# SEON vs NoFraud: which do AI models recommend for ecommerce fraud, October 2026

Finance AI Recommendation Index, October 2026 Edition, Ecommerce fraud prevention. Six of fourteen models named SEON first on the direct prompt; zero named NoFraud. Page: https://finance-ai-index.com/receivables/ecommerce-fraud-prevention/seon-vs-nofraud/

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| SEON | 16% | #1 of 11 | 12% | 34 | 13 of 14 |
| NoFraud | 8% | #5 of 11 | 13% | 23 | 12 of 14 |

## The direct prompt, model by model

- Gemini 3.5 Flash: seon first (first choices: SEON) (alternatives: Allianz Trade Pay, Sift, Signifyd, TreviPay)
- Perplexity Sonar: seon first (first choices: SEON) (alternatives: Kount, Sift, Signifyd)
- DeepSeek V4 Flash: seon first (first choices: SEON) (alternatives: Sift, Signifyd, Stripe Radar)
- Kimi K2: seon first (first choices: SEON) (alternatives: Eftsure, Kount, Trustmi)
- MiniMax M2.5: seon first (first choices: ClearSale, SEON) (alternatives: Forter, Riskified)
- GPT-6 Luna: seon first (first choices: SEON) (alternatives: Kount)
- Grok 4.1 Fast: neither first, one named (first choices: Sift) (alternatives: Chargeflow, Fingerprint, SEON, Signifyd, Wyllo)
- Mistral Small: neither first, one named (first choices: Signifyd) (alternatives: NoFraud, Sift)
- Llama 4 Maverick: neither first, one named (first choices: Wyllo) (alternatives: NoFraud, SEON, Sift, Signifyd, cside)
- GLM 4.7 FlashX: neither first, one named (first choices: Signifyd) (alternatives: ClearSale, Eftsure, SEON, Sift)
- Muse Glimmer 30B: neither first, one named (first choices: Signifyd, Wyllo, formerly NoFraud) (alternatives: Eftsure, SEON)
- Claude Haiku 4.5: neither named (first choices: FraudNet, Trustmi) (alternatives: Kount, Sift, Signifyd)
- GPT-5.4 mini: neither named (first choices: Sift) (alternatives: Forter, Kount, Signifyd)
- Qwen 3.7 Flash: neither named (first choices: Signifyd) (alternatives: Fingerprint, Kount)

## What the models said about SEON

- "SEON is often featured for flexible fraud signals, but it is not as clearly the cheapest option" (Perplexity Sonar, budget prompt, soft negative)
- "SEON — Mixed Reviews (21% 1-star ratings) ... blackbox nature (opaque ML models)" (GLM 4.7 FlashX, negative prompt, soft negative)
- "Examples of platforms using this model: Sift, Kount, SEON, Forter" (Gemini 3.5 Flash, negative prompt, soft negative)
- "SEON offers modular fraud prevention tools with a free tier... particularly accessible to small and mid-market businesses" (Claude Haiku 4.5, budget prompt, first choice)
- "I'd recommend starting with ClearSale or SEON as they specifically target mid-market businesses" (MiniMax M2.5, direct prompt, first choice)
- "SEON is highly recommended for mid-market B2B companies that have an in-house operations or risk team" (Gemini 3.5 Flash, direct prompt, first choice)

## What the models said about NoFraud

- "NoFraud is the one I'd most strongly caution against for smaller merchants, based on the 1.8/5 Trustpilot score" (DeepSeek V4 Flash, negative prompt, hard negative)
- "Its paid starter plans begin at $250/month, so they may not suit a very limited budget." (GPT-6 Luna, budget prompt, soft negative)
- "Examples of platforms using this model: Riskified, Signifyd, ClearSale, NoFraud" (Gemini 3.5 Flash, negative prompt, soft negative)
- "The best ecommerce fraud prevention platform for a company with a limited budget is NoFraud for Shopify merchants" (Llama 4 Maverick, budget prompt, first choice)
- "For the most budget-friendly options, start with NoFraud (free Lite tier, $250/month)" (Mistral Small, budget prompt, first choice)
- "NoFraud is widely considered the best value platform for growth-stage businesses." (Qwen 3.7 Flash, budget prompt, first choice)

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. Comparisons are drawn for the top eight products in each category. Published under CC BY 4.0; the output is the models' output, and nothing here is a recommendation by the index.