AI refund fraud and fake damage claims: a support checklist for ecommerce teams

AI refund fraud is becoming a real operational risk for ecommerce teams. It does not mean every customer is suspicious. Most customers are honest. But generative AI and easy editing tools make it easier for bad actors to create fake damage photos, altered receipts, and convincing refund stories.

Support rule: never accuse first. Ask for clear evidence, check the order context, document the case, and escalate only when the signals are risky.

What AI refund fraud can look like

• Fake damaged product photos: images that show cracks, stains, tears, or missing parts that may not match the actual product.
• Edited receipts or labels: screenshots or PDFs that change dates, order numbers, or shipping details.
• Scripted refund stories: polished messages that pressure the agent for an instant refund, often with urgency or emotional language.
• Delivered-not-received abuse: repeated claims where tracking says delivered but the customer asks for replacement or refund.
• Policy manipulation: customers quoting partial policy language to push for exceptions.

The goal is not to distrust customers. The goal is to make your workflow consistent enough that genuine customers get helped quickly and risky cases are reviewed carefully.

The dangerous mistake: accusing too early

A bad support reply can turn a risky claim into a public complaint. Avoid wording like “this looks fake” or “you are trying to scam us.” Instead, use neutral verification language.

Before using this template: confirm that each stated status, approval and completed action is true for this order. Replace every placeholder, remove unavailable options and use verified estimates. A draft does not perform the action.

Hi <name>,

Thanks for sending the details. To review this correctly, could you please send the following photos:

1. One photo of the full item
2. One close-up photo of the damaged area
3. One photo of the shipping package and label

Once we have those, we can review the best next step for order <orderid>.

Best,
<agent_name>

AI refund fraud checklist for support teams

1) Check the order context first

• Order date, delivery date, and return window.
• Product type, value, and whether it is commonly abused.
• Previous refunds, replacements, chargebacks, or DNR claims.
• Shipping carrier status and delivery proof.

2) Ask for evidence in a consistent format

• Full product photo, not only a cropped close-up.
• Close-up of the issue.
• Packaging and shipping label.
• Short video when the claim is high value or unclear.
• Serial number, batch number, or SKU photo if relevant.

3) Compare the claim with the product reality

• Does the damage match how the item is built?
• Does the packaging damage match the product damage?
• Is the photo background, lighting, or scale inconsistent?
• Does the customer’s story change between messages?

4) Escalate before approving high-risk refunds

For high-value orders, repeated claims, mismatch between photos and tracking, or strong fraud signals, escalate internally before promising a refund or replacement.

Soft fraud signals to watch

• The customer demands an instant refund and refuses replacement or reshipment.
• The claim arrives immediately after delivery with no packaging photo.
• The photo is very cropped and does not show the full product.
• The customer has multiple refund, DNR, or chargeback cases.
• The supplied details conflict with a verified order or carrier record and need clarification.

These are signals, not proof. Use them to decide whether the case needs more evidence or manager review.

Protect conversion and trust: honest customers should still receive fast, polite replies. The checklist is there to reduce abuse without making good customers feel attacked.

Copy-paste macros for suspected refund abuse

Additional evidence request

Before using this template: confirm that each stated status, approval and completed action is true for this order. Replace every placeholder, remove unavailable options and use verified estimates. A draft does not perform the action.

Hi <name>,

Thanks for reporting this. We’re sorry the item did not arrive as expected.

To review the claim properly, please send:
- A full photo of the item
- A close-up photo of the damaged area
- A photo of the package and shipping label

Once we receive those, we’ll review the best resolution for order <orderid>.

Best,
<agent_name>

High-value claim escalation

Before using this template: confirm that each stated status, approval and completed action is true for this order. Replace every placeholder, remove unavailable options and use verified estimates. A draft does not perform the action.

Hi <name>,

Thanks for the details. Because this claim requires a closer review, I’m escalating it to our support lead.

We’ll check the order history, shipping details, and the evidence you provided, then follow up with the next step.

Best,
<agent_name>

Policy-based decline without accusation

Before using this template: confirm that each stated status, approval and completed action is true for this order. Replace every placeholder, remove unavailable options and use verified estimates. A draft does not perform the action.

Hi <name>,

Thanks for your patience while we reviewed order <orderid>.

Based on the information available, we’re not able to approve a refund for this claim under our current policy.

If you have additional photos or delivery documentation that may help us review the case again, please send them here.

Best,
<agent_name>

Build a safer workflow inside your helpdesk

• Create one macro for evidence requests.
• Create one internal note format for fraud review.
• Create one escalation rule for high-value or repeated claims.
• Keep all wording neutral and policy-based.
• Link the case to previous DNR, damage, return, or chargeback tickets.

A polished message, edited image, urgent request or repeated claim is not proof of fraud. Do not rely on an AI-image detector as a final decision. Ask for proportionate evidence, allow reasonable alternatives and escalate uncertain cases. Store policy does not remove applicable consumer rights.

Using this workflow with CaseKit

CaseKit drafts replies from order facts in the Shopify app. The Gmail extension is optional. Review the draft against the current order and your store policy before sending; CaseKit does not send messages, cancel orders, issue refunds or create shipments.