Notifications
Clear all
Topic starter
11/07/2026 2:01 pm
TL;DR: Ecommerce return optimization is now about balancing customer experience with fraud prevention as returns cost merchants about $850 billion in 2025 and 9% of that activity is tied to fraud, according to the National Retail Federation and Happy Returns. The real shift is that AI shopping agents can scale returns abuse faster than legacy review processes can detect it.
NHIMG editorial — based on content published by Signifyd: The complete guide to ecommerce return optimization in 2026
By the numbers:
- Returns cost merchants about $850 billion in 2025, with 9% of that activity tied to fraud.
- The cost of processing an online return averages 21% of an order’s value, according to a Pitney Bowes BOXpoll survey of digital and omnichannel brands.
- Signifyd’s work with online retailers found that 23% of consumers who receive an instant refund will soon buy something again from that business.
Questions worth separating out
Q: How should security teams reduce return fraud without hurting legitimate customers?
A: Use risk-based decisioning rather than blanket restrictions.
Q: Why do AI shopping agents change return fraud governance?
A: AI shopping agents can generate high volumes of coordinated return requests, refund claims, and account activity far faster than manual review can absorb.
Q: What signals indicate return controls are not working?
A: Look for rising repeat returns, high-value item abuse, repeated refund claims, unusual regional spikes, and mismatches between return reasons and item condition.
Practitioner guidance
- Implement risk-scored refund decisions Use account history, return frequency, item value, and behavioural signals to decide when instant refunds are appropriate and when inspection or verification is required.
- Link returns to trusted customer identity Tie every return request to a verified purchase record, account history, and device or session patterns so repeat abuse can be detected across identities and channels.
- Separate product issues from policy abuse Analyse return reasons, sizing complaints, and item-condition data to distinguish operational defects from abuse patterns before changing policy or tightening controls.
What's in the full article
Signifyd's full guide covers the operational detail this post intentionally leaves for the source:
- A full breakdown of return fraud types, including serial returning, bracketing, bricking, empty-box scams, and switch fraud
- The policy examples behind conditional returns, restocking fees, and pre-paid label tracking
- Operational details on how instant refunds are applied in low-risk cases and when inspection is required
- Step-by-step examples of how returns data is used to detect abuse and improve retention
👉 Read Signifyd’s complete guide to ecommerce return optimization in 2026 →
Agentic returns abuse and return fraud: what teams need to act on?
Explore further
11/07/2026 2:11 pm
Agentic returns abuse is a governance problem, not just a fraud pattern. Once AI shopping agents can generate, coordinate, and repeat return actions at scale, the control issue becomes identity assurance across many low-friction interactions. That changes the returns program from one-off review into lifecycle governance for accounts, devices, and behavioural trust. Practitioners should treat automated commerce agents as a new class of risk-bearing identity.
A question worth separating out:
Q: When should merchants require inspection before issuing a refund?
A: Require inspection when the item is high value, prone to substitution or tampering, or associated with repeated abuse from the same customer or account cluster. Inspection is most useful when physical condition must be confirmed before money leaves the merchant.
👉 Read our full editorial: Ecommerce return optimization is becoming a fraud control problem
