AI assistants offer another way for people to research bank accounts, brokers, loans and insurance policies. When a potential customer asks ChatGPT, “Which broker is best for an ETF savings plan?” or “What is the cheapest liability insurance?”, the brands appearing in that answer can enter the consideration set. For banks, brokers and insurers, understanding the sources behind these answers is a useful starting point for investigating AI visibility.
Peak Ace’s LLM Visibility Benchmark analysed 1,000 commercially relevant prompts across 10 industries to measure which brands appear in AI-generated answers, which sources are retrieved and how brands are represented. Based on a fixed German-language prompt set tested across ChatGPT, Gemini and Google AI Overview in August 2026, our standardised snapshot shows how the source mix differs by industry and intent.
Here’s what the data reveals for finance and insurance, and what it may imply for GEO.
Contents
- How we conducted our LLM Visibility Benchmark
- Which sources do AI systems use for finance questions in our benchmark?
- How does the finance source mix compare with other industries?
- Which finance brands are most visible in the benchmark snapshot?
- How can finance brands approach AI visibility? Building your GEO finance strategy
- The full picture is in the LLM Visibility Benchmark
- FAQ: AI Visibility for Finance & Insurance
How we conducted our LLM Visibility Benchmark
The benchmark is built on a clear, standardised methodology:
- 1,000 prompts across 10 industries, each with 100 commercially relevant, bottom-of-funnel prompts.
- Prompts were split across 10 topic clusters per industry to capture different decision-making situations.
- Four intent types were tested: best/top/test winner, comparisons, pricing/where to buy, and criteria/guidance, with 25 prompts per intent in each industry.
- The analysis focused on the German market and German-language answers from 1–31 August 2026.
- Each prompt ran 15 times in total, with five runs each across ChatGPT, Gemini and Google AI Overview.
- Data was sourced from Peec AI exports. Fifty brands per industry were monitored; brand reporting focuses on the Top 20 by visibility within the tracked set.
Alongside source analysis, the benchmark reports visibility, Share of Voice (SOV) and sentiment. Visibility measures how often a tracked entity appears in answers for the prompt set. Sentiment measures the tone of answers mentioning it on a scale of 0–100, where higher means more positive. These are indicators within the study, not market share or absolute assessments of provider quality.
For the full methodology, definitions and cross-industry breakdowns, the complete benchmark is available for download.
Which sources do AI systems use for finance questions in our benchmark?
The benchmark analysed which types of content the tested AI systems retrieve when processing prompts centred around finance and insurance. The finance scorecard records 89,485 retrievals during the study period.
Important: retrieval is not the same as citation. A retrieval records a page accessed as an information base. It does not necessarily mean that the page was explicitly named or linked in the final answer. The source mix below describes retrieved formats, not citation shares.
The source mix varies by intent:
| Intent | Benchmark source types, ranked |
| Best/Top/Test winner | Comparison, Product Page, Article, Homepage |
| Compare vs. | Comparison, Article, Product Page, Category Page |
| Criteria/Guidance | Article, Comparison, How-To Guide, Category Page |
| Pricing/Where to buy | Comparison, Article, Product Page, How-To Guide |
A few things stand out:
- Comparison pages lead three of the four intents. They rank first for best/top, comparison and pricing prompts, and second for criteria and guidance. This makes comparative content a recurring part of the observed finance source ecosystem.
- Articles appear in the top three for every intent and lead criteria and guidance. The pattern points to an explanatory layer throughout the tested decision journey, not just at the research stage.
- Product pages make the top three for recommendations, comparisons and pricing. Offering-level information appears alongside comparison and editorial formats. A product-page classification alone does not establish whether a page belongs to a provider or a third party.
- Listicles are absent from the top four for every finance intent. This differs from Automotive, where listicles lead recommendation questions. Absence from the leading formats does not mean zero retrievals.
- How-to guides appear among the leading formats for criteria and pricing. Their presence gives a reason to examine practical explanations alongside comparisons and product information.
For finance specifically, comparative and explanatory content is the main takeaway. This may reflect decision situations in which people need to understand differences between offers, conditions and costs. It does not prove that a particular format caused a brand recommendation.
The white paper names Finanztip, Finanzfluss, test.de and Verivox as examples of comparison and advice sources in the finance context. These illustrate the information ecosystem; they are not presented here as a measured ranking of citation-leading domains.
How does the finance source mix compare with other industries?
Across the benchmark, comparison pages are the largest individual source format by retrieval volume. The finance scorecard follows that wider pattern for recommendations, comparisons and pricing, but adds an important distinction for guidance.
Across industries, how-to guides lead criteria/guidance with 22.33% of the retrieval mix, followed by articles at 19.82%. In Finance & Insurance, articles appear first, followed by comparisons, how-to guides and category pages.
For comparison questions, the cross-industry mix is led by comparison pages at 32.65%, followed by articles at 16.06% and product pages at 10.46%. The finance scorecard shows the same first three formats in that order.
The practical implication is not that finance performs better or worse than another industry. It is that a finance content strategy should investigate both comparative information and editorial explanation, rather than treating every advice question as a how-to task.
Which finance brands are most visible in the benchmark snapshot?
As additional context, the three tracked brands with the highest visibility in the finance scorecard are:
- ING: 19% visibility
- Trade Republic: 15% visibility
- Scalable Capital: 12% visibility
All three are banking or brokerage brands; no insurer appears in the top three. This may reflect the financial decision situations covered by the prompt set, as well as the study’s brand mapping. It does not establish a universal preference for banks or brokers over insurers, or prove that the brands’ visibility results from a particular content strategy.
The scorecard reports average visibility of 6.15%, SOV of 3.4% and sentiment of 54.4. The three leading brands account for 40% of the normalised SOV within the Top 20, with an HHI of 856. These concentration figures describe the distribution within the tracked set, not market share or the likelihood that a smaller brand will be recommended.
The brand findings provide context. The more actionable focus remains the source ecosystem behind different finance questions. Visibility is not an endorsement of a provider or evidence of the suitability of its products.
How can finance brands approach AI visibility? Building your GEO finance strategy
The data raises several questions that marketers in banking, investing and insurance should consider:
- Are you represented on relevant comparison and advice pages? A strong brand website is one part of the information ecosystem. Investigate the sources retrieved for your own finance topics and whether they describe your offers accurately. The benchmark does not prove that absence from one source means absence from the final answer.
- Does your content strategy cover all four intent types? Brands that only optimise for “best broker” or “best insurance” questions address one quarter of this prompt set. Comparisons, pricing and guidance account for the other 75%. This is a defined study structure, not an estimate of consumer demand.
- Are your product pages clear enough to support comparisons? Product pages appear in the top three for three intents. Review whether features, fees, eligibility and conditions are explained consistently. This is a practical audit priority, not a page attribute causally tested in the benchmark.
- Are you monitoring how your brand is described, as well as whether it appears? Sentiment offers relative context within the study. It does not establish that positive reputation causes recommendations. Track brand mentions, retrieved sources and explicit citations separately.
- Have you considered a GEO finance strategy? Generative Engine Optimisation (GEO) combines SEO, content and Digital PR in a measurable approach to AI visibility. Use the source mix to formulate priorities, then test them against prompts relevant to your own business.
Expert tip: Consider authority beyond your own website
The dominance check clearly shows that in some industries, LLM visibility is concentrated on a few brands. In our experience, a strong reputation and solid off-site presence can be important drivers. A holistic GEO strategy combining SEO/GEO, content and digital PR can strengthen a brand’s digital authority and visibility – and so improve its chances of competing successfully in the LLM search space.
The full picture is in the LLM Visibility Benchmark
This blog post covers the finance and insurance highlights. The full LLM Visibility Benchmark goes considerably deeper:
- How the source mix differs across all 10 industries within the standardised snapshot.
- How source formats change depending on whether users ask for recommendations, comparisons, pricing or guidance.
- The industry scorecards, including visibility, SOV, sentiment and concentration measures.
- The methodology and interpretation limits behind the findings.
- What the observed patterns may imply for content, Digital PR and further AI visibility research.
Download the full LLM Visibility Benchmark here.
Had a look at our benchmark and looking to develop your finance AI visibility strategy? Get in touch with our LLMO and GEO team.
FAQ: AI Visibility for Finance & Insurance
What types of content do AI systems use to answer finance questions?
In our fixed German-market snapshot for August 2026, comparison pages lead recommendations, comparisons and pricing; articles lead criteria/guidance. Product pages and practical guides also appear among the leading formats for selected intents. These are retrieved source types, not necessarily explicit citations.
How visible are finance and insurance brands in ChatGPT and other AI systems?
The finance scorecard reports average visibility of 6.15% for the tracked set and study period. It is not a general visibility rate for every financial brand, a measure of market share or an assessment of product quality.
Which finance brands are most visible in the benchmark?
ING appears at 19% visibility, Trade Republic at 15% and Scalable Capital at 12%. The ranking applies only to the tracked brands, fixed prompts and August 2026 study period.
Does AI visibility for finance matter?
A brand appearing in an AI answer can become part of a customer’s consideration set. The benchmark helps investigate the sources behind those answers, but it does not measure sales uplift, conversion impact or a guaranteed advantage for challenger brands.
What is a GEO finance strategy?
A GEO finance strategy connects SEO, useful product and advisory content, relevant third-party coverage and Digital PR to work on visibility in AI-generated answers. The observed source mix provides a reason to examine comparisons, articles and product information together, then validate priorities through repeated testing.
