Fraud
Fraud combines the power of our in-house foundation model with real-time post-purchase data to stop customers from taking advantage of your brand.
Instantly detect and block suspicious activity across all returns, exchanges, and policies before it leads to revenue loss.
$
250
M
Refund value flagged before payout
Identify high-risk refunds before money leaves your business.
394
k+
High-risk customers identified
Repeated identities and behavior patterns surfaced automatically.
28
%
Fraud signals on $800+ returns
Fraudsters target high-value returns the most.
Save money
The fraud model reviews the size of the order, discounts used, return reasons, item quantity, refund amount, customer purchase history, customer return history, geographical anomalies on the shipping address vs. return address, device and browser details that could conceal the customers identity, and more.
Save time
Automated workflows can be triggered the moment fraud is identified, allowing you to immediately reject the return, block specific return outcomes, or flag the return for manual review.
Save money
Every return is evaluated for fraud before it is submitted. When a fraudulent return hits the admin, you will immediately see if it is a high risk or low risk return. For high risk returns, you can review which exact characteristics most influenced the score.
Save money
Inside your analytics dashboard you can review how many returns have been flagged as fraudulent, how many returns have been deemed high risk, and all identifying information about the customer.
Payment fraud tools focus on checkout and chargebacks. Loop Fraud Tools focus on returns fraud, where loss shows up after delivery and payment tools no longer have visibility.
No. Fraud decisions happen in the background. You control what, if anything, a higher-risk shopper sees.
No. Fraud scoring runs automatically and evaluates patterns over time, not static rules. Workflows can be added if you want to automate actions by risk level.
No. By evaluating behavior patterns instead of one-off signals, the system reduces false positives and unnecessary reviews.
Examples of return fraud include empty box returns, item switching, false “return not received” or “item not received” claims, and false defective claims. Platforms like Loop help brands spot suspicious return patterns like these and flag potential abuse before it becomes costly.
Fraud risk indicators appear directly in Loop’s returns views and workflows, so teams can act without switching tools.
