Hey everyone,
I wanted to share some things I’ve learned about return fraud detection — and then mention something I built that might help some of you.
The patterns that are hardest to catch manually:
1. Reason switching — The same customer uses “defective” one time, “wrong item” another, “didn’t fit” another. Individually none are red flags. Combined, it’s a pattern. Almost impossible to see without aggregated data.
2. Double-dip fraud — Customer submits a return and files a chargeback for the same order. You lose the product and the money. The window to catch this is small.
3. Pre-return signals — Some fraud correlates with order-level signals: shipping address anomalies, account age, device/IP patterns, order velocity. By the time a return is requested, you’ve already shipped.
4. Threshold abuse — Customers who consistently return just under amounts that would trigger manual review. This one is particularly hard to see unless you’re looking at behavioral history.
What I built:
I got frustrated enough that I built a tool called RefundSentry. It’s an add-on for Shopify (works with Shopify native returns, Loop, AfterShip, ReturnGO — no migration required) that scores returns using 22+ fraud signals and surfaces these patterns automatically.
It’s not a replacement for your return management setup. It just adds an intelligence layer on top.
I’m currently looking for beta testers — merchants getting 50+ returns/month who want to try it free. I’m genuinely looking for feedback on what’s useful and what’s missing.
If you’re interested or have questions about any of the fraud patterns above, happy to discuss in the comments. You can also reach me at support@refundsentry.com.
Site: https://refundsentry.com