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.
2,205 of 2,219
its rank by overall score
Closing balance

Named in

0of 60, its tower is dark

by 0 of 4 assistants

Statement note

What this means for SafeChange Co.

AI assistants do not name this fraud and identity company in any of their 60 answers, while the typical company in its sector is named in 1.7% of answers. Its overall score is 16 out of 100, ranking 2,205th of 2,219 companies. The company's website passes 1 of 14 technical checks for how ready it is for AI tools.

We asked 15 unbranded questions about its market, so no question mentioned SafeChange Co. by name. AI assistants named it in none of the 60 answers. Counting only how often it is named, it ranks 334th 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
16Overall score
  • Visibility0
  • Citations0
  • Sentiment50
  • Site quality33
  • AI readiness7
Summary of account
Named by AI in
0.0%
0 of 60 answers
Share of mentions
0.0%
of the mentions of it and its 5 peers
AI readiness
1/14
technical checks passed. Typical for its sector: 4
Rank in its sector
570 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

SafeChange Co. came up on 0 of 15 questions. Open any window to read that answer.

  1. 01Why might dynamic CVV card technology not be stopping fraudulent transactions?0/4
  2. 02What features should U.S. card issuers look for in a card fraud prevention solution?0/4
  3. 03Chip cards vs dynamic number cards—what's better for reducing card fraud?0/4
  4. 04What are the alternatives to dynamic card data technology for fighting card fraud?0/4
  5. 05Is it worth investing in single-use card number technology for credit unions?0/4
  6. 06What non-dynamic solutions are available to address card fraud at the issuer level?0/4
  7. 07How can payment card issuers use dynamic card numbers to reduce fraud risk?0/4
  8. 08In what scenarios are rotating card numbers most effective for payment security?0/4
  9. 09Which card fraud risks are most effectively managed by single-use transaction data systems?0/4
  10. 10What factors affect the cost of implementing dynamic card number solutions for banks?0/4
  11. 11What challenges do banks face when rolling out single-use card number systems?0/4
  12. 12Do dynamic CVV cards provide enough ROI for smaller banks?0/4
  13. 13What are the best card fraud prevention methods for U.S. banks?0/4
  14. 14How much does dynamic card number processing software typically cost for midsize issuers?0/4
  15. 15How does dynamic card number technology compare to static card information for preventing fraud?0/4
Total namedOpenAI: 0/15Claude: 0/15Perplexity: 0/15Gemini: 0/15All assistants: 0/60
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

The company is not named in this answer

OpenAIdid not name the company

“Is it worth investing in single-use card number technology for credit unions?”

Short answer: yes — but with important caveats. Single‑use (one‑time) card numbers and virtual card/tokenization are a strong fraud‑mitigation and member‑experience tool for many credit unions, especially for business/commercial programs and high‑fraud portfolios, but the business case depends on your member mix, fraud exposure, core processing capabilities, and vendor/partner costs. Below I summarize the evidence, who benefits most, key risks/costs, and recommended next steps (with sources).

Why it’s attractive (benefits)

Strong fraud reduction / security: single‑use virtual card numbers r …

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. Fiserv (SecureNow)
    66.7%
  2. Thales Group (Gemalto Payment Solutions)
    33.3%
  3. SafeChange Co.
    0.0%
  4. Idemia
    0.0%
  5. Giesecke+Devrient (G+D)
    0.0%
  6. IDEX Biometrics
    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
    corporate.visa.com
    8
  2. 02
    mastercard.com
    7
  3. 03
    corpay.com
    7
  4. 04
    wallester.com
    7
  5. 05
    aciworldwide.com
    6
  6. 06
    securetechalliance.org
    6
  7. 07
    usbank.com
    5
  8. 08
    chargebacks911.com
    5
  9. 09
    zeta.tech
    5
  10. 10
    image-ppubs.uspto.gov
    5

SafeChange Co. 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
0/15
Gemini
0/15
OpenAI
0/15
Perplexity
0/15
Market comparisonSection 06

How SafeChange Co. 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
0

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

Citations
0

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

Sentiment
50

How positively AI describes the company

Site quality
33

How well built the website's own pages are

AI readiness
7

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
16
Ranks 570 of 574. Typical company: 29
Named in answers
0.0%
Ranks 334 of 574. Typical company: 1.7%
AI readiness
7
Ranks 533 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
16
Ranks 170 of 171. Typical company: 30
Named in answers
0.0%
Ranks 103 of 171. Typical company: 1.7%
AI readiness
7
Ranks 161 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. SafeChange Co. 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
0 of 100
  • robots.txtNot met

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

    study: 86.1%
  • SitemapNot met

    Serve an XML sitemap and reference it 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
0 of 100
  • AI bot rulesNot 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 0 of 100 · 97% of checks could run · checked on October 9, 2026 · safechange.co · 2,200 companies scanned

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

AI readinessSection 10

The checks behind the AI readiness score

1 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

  • Not metrobots.txt publishedstudy: 85.1%
  • Not metXML 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%
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