Of 60 answers

The most named companies in the study, and S&P 500 financials, with how many of their 60 AI answers named them.

SWIFT47/60TRANSUNION46/60EXPERIAN42/60COINSTAR41/60NAVY FEDERAL CREDIT UNION41/60ZELLE41/60BLOOMBERG L.P.38/60TRADINGVIEW38/60BANCO POPULAR DE PUERTO RICO37/60PAYPAL37/60VENMO37/60CHEXSYSTEMS33/60EQUIFAX33/60MORNINGSTAR32/60S&P GLOBAL31/60ALPACA MARKETS30/60COINBASE30/60PINWHEEL29/60STRIPE29/60XERO29/60ACORNS28/60AFFIRM28/60PAYFAST (PAYGATE)28/60PAYONEER28/60INTERACTIVE BROKERS26/60CHARLES SCHWAB19/60THE HARTFORD9/60MOODY'S21/60CME GROUP19/60ACCENTURE11/60CHUBB10/60MORGAN STANLEY6/60AMERICAN EXPRESS14/60SYNCHRONY FINANCIAL12/60FACTSET20/60NASDAQ19/60ALLSTATE11/60WELLS FARGO18/60PAYCHEX10/60ARES MANAGEMENT3/60
Rezolve Ai
MethodDemo
Back to the study
AI visibility statementFinance AI Visibility Study 2026Issued October 14, 2026
Account holder

Arkose Labs

arkoselabs.com

Fraud, identity and complianceFinancial technology and data

Money20/20 attendee

Statement period
October 9, 2026
when the AI assistants were asked
Account no.
361 of 2,219
its rank by overall score
Place in its category
44th of 171 fraud and identity companies
Closing balance

Named in

2of 60 answers

by 2 of 4 assistants

Statement note

What this means for Arkose Labs

AI assistants rarely name Arkose Labs in their answers, mentioning the company in 2 out of 60 answers. Its overall score is 39 out of 100, ranking 361st of 2,219 overall and 94th of 574 in its sector. The company's website passes 6 of 14 technical checks for AI tool readiness.

We asked 15 unbranded questions about its market, so no question mentioned Arkose Labs by name. AI assistants named it in 2 of the 60 answers. Counting only how often it is named, it ranks 223rd of 574 in its sector. The typical financial technology and data company is named in 1.7% of answers.

Named fewer than three times in total. Read this as a sign that AI assistants rarely bring this company up, not as an exact comparison with its peers.

Overall score
39Overall score
  • Visibility7
  • Citations0
  • Sentiment100
  • Site quality80
  • AI readiness43
Summary of account
Named by AI in
3.3%
2 of 60 answers
Share of mentions
16.7%
of the mentions of it and its 5 peers
AI readiness
6/14
technical checks passed. Typical for its sector: 4
Rank in its sector
94 of 574
by overall score, where the typical financial technology and data company scores 29

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Line itemsSection 01

Every question, and where the company came up

Arkose Labs came up on 1 of 15 questions. Open any window to read that answer.

  1. 01Alternatives to AI-based fraud prevention for controlling bot-driven attacks?0/4
  2. 02Why isn't my current bot management solution catching sophisticated AI agents?0/4
  3. 03Non-AI solutions for online businesses dealing with bot threats and fraud?0/4
  4. 04What are some manual techniques for detecting digital fraud without automated platforms?0/4
  5. 05AI-driven fraud prevention tools vs rule-based monitoring systems—what's the difference?0/4
  6. 06What are the top use cases for enterprise AI fraud detection tools?0/4
  7. 07How does AI-based bot detection differ from traditional CAPTCHA methods?0/4
  8. 08Common pitfalls when deploying AI-driven fraud detection on high-traffic sites?0/4
  9. 09How is pricing typically structured for AI-powered bot management platforms in the US?0/4
  10. 10Is investing in a dedicated bot detection platform worth it for enterprise-level businesses?0/4
  11. 11Does using an AI-based fraud prevention service improve ROI for online platforms?0/4
  12. 12Best ways to detect advanced bots on e-commerce platforms?0/4
  13. 13Are there affordable AI-driven fraud prevention options for mid-sized online businesses?0/4
  14. 14How can online marketplaces protect themselves from automated credential stuffing attacks?0/4
  15. 15What are the most effective AI-powered fraud prevention solutions for large websites?2/4
Total namedOpenAI: 1/15Claude: 0/15Perplexity: 1/15Gemini: 0/15All assistants: 2/60
named the companyanswered without naming itdid not answer

Columns, left to right: OpenAI, Claude, Perplexity, Gemini. Across the study, on 50% of the questions where a company is named, only one AI assistant names it.

Transaction detailSection 02

What OpenAI actually said

An answer that names the company

OpenAInamed the company

“What are the most effective AI-powered fraud prevention solutions for large websites?”

Short answer — for large, high-traffic websites the most widely used and effective AI/ML‑driven fraud-prevention solutions are (in no particular order):

Sift — strong ML decisioning and a large global event network for payments, account abuse, ATO and behavioral risk scoring; good when you need real‑time scoring plus custom workflows and global signal sharing. (sift.com)

Arkose Labs (Arkose Titan / Arkose Bot Manager) — focused on combining detection with adaptive challenge/mitigation (particularly good at stopping credential stuffing, account takeover, fake‑account …

The AI’s answer as we received it, shortened and not checked for accuracy.

PayeesSection 03

Where the mentions went

The companies these answers named, and how the mentions were split between this company and its peers.

Also named

CompanyTimes named
  1. 01
    DataDome
    10
  2. 02
    Kasada
    1

Share of mentions

CompanyShare
  1. DataDome
    41.7%
  2. PerimeterX (Human Security)
    25.0%
  3. Arkose Labs
    16.7%
  4. Kasada
    16.7%
  5. CHEQ
    0.0%
  6. Netacea
    0.0%
ReferrersSection 04

Websites the AI used as sources

How many times each website was listed as a source in these answers

  1. 01
    fraudio.com
    11
  2. 02
    ibm.com
    9
  3. 03
    cequence.ai
    9
  4. 04
    datadome.co
    8
  5. 05
    humansecurity.com
    8
  6. 06
    radware.com
    8
  7. 07
    cside.com
    7
  8. 08
    mastercard.com
    6
  9. 09
    fraud.net
    6
  10. 10
    stripe.com
    6

Arkose Labs is in the study as a fraud and identity company.

By assistantSection 05

Which AI assistants named the company

Answers naming it, out of answers received

Perplexity
1/15
OpenAI
1/15
Gemini
0/15
Claude
0/15
Market comparisonSection 06

How Arkose Labs compares with all 2,219 companies

Every tower is a company in the study, taller for a higher overall score and lit where AI assistants named it. Its own group stays lit. Hover a tower to see it, and click to open it.

What makes up the score

Each part is scored out of 100.

Visibility
7

How often AI assistants name the company, and how that compares with its peers

Citations
0

How often AI uses the company's own website as a source

Sentiment
100

How positively AI describes the company

Site quality
80

How well built the website's own pages are

AI readiness
43

How easily AI tools can read and use the website

Visibility is calculated from the answers on this page. How the score is built.

Against its sectorSection 07

Compared with financial technology and data companies

574 companies in this sector. The gold tick on each bar is the typical company.

Overall score
39
Ranks 94 of 574. Typical company: 29
Named in answers
3.3%
Ranks 223 of 574. Typical company: 1.7%
AI readiness
43
Ranks 29 of 574. Typical company: 29
Against its categorySection 08

Compared with fraud and identity companies

171 companies in this category. The gold tick on each bar is the typical company.

Overall score
39
Ranks 33 of 171. Typical company: 30
Named in answers
3.3%
Ranks 71 of 171. Typical company: 1.7%
AI readiness
43
Ranks 11 of 171. Typical company: 36
AI agentsSection 09

Ready for AI agents

What this website publishes for an AI agent to read, use and pay through, on a scale of six levels.

  1. 0
  2. 1
  3. 2
  4. 3
  5. 4
  6. 5

Level 1: Basic web presence. Two of robots.txt, a sitemap and Link headers are in place.

To reach level 2:
  • Content Signals
Can an AI agent check out here?

Not by itself. Arkose Labs publishes none of the agent payment protocols we look for. An agent would have to click through the website like a person.

Agent payment protocols
  • x402Not present
  • MPPNot present
  • AP2Not present
  • ACPNot present
  • UCPNot present
Interfaces for AI agents
  • MCP Server CardNot present
  • A2A Agent CardNot present
  • Agent SkillsNot present
  • API CatalogNot present
  • llms.txtPublished
  • WebMCPCould not check
Show every check, for your technical team
Discoverability
77 of 100
  • robots.txtPassed

    A valid robots.txt with crawl rules at the site root.

    study: 86.1%
  • SitemapPassed

    An XML sitemap listing the pages, ideally referenced from robots.txt.

    study: 87.9%
  • Link headersNot met

    Send Link headers such as rel="api-catalog" or rel="describedby" on the homepage.

    study: 1.5%
  • DNS for AI DiscoveryNot present

    Advertise agent endpoints with SVCB records under _agents.

    study: 4.3%
Content accessibility
25 of 100
  • Markdown negotiationNot met

    Return a markdown version when asked with Accept: text/markdown, for example at the CDN.

    study: 6.2%
  • llms.txtPartly met

    Publish an llms.txt with a short summary and the key links.

    study: 31.0%
Bot access control
63 of 100
  • AI bot rulesPassed

    Explicit robots.txt rules for AI crawlers such as GPTBot, ClaudeBot and PerplexityBot.

    study: 92.7%
  • Content SignalsNot met

    Add a Content-Signal line for search, ai-input and ai-train to robots.txt.

    study: 2.3%
  • Web Bot AuthNot present

    Only relevant if you run your own agents or crawlers: publish a signing key directory.

    study: 0.3%
APIs, auth and MCP
0 of 100
  • API CatalogNot met

    List public APIs in a linkset at /.well-known/api-catalog.

    study: 1.3%
  • OAuth discoveryNot met

    Publish OpenID Connect or OAuth server metadata under /.well-known.

    study: 6.3%
  • OAuth Protected ResourceNot met

    Serve protected resource metadata at /.well-known/oauth-protected-resource.

    study: 5.3%
  • auth.mdNot met

    Publish an auth.md describing how agents sign in.

    study: 0.2%
  • MCP Server CardNot met

    Offer an MCP server and publish its server card under /.well-known.

    study: 0.8%
  • A2A Agent CardNot met

    Publish an agent card at /.well-known/agent-card.json.

    study: 0.2%
  • Agent SkillsNot met

    Publish a skills index with the main tasks agents can do.

    study: 1.0%
  • WebMCPCould not check

    Register key actions such as search or cart as WebMCP tools.

    study: 0.0%
  • ARD manifestNot met

    Publish an ai-catalog.json listing every agent interface.

    study: 0.2%

Score 37 of 100 · 97% of checks could run · checked on October 9, 2026 · www.arkoselabs.com · 2,200 companies scanned

See how every company did, and how this was measured.

AI readinessSection 10

The checks behind the AI readiness score

6 of 14 passed. This list is for your technical team. The percentage is how many companies in the study pass each check.

These 14 checks ran during the audit and are what the AI readiness part of the overall score counts. The scan above ran later and is stricter on Link headers and Markdown, so a check can pass here and not there.

Show the 14 checks

Discoverability

  • Passedrobots.txt publishedstudy: 85.1%
  • PassedXML sitemapstudy: 79.4%
  • PassedHTTP Link headers (RFC 8288)study: 42.5%

Content Accessibility

  • Not metMarkdown content negotiationstudy: 7.4%
  • Passedllms.txt publishedstudy: 28.1%
  • PassedToken budget (page weight)study: 97.3%

Bot Access Control

  • PassedExplicit AI bot rulesstudy: 11.5%

API / Auth / MCP

  • Not metAPI Catalog (.well-known/api-catalog)study: 1.1%
  • Not metOAuth Authorization Server discoverystudy: 5.2%
  • Not metOAuth Protected Resource discoverystudy: 4.6%
  • Not metMCP Server Cardstudy: 0.2%
  • Not metA2A Agent Cardstudy: 0.0%
  • Not metWebMCP toolsstudy: 0.3%
  • Not metAgent Skills declaredstudy: 0.1%
AI crawlersSection 11

What AI crawlers may read (robots.txt)

This website sets rules for 1 AI crawler by name. All others follow its general rules.

Training AI

  • GPTBotpartly
  • ClaudeBotpartly
  • Anthropic-AIpartly
  • Google-Extendedpartly
  • Applebot-Extendedpartly
  • Meta-ExternalAgentpartly
  • FacebookBotpartly
  • Bytespider*blocked
  • CCBotpartly
  • Diffbotpartly
  • Omgilibotpartly

AI search

  • OAI-SearchBotpartly
  • Claude-SearchBotpartly
  • PerplexityBotpartly
  • Applebotpartly
  • Amazonbotpartly
  • YouBotpartly

Answering questions

  • ChatGPT-Userpartly
  • Claude-Userpartly
  • Perplexity-Userpartly
  • DuckAssistBotpartly
  • MistralAI-Userpartly

Read from the website’s own rules file (robots.txt). An asterisk marks a crawler the file names. The rest follow its general rules. “Partly” means only some pages are blocked.

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End of statement
All 2,219 companiesHow we measured this
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