Comparison

How Scaling Shopify Brands Choose Between Fraud Prevention Vendors

Most high-growth Shopify brands eventually hit the same inflection point: fraud is growing, built-in protections aren’t cutting it, and you need a dedicated solution. The right choice depends on your stage, your customization needs, and how you want to operate: Some merchants want to hand fraud off entirely and let a guarantee cover the losses. Others want full control over every decision. Most land somewhere in between. Where you fall shapes which vendor fits. Here’s how the five most common approaches compare.

	Signifyd	Riskified	Forter	Sift	SEON
Transparency	Vendor decides; limited visibility	Vendor decides; limited visibility	Automated; limited visibility	ML-driven; limited visibility	Full visibility into scoring & rules
Shopify Fit	Native app (checkout only)	Native app (checkout only)	Native app	Native app + external console	Native app + API + Shopify Flow
Pricing	% of GMV	Per-order fee	% of GMV (plus add-ons)	Tiered SaaS	Flat SaaS (usage-based)
Coverage	Payment fraud	Payment fraud	Payment + identity	Payment + content + account	Full journey: signup to payment
Customization	Minimal	Minimal	Minimal	Moderate	Unlimited; you own decisions
Chargeback Guarantee	Yes, unless manually approved	Yes, unless manually approved	None	None	Optional with Chargeflow

Who Each Solution Is Best For

Sanctions screenings operate through sophisticated processes that ensure thorough and accurate screening of entities against relevant lists. Here’s a breakdown of how these solutions typically work:

Pros

Native Shopify integration
Full visibility
Flat pricing

Full control over fraud decisions

Configurable rules engine with complete transparency into every risk score. Your team sees why decisions are made and can tune thresholds in real time.

Native Shopify integration

New Shopify app integrates into Shopify workflows and Flow. Protects the full journey, from signup and login to checkout.

Predictable pricing

Flat SaaS pricing based on API usage – not a percentage of GMV. Your fraud costs don’t grow just because your revenue does.

900+ real-time proprietary signals

Email, phone, device, IP, social footprint and behavioral data. Deeper intelligence than stale, third-party data or siloed checkout-only solutions.

Best for brands that want covered fraud liability and prefer to fully outsource decisions.

Signifyd’s chargeback guarantee means you hand off fraud entirely – they approve or decline, and absorb the losses. Works well for brands without a fraud team. The trade-off: no visibility into decisions, pricing is a percentage of GMV that grows with your revenue and the vendor is incentivized to over-decline to protect their margins. Their chargeback guarantee does not cover custom rules or approvals.

Best for enterprise merchants willing to pay premium per-order fees for liability shift.

Riskified’s Chargeback Guarantee reimburses merchants for fraud on approved orders, and they’ve expanded into account takeover prevention, policy abuse, and dispute management through separately purchasable products. GMV% pricing grows directly with your volume and can get expensive at scale, and the chargeback guarantee model still incentivizes the vendor to decline borderline orders to protect its own margins. They offer a native Shopify app, though their coverage beyond payments requires purchasing additional modules on top of the core guarantee.

Best for enterprises that want automated identity-based decisions and can invest in multiple tools.

Identity-first approach using behavioral analysis and third-party consortium data for device intelligence. Heavier integration than Shopify-native apps, with separate fees for device fingerprinting. Reviews mention inconsistent approval rates and limited control over decisioning logic.

Best for marketplace and marketplaces and tech platforms needing broad support beyond payments.

Covers payment fraud, promotion abuse, and account takeover across more touchpoints in the user journey, including signup and transactions. Operates mostly outside the native Shopify environment and the ML-driven model offers limited rule customization for Shopify-specific fraud patterns.