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

DataVisor

datavisor.com

Fraud, identity and complianceFinancial technology and data

Money20/20 attendee

Statement period
October 9, 2026
when the AI assistants were asked
Account no.
410 of 2,219
its rank by overall score
Place in its category
49th of 171 fraud and identity companies
Closing balance

Named in

5of 60 answers

by 3 of 4 assistants

Statement note

What this means for DataVisor

AI assistants name DataVisor in 8.3% of answers, which is more frequent than the typical company in its sector at 1.7%. Its overall score is 38 out of 100, ranking 410th of 2,219 companies. The website passes 4 of 14 technical checks for readiness with AI tools.

We asked 15 unbranded questions about its market, so no question mentioned DataVisor by name. AI assistants named it in 5 of the 60 answers. Counting only how often it is named, it ranks 130th of 574 in its sector. The typical financial technology and data company is named in 1.7% of answers.

Overall score
38Overall score
  • Visibility12
  • Citations2
  • Sentiment100
  • Site quality73
  • AI readiness29
Summary of account
Named by AI in
8.3%
5 of 60 answers
Share of mentions
20.0%
of the mentions of it and its 5 peers
AI readiness
4/14
technical checks passed. Typical for its sector: 4
Rank in its sector
127 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

DataVisor came up on 2 of 15 questions. Open any window to read that answer.

  1. 01What are common enterprise use cases for real-time financial crime prevention platforms?0/4
  2. 02How do non-AI fraud prevention strategies compare to AI-powered approaches for banks?0/4
  3. 03What should enterprises look for when choosing an AI-powered risk and fraud management solution?0/4
  4. 04What fraud prevention tools do US enterprises use to detect application and onboarding fraud?0/4
  5. 05How can organizations improve accuracy in their AI fraud detection systems?0/4
  6. 06What factors affect the cost of implementing an enterprise-level AI-based fraud prevention solution?0/4
  7. 07What are some alternatives to using AI-driven software for managing fraud and risk in financial institutions?0/4
  8. 08What are the best AI-powered platforms for enterprise financial crime prevention?3/4
  9. 09Are there subscription pricing models available for cloud-native fraud detection platforms in the US?0/4
  10. 10Which real-time fraud detection solutions are recommended for large banks?2/4
  11. 11AI-powered risk management platforms vs legacy fraud solutions—what are the main differences?0/4
  12. 12Why might an AI-based fraud platform flag too many false positives, and how can this be reduced?0/4
  13. 13How does machine learning-based fraud prevention differ from rules-based systems?0/4
  14. 14Can AI-powered fraud platforms help prevent account takeover and payment fraud simultaneously?0/4
  15. 15Is investing in an AI-native fraud detection platform worth it for mid-sized financial institutions?0/4
Total namedOpenAI: 0/15Claude: 2/15Perplexity: 1/15Gemini: 2/15All assistants: 5/60
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 Perplexity actually said

An answer that names the company

Perplexitynamed the company

“What are the best AI-powered platforms for enterprise financial crime prevention?”

The strongest enterprise options are NICE Actimize, SAS AML, Oracle Financial Services, Quantexa, Feedzai, SymphonyAI NetReveal, and DataVisor. The best choice depends on whether the priority is broad AML compliance, real-time fraud prevention, entity/network intelligence, or rapid deployment.

Platform · Best fit · Key strengths

NICE Actimize · Large banks and global financial institutions · Mature AML, fraud, sanctions, case-management, and investigation capabilities; strong for complex, regulated environments

SAS Anti-Money Laundering · Banks wanting adva …

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
    Feedzai
    13
  2. 02
    Sift
    5
  3. 03
    Kount
    3
  4. 04
    Featurespace
    2

Share of mentions

CompanyShare
  1. Feedzai
    36.0%
  2. DataVisor
    20.0%
  3. Sift
    20.0%
  4. Featurespace
    12.0%
  5. Kount
    12.0%
  6. Fraud.net
    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
    fraud.net
    13
  2. 02
    corporate.visa.com
    11
  3. 03
    fluxforce.ai
    9
  4. 04
    datavisor.com (its own website)
    9
  5. 05
    emburse.com
    8
  6. 06
    sardine.ai
    8
  7. 07
    fraudio.com
    8
  8. 08
    microblink.com
    7
  9. 09
    sas.com
    7
  10. 10
    sourceforge.net
    6

DataVisor 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

Gemini
2/15
Claude
2/15
Perplexity
1/15
OpenAI
0/15
Market comparisonSection 06

How DataVisor 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
12

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

Citations
2

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

Sentiment
100

How positively AI describes the company

Site quality
73

How well built the website's own pages are

AI readiness
29

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
38
Ranks 127 of 574. Typical company: 29
Named in answers
8.3%
Ranks 130 of 574. Typical company: 1.7%
AI readiness
29
Ranks 224 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
38
Ranks 45 of 171. Typical company: 30
Named in answers
8.3%
Ranks 35 of 171. Typical company: 1.7%
AI readiness
29
Ranks 88 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 0: Not yet agent-ready. Agents struggle to find basic information about the site.

To reach level 1:
  • 2 of robots.txt, Sitemap, Link headers
Can an AI agent check out here?

Not by itself. DataVisor 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.txtNot present
  • WebMCPCould not check
Show every check, for your technical team
Discoverability
36 of 100
  • robots.txtNot met

    Publish a valid robots.txt with crawl rules and a Sitemap line.

    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
0 of 100
  • Markdown negotiationNot met

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

    study: 6.2%
  • llms.txtNot met

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

    study: 31.0%
Bot access control
32 of 100
  • AI bot rulesPartly met

    Add explicit robots.txt groups for the main AI crawlers.

    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 15 of 100 · 97% of checks could run · checked on October 9, 2026 · www.datavisor.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

4 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%
  • Not metllms.txt publishedstudy: 28.1%
  • PassedToken budget (page weight)study: 97.3%

Bot Access Control

  • Not metExplicit 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%
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ShareSection 11

Share this result

Overall score 38 of 100 badge for DataVisor

A badge of this result that you can show on your own website. Copy the code below.

<a href="https://aeo-audit.rezolve.com/us/finance/datavisor.com"><img src="https://aeo-audit.rezolve.com/finance/badges/datavisor.com.svg" alt="Overall score 38 of 100, Finance AI Visibility Study 2026" width="280" height="64"></a>
End of statement
All 2,219 companiesHow we measured this
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