Comparison

Shopify Fraud Filter alternative

Shopify sunset the official Fraud Filter app on January 31, 2025, leaving merchants who relied on it looking for replacements. Shopify recommended migrating to Shopify Flow — but Flow can't replicate the cross-order identity matching that catches the most common form of merchant fraud: discount code abuse. Here's what changed, what Shopify's recommended replacements actually cover, and where CustomerGenius fits.

TL;DR

Shopify retired the Fraud Filter app on January 31, 2025 and recommended Shopify Flow as the migration path. For payment fraud and stolen-card chargebacks, Shopify still ships a built-in fraud analysis (the low/medium/high risk indicator) and offers Shopify Protect for Shop Pay orders, plus a marketplace of third-party apps like NoFraud, Signifyd, and the new Fraud Control. None of those replacements specifically address discount code abuse — the repeated redemption of first-time-customer codes by the same person under throwaway emails. CustomerGenius is built for exactly that problem, with multi-signal identity matching, fuzzy address detection, and configurable automation rules. It's not a one-to-one Fraud Filter replacement, but it covers the discount-abuse gap that none of the official replacements address.

Feature-by-feature comparison

FeatureCustomerGeniusShopify Fraud Filter
Still available in 2026
Detects discount code abuse across emailsPartial
Identity matching across email, phone, name, address
Fuzzy address matching (typos, abbreviations)
Cross-evaluation discount groups (SAVE10/SAVE20)
Configurable per-code thresholds and actions
Occurrence-based rules (act on 3rd attempt etc.)
Identity-based customer block listPartial
Subscription-aware (skips Recharge renewals)
Automatic refund/cancel on flagged orders
Cost$9.99–$29.99/moWas free

Why Shopify sunset Fraud Filter

Shopify announced the deprecation of the Fraud Filter app and retired it on January 31, 2025. The app had been around for years as a free utility that let merchants build custom filters — for example, block orders from specific email patterns, block by IP address, or auto-cancel orders shipping to known-fraud addresses. It was rule-based, manually maintained, and never had any cross-order identity logic. Shopify's official guidance to merchants was to migrate to Shopify Flow, which now exposes broader event triggers and actions across the platform, plus to rely on Shopify's built-in fraud analysis and the new Fraud Control app for the chargeback-prevention use cases.

What Shopify's recommended replacements actually cover

Shopify's built-in fraud analysis (the low/medium/high risk indicator on every order) and Shopify Protect (chargeback protection for Shop Pay orders) are both focused on payment fraud: stolen cards, mismatched billing/shipping, suspicious checkout behavior. They do that well. Shopify Flow is a workflow automation tool that can implement simple if-then rules at order time — useful for tagging, notifications, and basic conditional logic, but with no native concept of comparing one order against historical orders to find aliased customers. Third-party apps like NoFraud, Signifyd, and Fraud Control fill the chargeback/payment-fraud gap that the old Fraud Filter left. None of these replacements are built for discount code abuse, which is a fundamentally different problem.

Why discount code abuse needs its own tool

Discount stacking abuse doesn't look like payment fraud. The abuser uses their own real credit card, their own real address, and ships to themselves — every payment-fraud signal passes cleanly. The fraud is happening across orders, not within any single order: the same person creating a new email address each time to redeem a first-time-customer discount over and over. None of Shopify's recommended replacements compare new orders against historical orders on identity signals. CustomerGenius does exactly that. It pulls every prior order using a monitored discount code, matches the new order's email, phone, name, billing address, and shipping address (with fuzzy matching for typos and format variations), scores the result, and acts on it according to your configured threshold.

What you'd actually use post-sunset

For most Shopify stores, the post-Fraud-Filter stack looks like this. Payment fraud and chargebacks: Shopify's built-in fraud analysis plus Shopify Protect if you're on Shop Pay; add Signifyd, NoFraud, or Fraud Control if you have meaningful chargeback exposure (typically $10M+ GMV stores). General workflow automation: Shopify Flow. Discount code abuse: CustomerGenius. The categories don't overlap. None of these tools competes with the others; they cover different parts of the post-sunset fraud surface area.

Migrating discount-abuse rules from Fraud Filter to CustomerGenius

If you used Fraud Filter to block specific email patterns or addresses associated with discount abuse, you can replicate (and substantially improve on) those rules in CustomerGenius. The block list feature lets you ban specific customers across identities — so if a customer signs up under a new email but uses the same address or phone, they're still blocked. Custom automation rules let you set per-code thresholds (different thresholds per discount code) with configurable actions (refund vs. fulfillment hold vs. notify only). Cross-evaluation groups let you treat a family of codes — like every code starting with WELCOME — as a single eligibility pool, which Fraud Filter couldn't do at all. Setup typically takes under 10 minutes.

Which one should I use?

If you specifically lost discount-abuse protection when Fraud Filter was retired, CustomerGenius is the closest replacement — and meaningfully better at that specific job than Fraud Filter ever was. For payment fraud, lean on Shopify's built-in fraud analysis plus Shopify Protect (free) or NoFraud / Signifyd / Fraud Control (paid) if your chargeback exposure warrants it. The CustomerGenius 14-day free trial scans your last 60 days of orders during onboarding and shows you exactly how much discount abuse exists in your data, so you can see whether it's worth replacing the discount-abuse capability you lost.