Tag and Cancel Fraudulent Orders with Address Discrepancies

6 min setup

No coding required

Runs automatically

Prevent chargebacks and fraud by detecting when the same shipping address is reused under a different name or email. This workflow pairs with the Store Shopify Orders in a Database template. When a new Shopify order is created, MESA checks your order database to see if the shipping address has been used before. If it has, and the customer name or email doesn't match, an approval step will be sent to you for review. A "Fraud" tag is added if deemed a fraudulent order, and MESA will automatically cancel the order.

Set this up in 6 minutes → Related article

Order Created

Query: Find Past Orders with Same Shipping Address

Filter: Compare Customer Name and Email

Approval

Tag Order as Fraud and Cancel the Fraudulent Order

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How it works

5 steps to start automatically flagging and canceling potentially fraudulent orders

App connector: Shopify • Time to complete: 0 minutes (Auto-configured)

Why this matters: This step monitors your store for new orders and kicks off the fraud detection process immediately when someone completes a purchase.

When a customer places an order in your Shopify store, this trigger captures all the order details including customer information, shipping address, and order contents. The system automatically pulls this data without any configuration needed from you. This information then flows to the next step where it gets analyzed against your order history to identify potential fraud patterns.

App connector: Data • Time to complete: 0 minutes (Auto-configured)

Why this matters: This step searches your order history to find previous orders that were shipped to the exact same address, which is crucial for detecting identity mismatches that often indicate fraud.

The system queries your order database looking for any previous orders that match the current order's complete shipping address including street address, city, state, zip code, and country. This comparison happens automatically using the address data from the trigger step. If matching addresses are found, the system retrieves customer names and email addresses from those historical orders to compare against the current order's customer details.

App connector: Filter • Time to complete: 0 minutes (Auto-configured)

Why this matters: This filter determines whether the current customer's identity matches previous customers who used the same shipping address, which is the key fraud indicator this workflow detects.

The system compares the current order's customer name and email address against the customer information from previous orders shipped to the same address. If the names don't match AND the email addresses don't match, the filter identifies this as suspicious activity that warrants manual review. When identity mismatches are detected, the workflow proceeds to the approval step, but if the customer information matches historical orders, the workflow stops here as no fraud is suspected.

App connector: Approval • Time to complete: 2 minutes

Why this matters: This approval step puts a human checkpoint in place before any automated actions are taken, ensuring you can verify fraud suspicions before canceling legitimate orders.

When identity mismatches are detected, this step sends you an approval request with details about the suspicious order including the order number and why it was flagged. You'll need to configure the email address where you want to receive these fraud alerts. The approval message explains that the shipping address recipients don't match previous orders to that address. You can then review the order details in Shopify and either approve the fraud designation (which proceeds to tag and cancel the order) or reject it if the order appears legitimate.

App connector: Shopify • Time to complete: 0 minutes (Auto-configured)

Why this matters: These final steps automatically mark confirmed fraudulent orders and cancel them, preventing shipping costs and potential chargebacks while maintaining a clear record of fraud incidents.

Once you approve the fraud designation, the system first adds a "Fraud" tag to the order in Shopify for easy identification and reporting purposes. Immediately after tagging, the system cancels the order completely, which prevents fulfillment and shipping. Both actions happen automatically in sequence without additional input needed from you, ensuring fraudulent orders are handled quickly and consistently.

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Frequently asked questions

What happens if the same legitimate customer moves and starts using a new address?

The workflow will flag this as suspicious since their name appears with a different address than before. However, the manual review step lets you verify this is legitimate before canceling the order. You can simply reject the fraud approval to let the order proceed normally.

Will this catch fraud attempts using completely fake customer information?

This workflow specifically detects identity mismatches at known addresses - when someone uses a real address but different customer details than previous orders to that location. It won't catch fraud using entirely fabricated addresses that have never been used before.

How quickly does the fraud detection process run after an order is placed?

The entire detection process typically completes within 2-3 minutes of order placement. If fraud is suspected, you'll receive the approval notification immediately, allowing you to review and take action while the customer's session is often still active.

Make this template your own!

Customize this workflow even further:

Add shipping notifications

Send automated emails to customers explaining order cancellations, or notify your fulfillment team when fraud is detected to halt shipping processes.

Create fraud reporting dashboards

Store fraud incident data in custom tables to track patterns over time, analyze fraud trends by product or region, and generate monthly security reports.

Integrate with review platforms

Connect to customer service tools to automatically create support tickets for canceled fraud orders, ensuring proper customer communication and dispute management.

Deploy AI for enhanced detection

Use AI agents to analyze additional fraud indicators like order value patterns, shipping speed preferences, or payment method combinations before triggering manual reviews.

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