Which AI Engine Cites Fintech Brands Most? A 23,294-Conversation Benchmark

TL;DR

• Visibility here means the share of tracked conversations where a brand appears at all. Across 23,294 tracked AI conversations, the median fintech brand's visibility swings 2.4x depending on which engine you measure.
• The best-powered example is Toku, a LoudFace client whose tracked prompts are the corpus behind these numbers: 36.19% visibility on Google AI Overviews against 7.92% on Perplexity, a 4.6x gap measured across 625 conversations on its weakest engine.
• Rank order changes by engine. Toku is the 3rd most-visible vendor on Google AI Overviews and the 5th on Perplexity, from the same prompt set on the same days.
• Google AI Overviews describes these brands warmer than ChatGPT does for 9 of the 10 brands, by 6 to 13 points on a 100-point sentiment score.

The short answer

There is no such thing as "our AI search visibility." There is only visibility on a named engine. We tracked 10 fintech payroll and payments vendors across 142 buyer prompts and 23,294 AI conversations between 1 May and 30 July 2026. Every vendor scored differently on ChatGPT, Perplexity and Google AI Overviews, and for six of the ten the gap was at least 2x. If your dashboard reports one blended AI visibility number, it is hiding the engine that is actually losing.

We ran the wider version of this question across B2B SaaS in our AI citation benchmark. This one is fintech only, and it measures something the first one did not: how differently each engine treats the same brand.

What we measured, and why this dataset exists

This dataset comes from the daily AI-visibility tracking we run inside Peec AI for a fintech client. That produces a side effect most vendors never look at: a same-day, same-prompt comparison of how three engines treat the same set of brands.

The corpus:

Every prompt is phrased as a real buyer question rather than a keyword. "What is the fastest way to pay global contractors instantly?" "Which EOR providers support stablecoin or crypto payroll?" The topic spread covers EOR, contractor payments, stablecoin payroll, token compensation, treasury movement and regulatory questions like MiCA and the GENIUS Act.

One disclosure up front, because it shapes the citation numbers. Toku is a LoudFace client, and Toku's tracked prompt set is this corpus. That is why toku.com tops all three citation columns with 20,436 citations across the three engines. That lead is a property of whose prompts we tracked. It is not a ranking of who wins fintech, and nobody should read it as one.

The same caveat applies to Toku's visibility levels. Its 36.19% on Google AI Overviews is partly a function of prompts written around Toku's own positioning, so that level cannot be compared against another brand's level. The cross-engine ratio holds anyway, because engine is the only variable that changes between the three columns.

Finding 1: the same brand gets a different answer from every engine

Visibility here means the share of tracked conversations where the brand appeared at all. Same brands, same 142 prompts, same 91 days. Only the engine changes. The last column is the sample size behind each brand's weakest engine, because a ratio is only as trustworthy as the smallest number underneath it.

The same fintech brand gets a different answer from every engine

Visibility is the share of tracked conversations where the brand appeared at all. Same 10 vendors, same 142 prompts, same 91 days. Only the engine changes.

ChatGPTGoogle AI OverviewsPerplexity

Scroll the chart sideways to see it all.

Source: LoudFace, Peec AI tracking, 1 May to 30 July 2026. n = conversations behind each vendor’s weakest engine. Ratios built on fewer than about 50 conversations are noise, shown in red.

Median spread: 2.4x. Six of the 10 at least double, and only four of those rest on enough conversations to trust.

AllScale's 25.1x looks like the headline of the whole study until you read across to that final column. Four conversations. Velocity Global's 15.8x sits on 48. Ratios built on fewer than about 50 conversations are noise. They are not measurement, and we are not going to pretend otherwise just because they make a better chart.

The confound underneath the whole table is coverage. The widest swings belong to the brands with the thinnest coverage: broad coverage smooths out, thin coverage whips around. But the relationship is loose, and Riseworks breaks it. Computed from raw counts, the narrowest spreads in the set are Riseworks 1.1x, Remote 1.3x, BitPay 1.5x and Deel 1.7x. Riseworks heads that list, and it never clears 2.2% visibility on any engine, so the steadiest brand of the ten is also one of the thinnest-covered. Coverage explains the extremes without explaining every row. So the number worth acting on is the widest spread that survives a decent sample.

That is Toku at 4.6x, with 625 conversations behind its weakest engine. Strong on Google AI Overviews (36.19%, 3rd in the tracked set), thin on Perplexity (7.92%, 5th). One body of content, retrieved two different ways. The first time I saw a gap that size on a brand we work on daily, my assumption was that the tracking had broken. It had not. Google and Perplexity simply read the internet differently, and the difference is worth 28 percentage points.

The leaderboard changes depending on who you ask

Deel and Remote trade the top slot on all three engines. Third place is where the tracked set actually splits, and it goes to a different company on Google than on the other two. Across all 10 vendors, ChatGPT produced the best result for 4 of them, Google AI Overviews for 3 and Perplexity for 3. No engine is systematically generous, and the mechanics of closing a thin one are their own job, which we walk through in how fintech companies get cited in AI search.

Finding 2: engines have systematically different moods

Peec scores sentiment 0 to 100, based on how positively the answer describes the brand. That is Peec's own scoring model rather than a published scale, so the per-brand direction is the defensible claim and the absolute point values are not. We averaged it across all 10 vendors per engine. The mean is unweighted, so AllScale's 4 conversations count for as much as Remote's 3,654.

Google AI Overviews describes these brands more warmly than ChatGPT does

Sentiment gap per brand, Google AI Overviews minus ChatGPT, on Peec’s own 0 to 100 scoring model. Warmer on Google for 9 of the 10 brands. Riseworks is the only reversal.

Scroll the chart sideways to see it all.

Source: LoudFace, Peec AI tracking, 1 May to 30 July 2026. Sentiment is Peec’s own scoring model, not a published scale, so the direction is the defensible claim rather than the point values.

The 9.4-point mean gap holds at the brand level. Google AI Overviews scored warmer than ChatGPT for 9 of the 10 brands, by 6 to 13 points. Deel: 69 on Google, 58 on ChatGPT. Remote: 69 and 59. Bitwage: 67 and 57. Riseworks is the single reversal, and it reverses by one point (67 on ChatGPT against 66 on Google), which is inside the noise of any sentiment score.

Filtering out the thin samples does not soften it. Restricted to the 8 vendors with at least 50 conversations on their weakest engine, Google AI Overviews still runs warmer for 7 of the 8, and Riseworks is again the only reversal.

So ChatGPT is the cold reader of the three. We have no non-fintech control group, so we cannot separate an engine trait from something specific to payroll and payments. It held for 9 of the 10 brands here, and that is as far as this data goes. The practical read: if a buyer tells you an AI described your company flatly, check ChatGPT first, because that is where the coldest framing showed up.

Finding 3: ChatGPT leans on community, Google leans on vendors

ChatGPT cited Reddit 3,713 times in this corpus. Perplexity cited it 91 times. That is a 40.8x gap on the same prompt set across the same 91 days, and it is the widest engine difference anywhere in this dataset. Google AI Overviews lands in between at 1,063, which makes ChatGPT 3.5x heavier on Reddit than Google is.

3,713 against 91 is the figure I keep returning to. Two engines answering identical buyer questions, one treating a forum as a primary source and the other barely touching it.

Domains from the citation report, ranked by ChatGPT citations:

ChatGPT cited Reddit 40.8 times more often than Perplexity did

Citations of reddit.com across the same fintech prompt set. The engines are not reading the same internet.

Scroll the chart sideways to see it all.

Source: LoudFace, Peec AI tracking, 1 May to 30 July 2026. reddit.com citation counts, all tracked fintech prompts.

Reddit's rate on ChatGPT was 2.28 citations per conversation where Reddit was retrieved, meaning ChatGPT does not merely find those threads, it quotes them more than once inside the same answer.

That gap is where a lot of fintech AEO programs get confusing. A team publishes a comparison page, watches Google AI Overviews pick it up, cannot work out why ChatGPT ignores them, and concludes the page is weak. ChatGPT is reading a different corpus, and a meaningful slice of it is a forum thread nobody at your company controls.

One row in that table deserves a second look. eco.com is not one of the 10 tracked vendors, and it still carries 2,723 ChatGPT citations, more than deel.com's 2,352. A large share of the corpus deciding these answers sits outside the 10 tracked vendors entirely.

Two vendor-owned domains run against the pattern most vendors show. riseworks.io takes 4,209 citations on Google AI Overviews against 1,536 on ChatGPT. bitwage.com goes further, climbing from 327 on ChatGPT to 535 on Google to 878 on Perplexity. Google's citation mix leans vendor-owned, so a vendor domain outperforming its own ChatGPT count on Google is that lean showing up on a single row. That covers riseworks.io. It covers bitwage.com only halfway, because bitwage.com peaks on Perplexity at 878, which sits outside anything this finding claims, and we did not inspect why.

Finding 4: you are probably ranked fine and cited rarely

The trap in this data is assuming a low score means a ranking problem.

Average position when cited, across the whole corpus, sits between 1.7 and 6.0. Deel averages 1.7 on ChatGPT. Toku averages 1.9 on Google AI Overviews, where it appeared in 2,704 conversations and a position was recorded in most of them rather than all. Bitwage averages 2.8 on Google AI Overviews. When these brands appear, they appear near the front of the answer.

One limit narrows that claim hard. Position is only observed in conversations where the brand was already cited, so it says nothing about the conversations that skipped the brand entirely. It cannot explain an absence. What the data does rule out is one specific failure mode: these brands are not being retrieved and then buried at the bottom of the answer.

So the deficit that survives is frequency, not rank.

That tells you where not to spend. If your position when cited is already inside 2.0 and your visibility is under 10%, more polish on the pages that already get cited is the low-yield option, because those pages are doing their job. The open question is why the other 90-plus percent of conversations never reached you, and that question lives in the retrieved set: whether a page of yours, or a third-party page that names you, is in the corpus the engine pulls from at all. Structure work and corpus work are separate budgets, and this dataset can only tell you that the second one is unfinished.

The fintech companies AI cites in this category

The wider field these answers draw on: the 10 tracked vendors, plus the domains that showed up in the retrieved and cited set alongside them.

• Deel and Remote: the incumbent duopoly. Together they take 73.7% of share of answer on ChatGPT, 61.5% on Google AI Overviews and 66.1% on Perplexity. Share of answer is the stricter of the two metrics here. Visibility counts the conversations you appear in at all, while share of answer divides your mentions by every mention in those same answers. The denominator is the 10 tracked vendors. It is not the fintech category, so read these percentages as dominance of a measured set.
• Papaya Global: best represented on ChatGPT (34.63%), thinnest on Google AI Overviews (15.31%).
• Toku: strongest on Google AI Overviews at 36.19% visibility and position 1.9, weakest on Perplexity at 7.92%.
• Rippling, Oyster HR, Multiplier, Remofirst, Playroll, Native Teams, Safeguard Global, Atlas HXM, Workmotion: the broader EOR field, all present in the cited corpus at varying depth.
• Wise, Stripe, Airwallex, Payoneer and Gusto: cited when the question turns to payment mechanics rather than employment. payoneer.com is directly in the corpus with 235 citations (127 on ChatGPT, 79 on Google AI Overviews, 29 on Perplexity).
• Bitwage, Request Finance, Riseworks, AllScale, BitPay, TransFi, Transak, MoonPay, Crossmint: the crypto payroll and settlement layer.
• G2, Capterra, TechnologyAdvice, Geekflare, TechRadar, Forbes, Reuters: the third-party review and editorial corpus. Third-party pages carry real weight on ChatGPT. eco.com sits outside the 10 tracked vendors and still took 2,723 ChatGPT citations there, ahead of deel.com's 2,352.

If your brand is missing from this list, that says something about our prompt set before it says anything about you. All 142 prompts sit inside one fintech payroll and payments project, leaning toward global payroll, contractor payments and stablecoin settlement. Sell into a different question set and you need your own prompt list before any of these numbers apply.

So which engine do you fix first?

Start with what this dataset cannot tell you. It measures visibility. It does not measure revenue, and nothing here says a citation on Perplexity is worth more or less to your pipeline than a citation on ChatGPT. Any ranking of engines by pipeline value would be invented. Three things follow from the data.

Volume cannot be your tiebreak, and not because the engines are equal. Peec runs every tracked prompt against all three engines, so the per-engine conversation counts here (ChatGPT 7,933, Perplexity 7,889, Google AI Overviews 7,472) are balanced by design. That balance is what makes the cross-engine comparison valid. It says nothing about how many of your buyers actually use each engine. If you want to weight by audience size, take that from your own analytics, not from this table.

Your weakest engine is your largest upside. Toku sits at 36.19% on Google AI Overviews and 7.92% on Perplexity with one body of content. Your best column proves the content can be retrieved. Your worst column is where that same content is not being found. A brand at 1% everywhere has a different and harder problem.

Then join it to your own analytics. Per-engine visibility on its own cannot allocate a budget. Pull referral traffic and booked calls by source out of your own analytics, line them up against your per-engine visibility, and spend where a visibility gap sits on top of a source that already converts. If ChatGPT referrals close and ChatGPT is your weakest column, that is the first fix and you no longer need to guess. Set the reporting cadence before you start, because AI citations move on their own timeline and a two-week read will tell you nothing.

What to do with this, by engine

One rule applies to all three: stop reporting a blended number. Report per engine, every time, or you will keep optimizing the panel that was already working. If you would rather hand that job to a team that does it daily, we keep a current list of AEO agencies for fintech companies, ourselves included.

Methodology and limits

What this is. A single-category, first-party sample: 10 vendors, 142 prompts, 23,294 conversations, three engines, 91 days, measured through Peec AI. The cross-engine comparison is the reliable part, because engine is the only variable that changes across the prompt set, the window and the brand list. That also assumes Peec queries all three engines the same way, which we take from the vendor's implementation rather than verifying ourselves.

On the prompt count. 142 tracked prompts carried conversations during the window, and 95 of them are still active as of 30 July 2026. The remaining 47 were archived after they ran, which is why a live prompt list shows the smaller number, and we did not re-read the wording of those 47. Engine balance per prompt was spot-checked and holds, so the cross-engine comparison is unaffected by the difference.

One vendor was excluded after a data check. An eleventh brand, P100, was in the original tracking set. Its Peec entry was bound to the domain panda.tv rather than the company's own, so its counts could not be verified as P100 mentions, and we dropped it and recomputed everything. That is a tracking-configuration artifact on our end and says nothing about the company. It is why the counts here say 10. The exclusion moved the median spread from 2.5x to 2.4x and the best-engine win counts from 4/4/3 to 4/3/3.

Small denominators carry their sample size. Five of the ten vendors have fewer than 200 conversations behind their weakest engine, and three have fewer than 60. Their spreads appear in the table with that count attached so you can discount them yourself.

What this is not. It is not a fintech-wide census. The tracked project sits inside global payroll, contractor payments and stablecoin settlement, so it under-represents lending, banking-as-a-service and insurtech.

Do not read the month-over-month numbers as trend. Our tracked conversation volume grew about 27% between May and July as prompts were added, which dilutes visibility percentages. Month-to-month comparison in this dataset is invalid. Cross-engine comparison inside the same window is fine.

Gemini, Copilot and Grok are not here. They were not active channels in this project during the window. That absence is a real blind spot in the data. We are not implying those engines matter less.

We track this continuously, so the numbers move. Everything above is the 1 May to 30 July 2026 read. If you want your own version of the first table, the fastest route is a 90-minute share-of-answer audit on your own prompt set.