AI Refund and Return Agent for Fashion Retailer

Key results

48%

of refund tickets resolved automatically within the first 60 days

37%

reduction in the refund- and return-related support backlog

99%

policy-match accuracy across audited low-risk refund decisions

42%

faster resolution on cases, with order, payment, and policy context pre-filled

retail luxury store

Summary

A growing U.S. online fashion and accessories retailer engaged WiserBrand to review its support operations and identify where AI could eliminate repetitive work without weakening control over customer decisions.
The review pointed to refunds, returns, and billing disputes as the highest-impact area. These tickets came in frequently, followed a repeatable decision path, and took time away from higher-value customer issues.
Cooperation Period
Ongoing
Location
USA
Industry
Retail
Service Provided
AI Automation
fashion retailer case

Business Challenge

Every refund and return request followed the same manual process. For each ticket, a support agent had to:

  • Open the order and confirm payment status
  • Review the customer’s history
  • Check the request against the refund and return policy
  • Update internal systems
  • Write a policy-based response by hand

Most cases were not complex. They were repetitive, time-sensitive, and easy to delay when ticket volume increased.

What We Did

WiserBrand designed a refund and return agent that works through the queue the way an experienced support rep would, with clear limits around what it can and cannot decide.

For each incoming ticket, the agent:

  • 1

    Classifies the request type

  • 2

    Pulls the matching order and customer record

  • 3

    Confirms payment status

  • 4

    Checks the case against the approved refund and return policy

  • 5

    Completes eligible low-risk, in-policy actions automatically

  • 6

    Sends the customer a policy-based response

Project Results

The agent handles the assembly, verification, and routine execution. Consequential decisions stay with the team.

Routine refund and return requests moved out of the manual support queue.
Straightforward cases were resolved faster, without waiting behind complex tickets.
Support agents spent less time checking orders, payments, and policies by hand.
Escalated cases reached the team already prepared, with context and next-step guidance.
Refund decisions became more consistent because low-risk cases followed the same approved policy logic.
Managers gained a clearer review trail for automated decisions and escalations.