AI visibility statementFinance AI Visibility Study 2026Issued October 14, 2026

Account holder

Prism Data

prismdata.com

Banking and fintech infrastructure Financial technology and data

Money20/20 attendee

Statement period: October 9 to 14, 2026; when the AI assistants were asked
Account no.: 812 of 2,219; its rank by overall score
Place in its category: 107th of 315 fintech infrastructure companies

Closing balance

Named in

5of 60 answers

by 3 of 4 assistants

Statement note

What this means for Prism Data

AI assistants name this fintech infrastructure company in 8.3% of answers, which is higher than the typical company in its sector at 1.7%. Its overall score is 31 out of 100, ranking 812th of 2,219 overall. The company's website passes 1 out of 14 technical checks for readiness for AI tools.

We asked 15 unbranded questions about its market, so no question mentioned Prism Data by name. AI assistants named it in 5 of the 60 answers. Counting only how often it is named, it ranks 130th of 574 in its sector. The typical financial technology and data company is named in 1.7% of answers.

Overall score

Visibility16
Citations0
Sentiment50
Site quality80
AI readiness21

Summary of account

Named by AI in: 8.3%; 5 of 60 answers
Share of mentions: 35.7%; 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: 237 of 574; by overall score, where the typical financial technology and data company scores 29

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Line items01

Every question, and where the company came up

Prism Data came up on 3 of 15 questions. Open any window to read that answer.

01What should financial institutions consider before choosing a cash flow analytics provider?0/4
02Typical costs involved in implementing a cash flow analytics platform for a regional bank0/4
03How does cash flow underwriting compare to traditional credit scoring methods for risk management?0/4
04Why might a cash flow underwriting platform fail to estimate income accurately?0/4
05How can lenders use transaction data insights to improve loan approvals for applicants with limited credit history?0/4
06Best practices for using cash flow insights to identify and prevent lending fraud0/4
07Which cash flow underwriting platforms are most effective for score-thin files?3/4
08What are common issues when integrating cash flow analytics with existing loan origination systems?0/4
09What are the best cash flow analytics platforms for lenders looking to expand credit approvals?1/4
10Is investing in a cash flow underwriting platform worth it for mid-sized lenders?0/4
11What are some non-traditional data sources financial institutions use for credit decisioning besides cash flow analytics?0/4
12Ways financial institutions use cash flow analytics to lower lending losses0/4
13Alternatives to using cash flow underwriting for expanding loan approvals0/4
14What pricing models are common for cash flow underwriting and analytics services?1/4
15Cash flow analytics vs bank transaction data analysis: which is better for fraud detection in lending?0/4

Total named0/151/151/153/155/60

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 detail02

What Perplexity actually said

An answer that names the company

Perplexitynamed the company

“Which cash flow underwriting platforms are most effective for score-thin files?”

The strongest shortlist for score-thin or no-file applicants is:
Prism Data CashScore — Best-established cash-flow scoring option for a broad consumer-lending population. It converts permissioned bank-transaction data into a three-digit risk score and is designed to complement bureau scores; Equifax reports an average 30% improvement in predictive performance versus traditional scores alone.
Plaid Check / Plaid income and cash-flow data — Best for lenders that want broad account connectivity, income verification, affordability signals, and flexible custom underwriting rathe …

The AI’s answer as we received it, shortened and not checked for accuracy.

Payees03

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

01
MX
1

Share of mentions

CompanyShare

Plaid (Income & Cash Flow)
50.0%
Prism Data
35.7%
MX
7.1%
Petal (Petal’s Prism Data Platform)
7.1%
Argyle
0.0%
Pinwheel
0.0%

Referrers04

Websites the AI used as sources

How many times each website was listed as a source in these answers

01
plaid.com
23
02
credolab.com
7
03
aloan.ai
6
04
f6s.com
6
05
floowed.com
6
06
assets.ctfassets.net
6
07
novacredit.com
6
08
experian.com
5
09
celent.com
5
10
ocrolus.com
5

Prism Data is in the study as a fintech infrastructure company.

By assistant05

Which AI assistants named the company

Answers naming it, out of answers received

Gemini

3/15

Claude

1/15

Perplexity

1/15

OpenAI

0/15

Market comparison06

How Prism Data 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

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

Citations

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

Sentiment

How positively AI describes the company

Site quality

How well built the website's own pages are

AI readiness

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 sector07

Compared with financial technology and data companies

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

Overall score

Ranks 237 of 574. Typical company: 29

Named in answers

8.3%

Ranks 130 of 574. Typical company: 1.7%

AI readiness

Ranks 401 of 574. Typical company: 29

Against its category08

Compared with fintech infrastructure companies

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

Overall score

Ranks 104 of 315. Typical company: 28

Named in answers

8.3%

Ranks 52 of 315. Typical company: 1.7%

AI readiness

Ranks 215 of 315. Typical company: 29

AI agents09

Ready for AI agents

What this website publishes for an AI agent to read, use and pay through, on a scale of six levels.

0Not yet agent-ready
1Basic web presence
2Bot-aware
3Agent-readable
4Agent-integrated
5Agent-native

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. Prism Data 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 · www.prismdata.com · 2,200 companies scanned

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

AI readiness10

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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<a href="https://aeo-audit.rezolve.com/us/finance/prismdata.com"><img src="https://aeo-audit.rezolve.com/finance/badges/prismdata.com.svg" alt="Overall score 31 of 100, Finance AI Visibility Study 2026" width="280" height="64"></a>

End of statement

All 2,219 companies How we measured this

Prism Data

What this means for Prism Data

Get the full report on Prism Data as a PDF

Every question, and where the company came up

What Perplexity actually said

Where the mentions went

Also named

Share of mentions

Websites the AI used as sources

Which AI assistants named the company

How Prism Data compares with all 2,219 companies

What makes up the score

Compared with financial technology and data companies

Compared with fintech infrastructure companies

Ready for AI agents

The checks behind the AI readiness score

Discoverability

Content Accessibility

Bot Access Control

API / Auth / MCP

Get the full report on Prism Data as a PDF

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