Field guide No. 06 · Returns
Return fraud detection
Recover margin without punishing honest customers.
Return-risk score · tunable threshold · case review
Return fraud detection
Return-risk score · tunable threshold · case review
The problem
A share of returns is abuse or fraud: wardrobing, empty-box or wrong-item returns, receipt fraud, serial returners and organized retail crime. It quietly erodes margin and hides inside honest volume.
The approach
Score every return in real time, then match the response to the risk: approve instantly, ask for an in-store return, hold for inspection or adjust terms. Computer vision verifies what came back.
How it works
Watch-outs
- False positives are the expensive error. Wrongly flagging a good customer loses them.
- Calibrate per category and channel. One threshold for everything is wrong everywhere.
- Explainability matters for store staff and for disputes.
Accelerators and stack
- Classical anomaly-detection and fraud ML (same family as payment fraud)
- Computer vision for return verification
- Loss-prevention and returns-management platforms
What it takes to start
Order and return history with outcomes, any known fraud labels, and a loss-prevention partner. Pilot scoring in six to eight weeks.
Pure, measurable margin recovery. Track the false-positive rate as closely as the fraud caught.
In short
Questions
Will this upset good customers?
Only if the threshold is wrong. That is why calibration, monitoring and a fast human override are part of the build.
Do loss-prevention teams use this?
They are usually the sponsor. The model gives them priorities instead of hunches.
Next step
Bring one number you want to move.
Thirty minutes with the founder. You'll leave with a straight answer on whether AI is the right tool for it.
AI is… the Future.