AI shoplifting detection applies computer vision to store cameras to highlight events that suggest theft, such as concealing items, loitering near high-value stock or not scanning products at a self-checkout. Staff receive an alert rather than watching screens.
Models analyse video for specific behaviours or mismatches between what is scanned and what is seen. Some systems link to POS data to catch "sweethearting" and scan avoidance. Alerts can go to a phone or earpiece, and footage around the event is saved for review.
Retailers use it to reduce shrinkage at self-checkouts, protect high-theft categories like liquor, health and beauty and electronics, and to support a safer response by prompting staff to offer help rather than confront. It also helps document incidents for police.
False alerts can waste time and unfairly target customers, and there are real privacy and bias concerns. Check the vendor's accuracy claims in your environment, how long video is kept, what the signage says and how staff are trained to respond. Facial recognition has stricter privacy expectations in Australia, so seek advice before using it.
It works best as one layer alongside good store layout, tags and trained staff. Pair alerts with a clear, safe response procedure, because staff safety and a respectful customer experience matter as much as the recovered stock.