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

Finance AI Recommendation Index, October 2026 Edition, Ecommerce fraud prevention. Four of fourteen models named Signifyd first on the direct prompt; two named Sift. Page: https://finance-ai-index.com/receivables/ecommerce-fraud-prevention/signifyd-vs-sift/

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| Signifyd | 8% | #4 of 11 | 19% | 43 | 14 of 14 |
| Sift | 4% | #6 of 11 | 16% | 44 | 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)
- GPT-5.4 mini: sift first (first choices: Sift) (alternatives: Forter, Kount, Signifyd)
- Grok 4.1 Fast: sift first (first choices: Sift) (alternatives: Chargeflow, Fingerprint, SEON, Signifyd, Wyllo)
- Claude Haiku 4.5: neither first, one named (first choices: FraudNet, Trustmi) (alternatives: Kount, Sift, 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)
- 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 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)

## What the models said about Sift

- "Can be complex to configure; occasional false positives requiring manual review. Not ideal for very small teams due to setup overhead." (Grok 4.1 Fast, negative prompt, soft negative)
- "Unlike consumer-focused tools (like Sift or Stripe Radar), these are specifically designed for B2B payment workflows" (Kimi K2, paraphrase prompt, soft negative)
- "Some users note that the system can occasionally generate false positives, which may require additional review time." (Claude Haiku 4.5, negative prompt, soft negative)
- "Start with Sift for most mid-market B2B ecommerce—it's consistently #1 in G2 rankings for balanced performance without overkill." (Grok 4.1 Fast, direct prompt, first choice)
- "Sift maintains #1 position across all fraud prevention categories in G2's Fall 2025 Reports" (Muse Glimmer 30B, comparative prompt, first choice)
- "Sift - Top-rated on G2 for real-time detection." (Grok 4.1 Fast, comparative 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.