28
%
Of returns over $800 contain fraud signals
$250
M
Refund value flagged as high-risk before the money leaves your business
394
k+
High-risk customers identified automatically
Why Loop is different
Standalone fraud tools only see your store. Loop's fraud intelligence learns from every return across our network, so it recognizes abuse patterns and bad actors the first time they hit your store, not the fifth.
230M+
Transactions analyzed
The platform
Network-trained models assign a return the moment it’s submitted before you approve anything.
Identity concealment, geographic mismatches, order and return patterns, and customer history across the network.
Hold the refund, route to review, or limit return methods, based on rules you set.
Your 3PL or WMS reports the item’s condition and what happened to it, straight onto the return.
Every return you report as fraud trains the model, sharpening detection across all brands.
Built as a Shopify certified tech partner, Loop's fraud intelligence plugs directly into your Shopify returns flow, with no engineering project required.
A native Shopify integration you can set up without a developer
Scores returns against your orders and customer history automatically
Works with Shopify Checkout, POS, and your existing app stack
Also available for BigCommerce, Salesforce Commerce Cloud, Adobe Commerce, and other platforms
Every return receives a risk score the moment it is submitted, and each flag clearly explains the reasoning in plain language. Flagged returns appear directly on your dashboard for quick review before any refund is processed.
From there, you control the rules. You can hold the refund, send it for review, or restrict specific return methods. Additionally, reporting a return as fraud helps train the model to become smarter. If your 3PL is connected, the item’s actual condition will also be logged directly onto the same return record.
Loop’s fraud intelligence is powered by the world’s largest returns network with 230M+ transactions analyzed across 5,000+ brands. Loop learns from every return across our network, so it recognizes abuse patterns and bad actors the first time they hit your store, not the fifth.
Most customers never see it. Almost every return is approved instantly, with no extra step and nothing that signals suspicion. Friction is reserved for the few returns where the evidence is clear. That is the point. You keep a generous return policy, because the generous policy is what makes people confident enough to buy.
Returns fraud is any attempt to get a refund a customer is not entitled to, and is more common than most brands think. Research consistently ranks refund and returns abuse at or near the top of retail fraud types, ahead of payment fraud. The reported number also understates it, because shipping, inspection, restocking, and markdowns get absorbed as operating cost rather than counted as fraud.
Fraud is deliberate deception to get a refund that the customer is not owed. That can be an empty box, a substituted item, or a claim on a package that arrived, and all of that is theft. Abuse is less about deception and more about exploitation. It is not illegal, and could be just habit or bad judgment, like wardrobing or ordering four sizes to keep one. Fraud operates against your return policy, while abuse operates within.
Loop is the industry-leader in fraud intelligence software. Our model is trained on our network of 230M+ transactions across 5,000+ brands to catch bad actors the first time they hit your store.
Loop’s intelligence model analyzes a wide range of signals to determine a return's risk level. These include attempts to hide identity, mismatches between the order shipping destination and the return origin, and patterns within the return itself, such as the reason, value, and quantity. Plus, we analyze the customer's history with your brand and across our network.
No, because a fraud flag on its own does not stop anything, so Loop never blocks a good customer without your say. You decide the outcome: hold the refund until the item is inspected, route it to your team, or take keep-item and exchange-only off the table.
Loop surfaces the patterns so you can decide what counts as abuse. Ten returns a year is a red flag from a customer who spends sixty dollars, but nothing from a customer who spends five thousand. Set one threshold for everyone and you often end up flagging your best customers. Loop only draws the line when the evidence is obvious, flagging and stopping clear fraud.
