Every category where a model named FraudNet 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
Ecommerce fraud prevention	Receivables and billing	2%	11 of 70	0%	6	under 10 labels · led by SEON at 16%
AML and transaction monitoring	Tax and compliance	0%	30 of 108	0%	4	under 10 labels · led by ComplyAdvantage at 33%

Movement

This is the first edition on this tier, so no move can be computed for FraudNet 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 FraudNet across every prompt where it was named for a mid-market B2B company. Fourteen models, six prompts per category.

ShowHide

Model	First choice	Alternative	Mention	Labels
Claude Haiku 4.5	1	0	0	1
GPT-5.4 mini	0	0	0	0
Gemini 3.5 Flash	0	0	0	0
Perplexity Sonar	0	1	1	2
Grok 4.1 Fast	0	0	0	0
Mistral Small	0	0	0	0
DeepSeek V4 Flash	0	1	0	1
Llama 4 Maverick	0	1	2	3
Qwen 3.7 Flash	0	0	1	1
Kimi K2	0	0	0	0
GLM 4.7 FlashX	0	0	2	2
MiniMax M2.5	0	0	0	0
GPT-6 Luna	0	0	0	0
Muse Glimmer 30B	0	0	0	0

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

Direct12 labels5

Paraphrase11 labels3

Comparative4 labelsNone

Budget-constrained0 labelsNone

Scale-constrained1 labelNone

Negative0 labelsNone

First choiceAlternativeMentionNegative28 labels in all, every segment counted; 8 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.

“FraudNet is a comprehensive fraud detection and risk management platform purpose-built for the demands of B2B e-commerce” Claude Haiku 4.5 · Ecommerce fraud · direct prompt · first choice

“Offers B2B transaction monitoring software that analyzes and tracks financial transactions between businesses in real-time.” Llama 4 Maverick · AML monitoring · direct prompt · alternative

“For best transaction monitoring specialization: Napier AI, FraudNet, or SymphonyAI Sensa.” Perplexity Sonar · AML monitoring · comparative prompt · alternative

“Also Worth Considering: FraudNet” DeepSeek V4 Flash · Ecommerce fraud · paraphrase 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 FraudNet, over the 28 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and FraudNet was named but was not.

ShowHide

ProductSame answerTook the first choice insteadHead to head

Sift10 of 280Not among the top eight

Signifyd8 of 281Not among the top eight

ComplyAdvantage7 of 282Not among the top eight

Eftsure7 of 282Not among the top eight

Forter6 of 281Not among the top eight

Riskified5 of 281Not among the top eight

Trustpair5 of 281Not among the top eight

NICE Actimize4 of 281Not among the top eight

Ondato4 of 281Not among the top eight

SEON4 of 281Not 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 FraudNet. 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 28 of the 28 answers that named FraudNet and are not a share of its labels.

Domains cited

fraud.net27

g2.com13

fingerprint.com10

eftsure.com9

trustmi.ai9

learn.g2.com8

shuftipro.com7

sourceforge.net7

trustpair.com7

ondato.com6

No domain is on file for FraudNet, so its own site is not marked.

Pages cited

eftsure.com/blog/products/top-financial-fraud-prevention-software-platforms-for-20259

fraud.net/resources/top-fintech-fraud-detection-services9

trustmi.ai/resource/best-b2b-fraud-detection-tools-software9

fraud.net/resources/best-fraud-detection-software-for-e-commerce8

fraud.net/resources/top-aml-software7

g2.com/categories/e-commerce-fraud-protection7

eftsure.com/en-au/blog/products/top-9-financial-fraud-prevention-software-platforms-for-206

fraud.net/resources/best-tools-for-detecting-payment-fraud6

Pages are listed as the models cited them.

Search and answers

Where FraudNet stands in Google search beside where it stands in the models' answers.

ShowHide

In search

Google, US estimates

Searches for its name, Google: 140 a month (“fraudnet”)
AI search demand for its name, est.: 6 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 11 of 70 in ecommerce fraud

Segment leader

16%

SEON

First choices: 8 across its categories
Named in: 28 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 FraudNet

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.

Is this your product?

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 FraudNet'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 FraudNet, 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 FraudNet by email, built from the raw record of the edition. It shows:

where FraudNet 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 FraudNet, 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.