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
September 23 to 25, 2026
when the AI assistants were asked
Account no.
693 of 2,219
its rank by overall score
Closing balance

Named in

4of 60 answers

by 3 of 4 assistants

Statement note

What this means for Fair Isaac

AI assistants name this credit bureau or scoring company in 6.7% of answers, which is about twice as often as the typical company in its sector. Its overall score is 34 out of 100, ranking 693rd of 2,219 in the study. The website passes 3 out of 14 technical checks for AI tool readiness.

We asked 15 unbranded questions about its market, so no question mentioned Fair Isaac by name. AI assistants named it in 4 of the 60 answers. Counting only how often it is named, it ranks 32nd of 93 in its sector. The typical lending and credit company is named in 3.3% of answers.

Overall score
34Overall score
  • Visibility13
  • Citations1
  • Sentiment67
  • Site quality87
  • AI readiness21
Summary of account
Named by AI in
6.7%
4 of 60 answers
Share of mentions
30.8%
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
34 of 93
by overall score, where the typical lending and credit company scores 31

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Line itemsSection 01

Every question, and where the company came up

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

  1. 01Is investing in advanced decision intelligence software worth it for mid-sized businesses?0/4
  2. 02What is the difference between decision intelligence platforms and predictive analytics tools?0/4
  3. 03Why might an analytics platform return inaccurate customer insights?0/4
  4. 04What are some good alternatives to enterprise-level decision intelligence platforms for small businesses?0/4
  5. 05Emerging trends in applied intelligence for customer engagement across industries?0/4
  6. 06How can decision intelligence platforms help improve customer retention for financial services?0/4
  7. 07How much does it typically cost to deploy an end-to-end decision intelligence solution for a medium-sized business?0/4
  8. 08How do analytics software and traditional business intelligence tools compare?0/4
  9. 09Best analytics approaches for personalizing retail customer experiences?0/4
  10. 10Open source options for advanced analytics and automated decision-making software?0/4
  11. 11What are the top analytics and decision intelligence platforms for improving customer understanding?1/4
  12. 12Common challenges when integrating decision intelligence software with existing business systems?0/4
  13. 13Best decision intelligence solutions for optimizing business processes in large organizations?3/4
  14. 14Should organizations prioritize analytics solutions with real-time decisioning capabilities?0/4
  15. 15What factors influence the pricing of enterprise analytics and decisioning software in the US?0/4
Total namedOpenAI: 2/15Claude: 0/15Perplexity: 1/15Gemini: 1/15All assistants: 4/60
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

“Best decision intelligence solutions for optimizing business processes in large organizations?”

For large organizations, the strongest decision intelligence solutions are typically Pyramid Analytics, Aera Technology, SAS Intelligent Decisioning, FICO Platform, Quantexa, and Palantir Foundry + AIP, because they combine governance, orchestration, AI, and enterprise-scale decisioning rather than just dashboards or reporting.

A practical way to choose is by the decision problem you need to optimize:

Solution · Best fit · Why it stands out

Pyramid Analytics · Large enterprises needing self-service analytics plus governance …

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
    SAS
    5
  2. 02
    FICO
    3

Share of mentions

CompanyShare
  1. SAS
    61.5%
  2. Fair Isaac
    30.8%
  3. Oracle Analytics Cloud
    7.7%
  4. IBM Decision Optimization (IBM Cloud Pak for Data)
    0.0%
  5. Experian Decision Analytics
    0.0%
  6. SAS Decision Manager
    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
    domo.com
    21
  2. 02
    ibm.com
    17
  3. 03
    thoughtspot.com
    8
  4. 04
    getdot.ai
    8
  5. 05
    linkurious.com
    8
  6. 06
    cxtoday.com
    7
  7. 07
    dataiku.com
    7
  8. 08
    gartner.com
    7
  9. 09
    sas.com
    7
  10. 10
    teradata.com
    6

Fair Isaac is in the study as a credit bureau or scoring company.

By assistantSection 05

Which AI assistants named the company

Answers naming it, out of answers received

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

How Fair Isaac 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
13

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
67

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 lending and credit companies

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

Overall score
34
Ranks 34 of 93. Typical company: 31
Named in answers
6.7%
Ranks 32 of 93. Typical company: 3.3%
AI readiness
21
Ranks 52 of 93. Typical company: 29
Against its categorySection 08

Compared with credit bureaus and scoring companies

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

Overall score
34
Ranks 10 of 13. Typical company: 37
Named in answers
6.7%
Ranks 10 of 13. Typical company: 16.7%
AI readiness
21
Ranks 5 of 13. Typical company: 21
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. Fair Isaac 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
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 25 of 100 · 97% of checks could run · checked on October 5, 2026 · www.fico.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

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.

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ShareSection 12

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Overall score 34 of 100 badge for Fair Isaac

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