AI USE CASE
Return Fraud Detection with ML
Automatically flag suspicious return patterns to cut retail shrinkage losses.
What it is
This use case applies machine learning to transactional and return-history data to surface anomalous return behaviours, receipt fraud, wardrobing, collusion with staff, or cross-store abuse. The model runs continuously, scoring each return request at the point of service and feeding alerts to loss prevention teams. Over time, feedback loops improve precision and reduce false positives that frustrate legitimate customers.
Data you need
At least 12–24 months of itemised transaction and return records, including customer identifiers, SKUs, return reasons, store IDs, and payment methods.
Required systems
- erp
- ecommerce platform
- crm
Why it works
- Integrate the scoring API directly into the POS or returns management system so staff see alerts without leaving their workflow.
- Establish a feedback loop where investigated cases are labelled and fed back into retraining cycles at least quarterly.
- Define clear escalation playbooks so frontline staff know exactly what action to take when a return is flagged.
- Combine ML scores with human review for borderline cases to maintain customer trust while controlling losses.
How this goes wrong
- Insufficient historical return data leads to poorly calibrated models with high false-positive rates that alienate honest customers.
- Fraud patterns shift seasonally or after policy changes, causing model drift if retraining is not scheduled.
- Store staff bypass or ignore alerts because the workflow integration is cumbersome, rendering the system ineffective.
- Over-reliance on a single signal (e.g., return frequency) without contextual features produces easy-to-game rules.
Example vendors
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