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.
271 of 2,219
its rank by overall score
Closing balance

Named in

3of 60 answers

by 3 of 4 assistants

Statement note

What this means for Derivative Path

AI assistants name this fintech infrastructure company in 5% of answers, which is more often than the typical company in its sector at 1.7%. Its overall score is 40 out of 100, ranking 271st of 2,219 companies and 73rd among 574 sector peers. The company's website passes 5 out of 14 technical checks for AI tool readiness.

We asked 15 unbranded questions about its market, so no question mentioned Derivative Path by name. AI assistants named it in 3 of the 60 answers. Counting only how often it is named, it ranks 192nd of 574 in its sector. The typical financial technology and data company is named in 1.7% of answers.

Overall score
40Overall score
  • Visibility14
  • Citations6
  • Sentiment75
  • Site quality93
  • AI readiness36
Summary of account
Named by AI in
5.0%
3 of 60 answers
Share of mentions
50.0%
of the mentions of it and its 5 peers
AI readiness
5/14
technical checks passed. Typical for its sector: 4
Rank in its sector
73 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

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

  1. 01How do derivatives management platforms differ from traditional risk management software?0/4
  2. 02Interest rate derivatives platform vs. in-house manual hedging systems—what are the pros and cons?0/4
  3. 03What should investment managers consider before adopting a derivatives advisory service?0/4
  4. 04What are good options for institutions seeking advisory services beyond derivatives technology platforms?0/4
  5. 05Is investing in a cloud-native derivatives platform worth it for a regional financial institution?0/4
  6. 06How can community banks use technology to improve commodity risk hedging?0/4
  7. 07Why might a derivatives dashboard not update real-time exposures correctly?0/4
  8. 08Best solutions to streamline interest rate hedging workflows for credit unions?2/4
  9. 09What factors affect the cost of a derivatives management and advisory platform for credit unions?1/4
  10. 10How to resolve common issues when settling FX derivatives through a management platform?0/4
  11. 11Alternatives to using a dedicated derivatives platform for managing financial risk exposures?0/4
  12. 12What are the leading trends in derivatives technology for risk management in US banks?0/4
  13. 13What are effective ways for financial institutions to centrally manage their derivatives exposures?0/4
  14. 14How do pricing models typically work for cloud-based derivatives technology in the US?0/4
  15. 15What are the most important features to look for in a derivatives management platform for banks?0/4
Total namedOpenAI: 1/15Claude: 0/15Perplexity: 1/15Gemini: 1/15All assistants: 3/60
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

“Best solutions to streamline interest rate hedging workflows for credit unions?”

… Vendor platforms that cover ALM, derivatives lifecycle and hedge accounting reduce manual handoffs and Excel risk; examples used in the industry include mainstream ALM/treasury suites (FIS / Fiserv / Wolters Kluwer / QRM) and specialist hedge-accounting/derivatives platforms (Derivative Path, Kyriba, Concourse/Hedgebook for certain sizes). Evaluate total cost, integration with your core, and support for ASC 815/IFRS 9. (fisglobal.com)

4) Automate hedge accounting and testing

Automate designation documents, pe …

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.

Share of mentions

CompanyShare
  1. Derivative Path
    50.0%
  2. Calypso (Adenza)
    50.0%
  3. Kiodex (FIS)
    0.0%
  4. numerix
    0.0%
  5. Kamakura
    0.0%
  6. 360TGTX
    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
    derivativepath.com (its own website)
    16
  2. 02
    bis.org
    8
  3. 03
    newyorkfed.org
    7
  4. 04
    occ.gov
    6
  5. 05
    worldmetrics.org
    5
  6. 06
    catalystcorp.org
    5
  7. 07
    isda.org
    5
  8. 08
    wifitalents.com
    4
  9. 09
    marketintelo.com
    4
  10. 10
    fisglobal.com
    4

Derivative Path is in the study as a fintech infrastructure company.

By assistantSection 05

Which AI assistants named the company

Answers naming it, out of answers received

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

How Derivative Path 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
14

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

Citations
6

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

Sentiment
75

How positively AI describes the company

Site quality
93

How well built the website's own pages are

AI readiness
36

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
40
Ranks 73 of 574. Typical company: 29
Named in answers
5.0%
Ranks 192 of 574. Typical company: 1.7%
AI readiness
36
Ranks 83 of 574. Typical company: 29
Against its categorySection 08

Compared with fintech infrastructure companies

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

Overall score
40
Ranks 30 of 315. Typical company: 28
Named in answers
5.0%
Ranks 85 of 315. Typical company: 1.7%
AI readiness
36
Ranks 40 of 315. Typical company: 29
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. Derivative Path 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
50 of 100
  • Markdown negotiationNot met

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

    study: 6.2%
  • llms.txtPassed

    An llms.txt that tells language models what the site offers.

    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 43 of 100 · 97% of checks could run · checked on October 9, 2026 · derivativepath.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

5 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%
  • 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 8 AI crawlers by name. All others follow its general rules.

Training AI

  • GPTBot*allowed
  • ClaudeBot*allowed
  • Anthropic-AI*allowed
  • Google-Extended*allowed
  • Applebot-Extendedallowed
  • Meta-ExternalAgentallowed
  • FacebookBot*allowed
  • Bytespiderallowed
  • CCBot*allowed
  • Diffbotallowed
  • Omgilibotallowed

AI search

  • OAI-SearchBotallowed
  • Claude-SearchBotallowed
  • PerplexityBot*allowed
  • Applebotallowed
  • Amazonbotallowed
  • YouBotallowed

Answering questions

  • ChatGPT-User*allowed
  • 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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