AMOS Fraud Protector

Develop by Shopify

Free App

Automate high-risk orders workflow, prevent fraud & chargeback

Email notifications

Handle refunds

Cancel high-risk orders

Automate high-risk workflow

Tag suspicious customers

Streamline restocks

Prevent chargebacks

About AMOS Fraud Protector

Launched Jul 19, 2024

Built by Shopify

Address

151 O’Connor St, Ottawa, ON, ON, CA

Website Support Portal Privacy policy Tutorial FAQ Document

Related Categories

Unlock Deals 20% off

BON Loyalty Rewards Program

(1,471 Reviews)

Unlock Deals 20% off

Sponsored

Loyalty rewards program with points, POS, referrals &...

Description

Designed for merchants who want to minimize fraud and secure their revenue. Based on Shopify fraud analysis, Protector can automatically cancel high-risk orders, tag suspicious customers or orders, and notify staff via email. It also covers refunds and restocks, streamlining fraud prevention. The app is ideal for stores facing frequent fraudulent activities, providing a reliable solution to prevent chargebacks and losses. Instead of manually managing high-risk orders, Protector does it for you.

Automatically cancel high-risk orders to prevent fraud.
Tag suspicious customers or orders for easy identification.
Notify staff via email about high-risk orders.
Handle refunds and restocks efficiently.
Avoid fulfilling fraudulent orders and prevent chargebacks.

Pros

Automates cancellation of high-risk orders, reducing manual workload
Tags suspicious customers or orders for easy identification
Notifies staff via email about high-risk orders, ensuring prompt action
Handles refunds and restocks efficiently, streamlining the process
Helps avoid fulfilling fraudulent orders, thus preventing chargebacks
Based on Shopify fraud analysis, leveraging existing infrastructure

Cons

Reliance on Shopify's fraud analysis means it may have limitations inherent in Shopify's system
Automatic cancellation might lead to rare false positives and lost sales
Tagging and notifications could potentially overwhelm staff if not managed well
May not fully cover complex fraud scenarios requiring more sophisticated analysis