AI USE CASE
Self-Checkout Fraud Detection Vision
Detect scan-skipping and product switching at self-checkout using real-time computer vision.
What it is
Computer vision models monitor self-checkout lanes to identify scan-skipping, product substitution, and concealment in real-time, triggering staff alerts before the customer completes payment. The system reduces reliance on manual attendants while improving detection accuracy compared to weight-based systems alone. Integration with existing POS and CCTV infrastructure keeps deployment complexity manageable.
Data you need
Video feeds from self-checkout lane cameras, paired with POS transaction logs showing item scans, quantities, and prices.
Required systems
- ecommerce platform
Why it works
- High-quality, well-positioned cameras covering the scanning zone and bagging area from multiple angles.
- Continuous model retraining on store-specific product catalogue and fraud patterns to maintain accuracy.
- Clear escalation workflows so staff know exactly when and how to intervene on triggered alerts.
- Regular review of false-positive and false-negative rates with a feedback loop back to model improvement.
How this goes wrong
- High false-positive rates cause unnecessary customer interruptions and erode trust in the system.
- Poor camera placement or low-resolution feeds degrade model accuracy below acceptable thresholds.
- Model fails to generalise across product categories with similar shapes or packaging, missing novel fraud patterns.
- Staff ignore alerts due to alert fatigue, rendering the system operationally ineffective.
Example vendors
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