something different: authority via entities.
The question used to be: “How do I make sure I rank at the top for the word ‘mortgage’?”
The question now is: “How do I make sure an AI model understands my brand as a reliable solution for a specific problem?”
That’s not an iteration on SEO. It’s a different discipline.
Financial brands aren’t off the radar because they’re worse than their competitors. They’re off the radar because they produce less usable answers for a model that has to choose. Present but interchangeable is the position most players occupy today — and that’s precisely where you need to intervene strategically.
Not with more budget. With better structure, clearer content and a stronger entity profile.
The brands that understand this first have a lead that’s hard to close. Because AI visibility isn’t built in a week, not even with a large media budget.
AI visibility is not a ranking problem. It is an attribution problem.
