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Retail loss prevention is a behavior analytics application that uses deep learning models to identify shoplifting patterns, suspicious browsing behavior, and organized retail crime.
Updated 2026-07-10 · 3 min read
Retail loss prevention is the identification of shoplifting patterns, suspicious browsing behavior, and organized retail crime that could lead to inventory shrinkage, generating automated suspicious behavior alerts for loss prevention teams.
Inventory shrinkage -- through theft, organized retail crime, or anomalies that go unnoticed -- is hard to track store by store across a multi-location chain.
Automated behavior analytics closes that gap. A retail chain needed to identify shoplifting patterns, suspicious browsing behavior, and organized retail crime across 20 store locations, and the result was a 35% reduction in inventory shrinkage.
The system identifies unusual behavior patterns, suspicious movements, and deviations from normal activity using deep learning models trained on diverse scenarios.
When a behavior pattern deviates from normal activity, the system flags it and generates an automated alert for loss prevention teams.
Retail Loss Prevention is one of BehaviorWatch's use cases, running on the same foundation as its anomaly detection feature: identifying unusual behavior patterns and deviations from normal activity using deep learning models.
Applied across 20 store locations, this approach sends automated suspicious behavior alerts to loss prevention teams, delivering a 35% reduction in inventory shrinkage.
BehaviorWatch identifies unusual behavior patterns and deviations from normal activity, including shoplifting patterns, suspicious browsing behavior, and organized retail crime.
Applied across 20 store locations for a retail chain, this approach resulted in a 35% reduction in inventory shrinkage.
The system sends an automated suspicious behavior alert to loss prevention teams.
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