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.

Back to the study
AI visibility statementFinance AI Visibility Study 2026Issued October 14, 2026
Account holder
Statement period
October 9 to 14, 2026
when the AI assistants were asked
Account no.
931 of 2,219
its rank by overall score
Closing balance

Named in

2of 60 answers

by 2 of 4 assistants

Statement note

What this means for Hawk

AI assistants rarely name this fraud and identity company, mentioning it in 2 of 60 answers. In the same answers, they also mention Feedzai and ComplyAdvantage. Its overall score is 29 out of 100, ranking 931st of 2,219 companies. The website passes 3 of 14 technical checks for readiness with AI tools.

We asked 15 unbranded questions about its market, so no question mentioned Hawk 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
29Overall score
  • Visibility5
  • Citations1
  • Sentiment50
  • Site quality87
  • AI readiness21
Summary of account
Named by AI in
3.3%
2 of 60 answers
Share of mentions
11.1%
of the mentions of it and its 5 peers
AI readiness
3/14
technical checks passed. Typical for its sector: 4
Rank in its sector
270 of 574
by overall score, where the typical financial technology and data company scores 29

Get the full report on Hawk as a PDF

Enter your work email to download it.

We’ll use your email to follow up about this report. Privacy policy

Line itemsSection 01

Every question, and where the company came up

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

  1. 01How do fintech companies use AI for ongoing risk management and compliance monitoring?0/4
  2. 02Alternatives to integrated AML and fraud prevention solutions for banks handling compliance in-house?0/4
  3. 03Manual fraud surveillance vs. automated AML platforms—what are the key differences for regulated entities?0/4
  4. 04What are the most effective AI-powered platforms for anti-money laundering and fraud detection in banking?2/4
  5. 05What non-AI methods are financial institutions using to manage their anti-fraud programs?0/4
  6. 06What steps can be taken if a fraud detection platform is missing suspicious activity in real-time?0/4
  7. 07Which types of platforms offer alternatives to traditional risk management tools in the financial sector?0/4
  8. 08How does rule-based transaction monitoring compare to AI-driven anti-financial crime technology?0/4
  9. 09Best practices for integrating anti-money laundering technology into existing banking systems?0/4
  10. 10How to fix high false positive rates in automated transaction monitoring systems?0/4
  11. 11What factors influence the pricing of AI-powered AML and fraud detection platforms for financial firms?0/4
  12. 12Best solutions to increase risk detection and reduce false positives for financial institutions?0/4
  13. 13Is investing in an AI-powered AML and fraud management solution worthwhile for mid-sized payment companies?0/4
  14. 14What should banks look for when choosing an AI solution for financial crime prevention?0/4
  15. 15Are there cost-efficient options for small financial institutions looking for compliance technology?0/4
Total namedOpenAI: 0/15Claude: 1/15Perplexity: 0/15Gemini: 1/15All 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 Claude actually said

An answer that names the company

Claudenamed the company

“What are the most effective AI-powered platforms for anti-money laundering and fraud detection in banking?”

… Napier AI provides an intelligent AML software platform that uses AI tools for transaction monitoring alongside machine learning for banking, payments, and wealth and asset management. HAWK:AI developed AML software built on machine learning and AI technology that delivers payment screening, transaction monitoring, customer screening and risk rating solutions to retail banking and payment companies.

The choice of platform depends on your institution's specific needs, size, and complexity of operations.

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
    6
  2. 02
    ComplyAdvantage
    4
  3. 03
    Unit21
    2
  4. 04
    Featurespace
    1

Share of mentions

CompanyShare
  1. Feedzai
    38.9%
  2. ComplyAdvantage
    27.8%
  3. Unit21
    16.7%
  4. Hawk
    11.1%
  5. Featurespace
    5.6%
  6. Jumio
    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
    10
  2. 02
    symphonyai.com
    8
  3. 03
    fraudio.com
    7
  4. 04
    oracle.com
    6
  5. 05
    financialcrimeacademy.org
    5
  6. 06
    fintech.global
    5
  7. 07
    tookitaki.com
    5
  8. 08
    fraud.net
    3
  9. 09
    riskwatch.com
    3
  10. 10
    fatf-gafi.org
    3

Hawk 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

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

How Hawk 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
5

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

Citations
1

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

Sentiment
50

How positively AI describes the company

Site quality
87

How well built the website's own pages are

AI readiness
21

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
29
Ranks 270 of 574. Typical company: 29
Named in answers
3.3%
Ranks 223 of 574. Typical company: 1.7%
AI readiness
21
Ranks 401 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
29
Ranks 90 of 171. Typical company: 30
Named in answers
3.3%
Ranks 71 of 171. Typical company: 1.7%
AI readiness
21
Ranks 133 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. Hawk 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
80 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 DiscoveryPassed

    SVCB or HTTPS records under _agents that advertise agent endpoints.

    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 28 of 100 · 97% of checks could run · checked on October 9, 2026 · hawk.ai · 2,200 companies scanned

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

AI readinessSection 10

The checks behind the AI readiness score

3 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%
  • Not metHTTP 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%
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

  • GPTBotpartly
  • ClaudeBotpartly
  • Anthropic-AIpartly
  • Google-Extendedpartly
  • Applebot-Extendedpartly
  • Meta-ExternalAgentpartly
  • FacebookBotpartly
  • Bytespiderpartly
  • 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.

Take it with you

Get the full report on Hawk as a PDF

Every number on this page in a PDF you can keep and share with your team. Enter your work email to download it.

We’ll use your email to follow up about this report. Privacy policy

Next step

Want AI assistants to name your company?

Book a 30-minute demo to see Rezolve Ai in action and what it can do for your business.

Request a demo