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

Affirm

affirm.comAFRM

Buy now, pay laterCards and payments

Money20/20 attendee

Also in the US AI Visibility Study

Statement period
September 23 and 24, 2026
when the AI assistants were asked
Account no.
91 of 2,219
its rank by overall score
Place in its category
1st of 15 buy now, pay later providers
Closing balance

Named in

28of 60 answers

by 4 of 4 assistants

Statement note

What this means for Affirm

AI assistants name Affirm in 46.7% of answers, far more often than the typical company in its sector. Its overall score is 43 out of 100, ranking 91st of 2,219 companies. The Affirm website passes 3 out of 14 technical checks for how ready it is for AI tools.

We asked 15 unbranded questions about its market, so no question mentioned Affirm by name. AI assistants named it in 28 of the 60 answers. Counting only how often it is named, it ranks 7th of 375 in its sector. The typical cards and payments company is named in 0.0% of answers.

Overall score
43Overall score
  • Visibility38
  • Citations1
  • Sentiment65
  • Site quality93
  • AI readiness21
Summary of account
Named by AI in
46.7%
28 of 60 answers
Share of mentions
26.7%
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
19 of 375
by overall score, where the typical cards and payments company scores 27

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Line itemsSection 01

Every question, and where the company came up

Affirm came up on 13 of 15 questions. Open any window to read that answer.

  1. 01Are buy now, pay later services a good idea for building credit history?2/4
  2. 02How do traditional credit cards compare to split-payment services for online shopping?1/4
  3. 03What should I do if I miss a payment on a buy now, pay later plan?1/4
  4. 04Do buy now, pay later services in the US usually have hidden fees?3/4
  5. 05Is using a no-fee installment plan worth it for large online purchases?0/4
  6. 06Are layaway programs still available as an option instead of pay-over-time?2/4
  7. 07Are buy now, pay later services accepted at most major online retailers in the US?4/4
  8. 08How are the true costs of pay-in-4 installment plans calculated?1/4
  9. 09How do pay-over-time payment plans work for US shoppers?2/4
  10. 10What are the best buy now, pay later options for online shopping in the US?4/4
  11. 11What are some alternatives to buy now, pay later for spreading out online payments?2/4
  12. 12Pay-in-4 versus longer-term installment plans—what’s the difference?2/4
  13. 13Can you use pay-over-time options for travel or vacation bookings?3/4
  14. 14Why was my application for a pay-over-time service declined at checkout?1/4
  15. 15How do interest-free buy now, pay later options compare to those with interest charges?0/4
Total namedOpenAI: 7/15Claude: 7/15Perplexity: 6/15Gemini: 8/15All assistants: 28/60
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

“What are the best buy now, pay later options for online shopping in the US?”

For online shopping in the US, the strongest BNPL options are usually PayPal Pay in 4, Affirm, Klarna, Afterpay, and Zip. CNBC’s September 2026 roundup names PayPal Pay in 4 as best for online shopping, while Affirm is best for widespread availability, Klarna for larger purchases, Afterpay for smaller purchases, and Zip for users with weaker credit profiles.

A practical way to choose among them:

Option · Best for · Notable points

PayPal Pay in 4 · Online shopping · CNBC ranks it best for online shopping; PayPal also says many online …

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
    Afterpay
    28
  2. 02
    Klarna
    25
  3. 03
    Sezzle
    11

Share of mentions

CompanyShare
  1. Affirm
    26.7%
  2. Klarna
    22.9%
  3. Afterpay
    21.9%
  4. PayPal Pay in 4
    20.0%
  5. Sezzle
    8.6%
  6. Zip (formerly Quadpay)
    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
    chase.com
    28
  2. 02
    paypal.com
    26
  3. 03
    klarna.com
    16
  4. 04
    cnbc.com
    16
  5. 05
    reddit.com
    16
  6. 06
    experian.com
    15
  7. 07
    consumerfinance.gov
    14
  8. 08
    justdial.com
    12
  9. 09
    finance.yahoo.com
    11
  10. 10
    miamiherald.com
    11

Affirm is in the study as a buy now, pay later provider.

By assistantSection 05

Which AI assistants named the company

Answers naming it, out of answers received

Gemini
8/15
Claude
7/15
OpenAI
7/15
Perplexity
6/15
Market comparisonSection 06

How Affirm 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
38

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
65

How positively AI describes the company

Site quality
93

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 cards and payments companies

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

Overall score
43
Ranks 19 of 375. Typical company: 27
Named in answers
46.7%
Ranks 7 of 375. Typical company: 0.0%
AI readiness
21
Ranks 216 of 375. Typical company: 29
Against its categorySection 08

Compared with buy now, pay later providers

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

Overall score
43
Ranks 1 of 15. Typical company: 37
Named in answers
46.7%
Ranks 1 of 15. Typical company: 16.7%
AI readiness
21
Ranks 9 of 15. 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?

We could not tell. The website turned away our requests for the files an agent would read. An agent arriving by itself is turned away the same way.

Agent payment protocols
  • x402Not present
  • MPPNot present
  • AP2Not present
  • ACPCould not check
  • UCPCould not check
Interfaces for AI agents
  • MCP Server CardCould not check
  • A2A Agent CardCould not check
  • Agent SkillsCould not check
  • API CatalogCould not check
  • 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 AuthCould not check

    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 CatalogCould not check

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

    study: 1.3%
  • OAuth discoveryCould not check

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

    study: 6.3%
  • OAuth Protected ResourceCould not check

    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 CardCould not check

    Offer an MCP server and publish its server card under /.well-known.

    study: 0.8%
  • A2A Agent CardCould not check

    Publish an agent card at /.well-known/agent-card.json.

    study: 0.2%
  • Agent SkillsCould not check

    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 manifestCould not check

    Publish an ai-catalog.json listing every agent interface.

    study: 0.2%

Score 37 of 100 · 65% of checks could run · checked on October 5, 2026 · www.affirm.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

Share this result

Overall score 43 of 100 badge for Affirm

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/affirm.com"><img src="https://aeo-audit.rezolve.com/finance/badges/affirm.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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