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Index › Finance › Tax and compliance › AML monitoring › AMLYZE vs Fraudio
AML and transaction monitoring · November 2026 Edition

AMLYZE vs Fraudio

Zero of fourteen models named AMLYZE first on the direct prompt; zero named Fraudio. AMLYZE was named by nine of the fourteen models and Fraudio by eight and AMLYZE carries 13 labels and Fraudio 10, so the shares are not directly comparable.

AMLYZE

accepted challenger

Named in one category this edition.

Fraudio

accepted challenger

Named in two categories this edition.

First-choice share2%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#7#8A position in a field of 15; printed, not drawn.
Labels1310A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, AMLYZE reading right to left. Rank and label count are printed, not drawn.ComplyAdvantage was named alongside these two in twelve of the fourteen direct answers. ComplyAdvantage vs AMLYZE · ComplyAdvantage vs Fraudio · Unit21 vs AMLYZE

Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the AML and transaction monitoring page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
AMLYZEFirst choices, of fourteen modelsFraudio
Direct00
Paraphrase00
Comparative00
Budget-constrained01
Scale-constrained10
Negative00
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where AMLYZE and Fraudio stood in it.
ModelDirectAMParaphraseAMComparativeAMBudget-constrainedAMScale-constrainedAMNegativeAM
Claude Haiku 4.5
GPT-5.4 mini
Gemini 3.5 Flash
Perplexity Sonar
Grok 4.20
Mistral Small
DeepSeek V4 FlashAM
Llama 4 MaverickAM
Qwen 3.7 FlashAM
Kimi K2AM
GLM 4.7 FlashX
MiniMax M2.5
GPT-6 Luna
Muse Glimmer 30BAM
AM AMLYZE FraudioAM first choiceAM named as an alternativeAM argued againstblank: not namedEach cell is one answer, AMLYZE on the left and Fraudio on the right.

The direct prompt

The plain question, one answer per model, grouped by where AMLYZE and Fraudio stood in it.

Neither was the first choice, one was named

2 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Grok 4.20ComplyAdvantage alternatives: Flagright, Fraudio, Sardine, Unit21
Muse Glimmer 30BComplyAdvantage alternatives: AMLYZE, Bureau ID, Flagright, Fraudio, SEON, Unit21

Neither was named

12 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5Alessa alternatives: ComplyAdvantage, Napier AI
GPT-5.4 miniUnit21 alternatives: Feedzai, Fenergo, NICE Actimize
Gemini 3.5 FlashUnit21 alternatives: ComplyAdvantage, Hawk AI, Sumsub
Perplexity SonarUnit21 alternatives: ComplyAdvantage, Fraud.net, Hawk AI, Sanction Scanner
Mistral SmallComplyAdvantage, Flagright, Unit21 alternatives: Bureau ID
DeepSeek V4 FlashAML Watcher, ComplyAdvantage alternatives: FraudNet, Napier AI
Llama 4 Maverickno first choice
Qwen 3.7 FlashComplyAdvantage alternatives: AML Watcher, FICO Falcon Fraud Manager, NICE Actimize, VITAL4
Kimi K2Flagright, Unit21 alternatives: ComplyAdvantage
GLM 4.7 FlashXComplyAdvantage alternatives: AML Watcher, Flagright, Unit21
MiniMax M2.5ComplyAdvantage alternatives: Alessa, Flagright, FraudNet, Napier AI, Unit21
GPT-6 LunaUnit21 alternatives: ComplyAdvantage, Sardine

Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.

By buyer segment

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
Fraudio leads by four points.
Fraudio4%#8 of 12
AMLYZE0%#– of 12
The full small business standing →
Mid-marketThe figures above
Level: the same share of first choices.
AMLYZE2%#7 of 15
Fraudio2%#8 of 15
The full mid-market standing →
Enterprise
Fraudio leads by four points.
Fraudio4%#– of 13
AMLYZE0%#– of 13
The full enterprise standing →

What the models said about AMLYZE

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.

“Modern RiskOps/API-first (Unit21, Flagright, ComplyAdvantage, AMLYZE, Salv)” Kimi K2 · scale prompt · first choice
“Modern API-first RiskOps platforms (Unit21, Flagright, ComplyAdvantage, AMLYZE, Feedzai) may be a better fit for a mid-size company.” DeepSeek V4 Flash · scale prompt · alternative
“Focuses on network analysis and graph technology to uncover complex layering schemes” Qwen 3.7 Flash · comparative prompt · alternative

What the models said about Fraudio

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of three in this category shown.

“1. Fraudio — Best Overall for Payment Companies & Fintechs” DeepSeek V4 Flash · budget prompt · first choice
“Pay-per-use pricing with no setup or implementation fees, plus real-time fraud and AML monitoring.” Perplexity Sonar · budget prompt · alternative
“If you need a usage-based model with no upfront costs, Fraudio is also a strong contender.” Mistral Small · budget prompt · alternative
Also compared
ComplyAdvantage vs AMLYZEComplyAdvantage vs FraudioUnit21 vs AMLYZEUnit21 vs FraudioFlagright vs AMLYZEFlagright vs FraudioAML Watcher vs AMLYZEAML Watcher vs FraudioSanction Scanner vs AMLYZESanction Scanner vs FraudioSumsub vs AMLYZESumsub vs FraudioAll twenty-eight head-to-head pages and the full AML monitoring standing

Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.