AI is now the first financial adviser people ask.
Before comparing rates on an aggregator, people describe their situation to an AI assistant and ask which card, loan, broker or platform fits. Those answers are opinionated, sourced and often wrong about your product. CiteRank AI measures what the 8 major engines say and where the errors are.
Vertical benches currently run 4 engines; full tracking covers all 8.
Financial comparison happens inside the answer now.
Engines synthesise rates, fees and eligibility from whatever they can read. When your terms are locked in PDFs, the engine quotes someone else's summary of your product — or a competitor's instead.
'Lowest fee broker for a small portfolio' produces a short list that rarely changes week to week.
Old rates, withdrawn offers and superseded fee schedules persist in answers long after you repriced.
Engines invent income, credit or residency criteria when nothing authoritative is machine-readable.
Regulator registration, insurance and custody arrangements are decisive but rarely structured for citation.
Comparison sites publish structured product data, so they are cited and you are paraphrased second-hand.
Marketing needs evidence of what engines said and when, especially when a claim is misattributed to you.
Which products the engines put in front of customers.
A sample of the finance prompt graph replayed across engines, by product line and customer segment.
| Category prompt | ChatGPT | Gemini | Perplexity | Claude | Answer concentration | You cited? |
|---|---|---|---|---|---|---|
| Best low-fee broker for a small portfolio | Provider A, Provider B, Aggregator | Provider A, Provider B, Aggregator | Provider A, Provider B, Aggregator | Provider A, Provider B, Aggregator | 3 of 15 | — |
| Credit card for someone with thin credit history | Provider C, Provider A, Provider D | Provider C, Provider A, Provider D | Provider C, Provider A, Provider D | Provider C, Provider A, Provider D | 4 of 12 | |
| Fastest business loan for a small company | Provider E, Aggregator, Provider B | Provider E, Aggregator, Provider B | Provider E, Aggregator, Provider B | Provider E, Aggregator, Provider B | 3 of 13 | — |
| Safest platform to hold long-term savings | Provider A, Provider F, Provider C | Provider A, Provider F, Provider C | Provider A, Provider F, Provider C | Provider A, Provider F, Provider C | 3 of 10 | — |
Illustrative modelled data — not live client results. Names anonymised.
A consumer lending brand, modelled end to end.
A lender was consistently described with outdated rates and invented eligibility rules, which cost qualified applicants before they ever reached the site. We published product terms as structured data and rebuilt the eligibility content into direct, citable answers.
- FinancialProduct, Offer and Organization schema published for every product line
- Rates, fees and eligibility exposed as current, machine-readable content rather than PDFs
- Regulator registration, custody and insurance details published as citable trust pages
- Segment pages rewritten to answer eligibility and comparison prompts in the first paragraph
- Weekly replays with misstatement flagging for compliance review
Illustrative modelled data — not live client results. Names anonymised.
Product-line visibility and misstatement tracking.
Visibility trend and share of voice, loaded with a fintech-shaped dataset.
AI Visibility — Share of Voice over time
12-week trend, weighted across the tracked LLMs.
Tip: tab into the chart to focus a weekly data point, then use the left and right arrow keys to step through weeks. Press Escape to exit.
AI recommendation share — you vs competitors
Per-engine Share of Voice across the last 30 days of replays. Hover or focus a bar for the breakdown.
Illustrative modelled data — not live client results. Names anonymised.
Questions from finance & fintech teams.
Can you flag when an engine states our terms incorrectly?
Yes. Replays capture the full answer text, so misstated rates, fees or eligibility are flagged with the date and engine for your compliance record.
Do you send customer data to AI engines?
No. Only public product-intent prompts are replayed. No customer, application or account data is involved at any point.
Is the reporting usable as a compliance artefact?
Every replay is timestamped and stores the answer text and cited sources, which gives marketing and compliance a defensible record of what engines said and when.
How many prompts does a finance brand need?
Usually 250 to 750, covering each product line, customer segment and the comparison prompts where aggregators compete.
What moves the needle fastest?
Publishing current terms and eligibility as structured, machine-readable content is consistently the highest-yield change in this vertical.
See what AI tells customers about your products.
Run a free audit and get the product-level breakdown of citations, omissions and misstatements.