# Stripe Radar vs Signifyd: which do AI models recommend for ecommerce fraud, October 2026

Finance AI Recommendation Index, October 2026 Edition, Ecommerce fraud prevention. Zero of fourteen models named Stripe Radar first on the direct prompt; four named Signifyd. Page: https://finance-ai-index.com/receivables/ecommerce-fraud-prevention/stripe-radar-vs-signifyd/

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| Stripe Radar | 14% | #3 of 11 | 9% | 32 | 12 of 14 |
| Signifyd | 8% | #4 of 11 | 19% | 43 | 14 of 14 |

## The direct prompt, model by model

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

## What the models said about Stripe Radar

- "scalable for mid-sized but more e-commerce/B2C oriented—less ideal for pure B2B vendor payments" (Grok 4.1 Fast, paraphrase prompt, soft negative)
- "it is more of a payments-layer fraud tool than a dedicated end-to-end ecommerce risk platform" (GPT-5.4 mini, comparative prompt, soft negative)
- "More focused on consumer/card transactions than pure B2B vendor payments" (Kimi K2, paraphrase prompt, soft negative)
- "the best ecommerce fraud prevention platform is usually Stripe Radar if you already process payments through Stripe" (GPT-5.4 mini, budget prompt, first choice)
- "Stripe Radar stands out as one of the best ecommerce fraud prevention platforms due to its extremely low cost" (Grok 4.1 Fast, budget prompt, first choice)
- "If you already process payments through Stripe, start with Stripe Radar (free for the basic plan)." (MiniMax M2.5, budget prompt, first choice)

## What the models said about Signifyd

- "Signifyd's public terms define eligible chargebacks narrowly... That's a reason to get the exact coverage schedule and exclusions in the signed agreement—not, by itself, a reason to avoid Signifyd." (GPT-6 Luna, negative prompt, soft negative)
- "Choose Signifyd or Riskified if: You want to outsource fraud completely... Go with Signifyd if you are mid-market and want seamless e-commerce platform plug-ins." (Gemini 3.5 Flash, comparative prompt, first choice)
- "The original "chargeback guarantee" pioneer ... Best fit: High-volume merchants ($10M+ GMV) who want fraud completely off their plate." (Kimi K2, comparative prompt, first choice)
- "The Best Overall (Especially for Open Credit): Signifyd ... widely considered the gold standard for B2B commerce" (Qwen 3.7 Flash, direct 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.