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

Verafin

verafin.com

Fraud, identity and complianceFinancial technology and data

Money20/20 attendee

Statement period
October 9 to 14, 2026
when the AI assistants were asked
Account no.
104 of 2,219
its rank by overall score
Place in its category
15th of 171 fraud and identity companies
Closing balance

Named in

12of 60 answers

by 4 of 4 assistants

Statement note

What this means for Verafin

AI assistants name Verafin in 20% of their answers, well above the 1.7% for the typical company in its sector. Its overall score is 43 out of 100, ranking 104th of 2,219 companies. Its website passes 6 out of 14 technical checks for readiness with AI tools.

We asked 15 unbranded questions about its market, so no question mentioned Verafin by name. AI assistants named it in 12 of the 60 answers. Counting only how often it is named, it ranks 46th of 574 in its sector. The typical financial technology and data company is named in 1.7% of answers.

Overall score
43Overall score
  • Visibility24
  • Citations2
  • Sentiment90
  • Site quality80
  • AI readiness43
Summary of account
Named by AI in
20.0%
12 of 60 answers
Share of mentions
35.3%
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
34 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

Verafin came up on 7 of 15 questions. Open any window to read that answer.

  1. 01How can a bank reduce false positives in transaction monitoring for financial crime?0/4
  2. 02Best practices for using AI-driven platforms in anti-fraud operations0/4
  3. 03Top anti-money laundering solutions for credit unions in the US4/4
  4. 04What are the best financial crime management platforms for midsize banks?3/4
  5. 05Which financial crime detection technologies are most effective for regional banks?1/4
  6. 06What factors influence the cost of advanced AML and fraud management platforms?0/4
  7. 07Average annual pricing for enterprise-level financial crime detection software in the US1/4
  8. 08How do automated sanctions screening tools compare to manual processes?0/4
  9. 09How do information sharing features improve financial crime prevention in compliance software?1/4
  10. 10What options exist besides consortium-based financial crime management platforms?1/4
  11. 11What should I do if my financial crime software flags too many legitimate transactions?0/4
  12. 12Do integrated fraud and AML technologies offer better value than standalone solutions?0/4
  13. 13Alternatives to machine-learning-based transaction monitoring for financial institutions0/4
  14. 14Fraud detection platforms vs. AML compliance systems—what’s the difference?1/4
  15. 15Is investing in an all-in-one financial crime management system worth it for small financial institutions?0/4
Total namedOpenAI: 2/15Claude: 2/15Perplexity: 3/15Gemini: 5/15All assistants: 12/60
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

“Top anti-money laundering solutions for credit unions in the US”

The strongest AML options for US credit unions are Verafin, Abrigo BAM+, DataVisor, Alessa, NICE Actimize AML Essentials, and Flagright. The best choice depends on institution size, core integrations, alert volume, fraud coverage, and whether the credit union needs a fully managed compliance workflow.

Solution · Best fit · Notable capabilities

Nasdaq Verafin · Mid-size and larger credit unions · AML, fraud detection, case management, SAR/CTR workflows, and network-based intelligence; frequently positioned as a leading North American credit-union platform. …

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
    Nasdaq Verafin
    5

Share of mentions

CompanyShare
  1. Actimize (Nice Actimize)
    38.2%
  2. Verafin
    35.3%
  3. Oracle Financial Services Financial Crime and Compliance Management
    14.7%
  4. Fiserv Financial Crime Risk Management
    11.8%
  5. FIS (Fidelity National Information Services) Financial Crime Risk Management
    0.0%
  6. ACI Worldwide Fraud Management
    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
    flagright.com
    11
  2. 02
    datavisor.com
    10
  3. 03
    financialcrimeacademy.org
    8
  4. 04
    fraudio.com
    7
  5. 05
    fintech.global
    6
  6. 06
    abrigo.com
    6
  7. 07
    verafin.com (its own website)
    6
  8. 08
    tookitaki.com
    5
  9. 09
    sigma360.com
    4
  10. 10
    sas.com
    4

Verafin 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
5/15
Perplexity
3/15
Claude
2/15
OpenAI
2/15
Market comparisonSection 06

How Verafin 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
24

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
90

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
43
Ranks 34 of 574. Typical company: 29
Named in answers
20.0%
Ranks 46 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
43
Ranks 12 of 171. Typical company: 30
Named in answers
20.0%
Ranks 7 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. Verafin 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
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
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
18 of 100
  • API CatalogNot met

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

    study: 1.3%
  • OAuth discoveryPartly met

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

    study: 6.3%
  • OAuth Protected ResourcePassed

    Metadata telling agents which authorization server to use.

    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 31 of 100 · 97% of checks could run · checked on October 9, 2026 · verafin.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%
  • 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%
  • PassedOAuth Authorization Server discoverystudy: 5.2%
  • PassedOAuth 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 no rules for any AI crawler by name, like 86% of the websites whose rules we could read. Every AI crawler follows its general rules.

Training AI

  • GPTBotallowed
  • ClaudeBotallowed
  • Anthropic-AIallowed
  • Google-Extendedallowed
  • Applebot-Extendedallowed
  • Meta-ExternalAgentallowed
  • FacebookBotallowed
  • Bytespiderallowed
  • CCBotallowed
  • Diffbotallowed
  • Omgilibotallowed

AI search

  • OAI-SearchBotallowed
  • Claude-SearchBotallowed
  • PerplexityBotallowed
  • Applebotallowed
  • Amazonbotallowed
  • YouBotallowed

Answering questions

  • ChatGPT-Userallowed
  • Claude-Userallowed
  • Perplexity-Userallowed
  • DuckAssistBotallowed
  • MistralAI-Userallowed

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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ShareSection 12

Share this result

Overall score 43 of 100 badge for Verafin

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/verafin.com"><img src="https://aeo-audit.rezolve.com/finance/badges/verafin.com.svg" alt="Overall score 43 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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