Every category where a model named SAS Anti-Money Laundering for a mid-market B2B company. Share is first choices across the direct, paraphrase, budget and scale prompts; rank is within every product named in that category.

Category	Function	Share	Rank	Negative rate	Labels	Quadrant	Since September 2026
AML and transaction monitoring	Tax and compliance	0%	22 of 108	0%	6	under 10 labels · led by ComplyAdvantage at 33%

Movement

This is the first edition on this tier, so no move can be computed for SAS Anti-Money Laundering yet. From the next edition this section shows, per buyer segment and per category, whether its share moved by more than the measured noise floor.

By model

How each model treated SAS Anti-Money Laundering across every prompt where it was named for a mid-market B2B company. Fourteen models, six prompts per category.

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Model	First choice	Alternative	Mention	Labels
Claude Haiku 4.5	0	0	0	0
GPT-5.4 mini	0	0	0	0
Gemini 3.5 Flash	0	0	0	0
Perplexity Sonar	0	0	0	0
Grok 4.1 Fast	1	0	0	1
Mistral Small	0	0	0	0
DeepSeek V4 Flash	0	1	0	1
Llama 4 Maverick	0	0	0	0
Qwen 3.7 Flash	0	0	0	0
Kimi K2	0	0	1	1
GLM 4.7 FlashX	0	0	0	0
MiniMax M2.5	1	0	0	1
GPT-6 Luna	0	0	1	1
Muse Glimmer 30B	1	0	0	1

By framing

Which of the six questions produced the naming. By model says how often; this says asked what. The first-choice count on the right carries the marks of the models that produced it.

FramingLabels by classFirst choices

Direct3 labels1

Paraphrase5 labels2

Comparative12 labels4not counted in share

Budget-constrained4 labels1

Scale-constrained1 labelNone

Negative4 labelsNone

First choiceAlternativeMentionNegative29 labels in all, every segment counted; 4 of the 8 first choices count toward share, since the comparative and negative framings do not. The bar is one segment per label class, to scale within the framing.

What the models said for it

Verbatim evidence the judge attached to positive labels.

“SAS Anti-Money Laundering - Category Leader (5-star ratings in data integrations, modeling/risk typology, speed/volume, platform/case management, workflow automation).” Grok 4.1 Fast · AML monitoring · comparative prompt · first choice

“larger institutions often prefer SAS or Oracle for enterprise-scale capabilities” MiniMax M2.5 · AML monitoring · comparative prompt · first choice

“Best for Large institutions needing analytics-rich AML workflows” Muse Glimmer 30B · AML monitoring · comparative prompt · first choice

“Best for: Larger enterprises that want deep analytical capability and custom modeling.” DeepSeek V4 Flash · AML monitoring · comparative prompt · alternative

And against it

Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.

No model argued against it.

Named alongside

The products named in the same answers as SAS Anti-Money Laundering, over the 29 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and SAS Anti-Money Laundering was named but was not.

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ProductSame answerTook the first choice insteadHead to head

NICE Actimize25 of 2910Not among the top eight

Oracle Financial Crime and Compliance Management14 of 293Not among the top eight

ComplyAdvantage14 of 291Not among the top eight

Alessa8 of 290Not among the top eight

LexisNexis Risk Solutions8 of 290Not among the top eight

Quantexa7 of 290Not among the top eight

Feedzai5 of 290Not among the top eight

Hawk AI5 of 290Not among the top eight

Unit215 of 290Not among the top eight

Featurespace4 of 290Not among the top eight

A head-to-head page exists where both products are among a category's top eight. The other rows are the same fact without a page behind them, so they link to the product instead.

What carried it into the answer

The sites and pages cited by the answers that named SAS Anti-Money Laundering. A fact about retrieval, not a lever on the model.

Citations exist only for the models that return a source list, five of the fourteen in this edition, so these counts come from 29 of the 29 answers that named SAS Anti-Money Laundering and are not a share of its labels.

Domains cited

fraud.net19

bureau.id14

sas.com14

riskpublishing.com12

sphinxhq.com11

gartner.com10

worldmetrics.org10

finantrix.com8

oracle.com8

salv.com8

No domain is on file for SAS Anti-Money Laundering, so its own site is not marked.

Pages cited

bureau.id/resources/blog/transaction-monitoring-software14

riskpublishing.com/best-anti-money-laundering-aml-software-compa12

sphinxhq.com/blog-posts/best-aml-software9

finantrix.com/buyer-guides/aml-transaction-monitoring-banks-fintechs8

salv.com/blog/best-aml-software8

worldmetrics.org/best/transaction-monitoring-software8

fraud.net/resources/best-aml-compliance-software7

fraud.net/resources/best-anti-money-laundering-software-for-fintech7

Pages are listed as the models cited them.

Search and answers

Where SAS Anti-Money Laundering stands in Google search beside where it stands in the models' answers.

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In search

Google, US estimates

Searches for its name, Google: 110 a month (“sas anti-money laundering”)
AI search demand for its name, est.: 2 a month
Its own site: No site of its own on file, so no site figures

In answers

This edition

Share of first choices

rank 22 of 108 in AML monitoring

Segment leader

33%

ComplyAdvantage

First choices: 4 across its categories
Named in: 29 answers
Its own site cited: No site on file to match

Search figures are US estimates from DataForSEO, read October 5, 2026; AI search demand is its modeled, directional estimate, not a count of queries to any assistant. The answers are this edition's. Two measurements side by side: neither is read as the cause of the other.

Follow SAS Anti-Money Laundering

An email the morning each edition publishes: where this product moved, where it held, and by how much against the noise floor. One address, confirmed by a click; a stop link in every email.

Already following? Everything you follow, with a stop for each.

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Claim this page

Claiming is free and changes nothing in the data. A claimed page shows a verified contact who is told when each edition publishes and when SAS Anti-Money Laundering's standing changes by more than the noise floor; the right to propose corrections to the vendor table, meaning names the judge wrote that should or should not read as SAS Anti-Money Laundering, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.

What a new claim receivesHide what a new claim receives

A new claim receives the current edition's vendor brief for SAS Anti-Money Laundering by email, built from the raw record of the edition. It shows:

where SAS Anti-Money Laundering is named, by buyer and by framing, and which cells hold its first choices;
the claims the models make when they name it, ranked, with the strongest and the weakest quoted;
its vocabulary against the segment leader's, and the pages the models cited;
who was chosen in the answers that did not name SAS Anti-Money Laundering, and every reason the record gives;
a battlecard for each top rival: the head-to-head split, why they win, and the reservation quoted against them;
one page of published figures cleared to show a buyer.