Retail loss prevention AI on existing cameras
· Updated · Slinai
Key facts
| US shrink context | NRF National Retail Security Survey has reported US retail shrinkage at $112.1 billion in a recent survey year — context, not an India forecast |
|---|---|
| India context | Industry estimates often cite 2–3% of revenue shrink for fashion and electronics — treat as a planning range, not a census |
| What AI flags | Concealment gestures, high-shrink dwell, bag stuffing, POS exceptions, after-hours doors |
| Evidence | Clip-backed alerts; POS-linked register review |
| Solution | Loss prevention use-case page — plus compare for vendor tables |
Loss prevention on CCTV used to mean rewind after the stock-take. Retail loss prevention AI means a clip while the bay is still open.
Slinai runs this on existing cameras: floor behaviour, register exceptions via data & integrations, and after-hours doors. The playbook page is loss prevention; the store hub is retail video analytics.
This post is about what to detect, what not to claim, and how to review — not a promise that shrink goes to zero.
The two clocks of shrink
Floor clock: concealment, booster bags, unusual dwell at accessories or blades, exit without a basket that matches the walk. Cameras already watch these aisles for “security.” Analytics makes the watch continuous.
Register clock: no-sale opens, void-after-tender, refunds, sweethearting. The NRF’s National Retail Security Survey has put US retail shrink at $112.1 billion in a recent survey year — useful as scale context, not as your P&L. Indian fashion and electronics operators often talk about 2–3% of revenue as a shrink planning range; your audit is the number that matters.
Neither clock is solved by a louder NVR beep.
POS + video is the adult version
Conversion analytics already needs a POS join. LP reuses it.
| Exception | Video question | Why the join matters |
|---|---|---|
| No-sale | Was the drawer opened with a customer, or empty? | Training vs theft |
| Void after tender | Did the goods leave? | Classic sweethearting pattern |
| Refund spike | Same face-on camera, same SKU story | Policy vs abuse |
| Till variance | Which hour, which till | Night audit becomes a clip list |
Without POS, “cashier fraud AI” is a person standing near a till. With POS, it is a transaction ID + 20 seconds of tape.
After hours and the perimeter
Most “analytics” demos are daytime retail. A lot of loss is a door at 01:14.
Use the same platform for armed/disarmed logic, loitering at the shutter, and staff-only hours. That is physical security as well as LP. Slinai’s video alarms path is clip-first so a guard company is not chasing wind-blown bags.
False positives, dignity, and process
- Do not auto-detain from a score.
- Do send verified alerts to a trained reviewer.
- Do keep an audit trail — who watched the clip (security).
- Do not turn on facial recognition “to be thorough.” Behaviour + POS is enough for most programmes.
Queues interact with LP: a jammed line (queue analytics) is cover for walkouts and for sloppy till work. Staffing is loss prevention.
How to shop vendors (short)
Use compare for Spot AI, Verkada, and peers, and the buyer’s guide for TCO and residency. For LP specifically, demand:
- Your footage, not a US grocery reel.
- POS exception types you actually use.
- False-positive handling (verification, not 200 emails).
- Existing CCTV — no camera tax to “enable AI.”
Book a demo with one high-shrink bay camera and one register camera. That pair tells you if the product is operations or theatre.
Common questions
Can AI stop shoplifting by itself?
No. It shortens time-to-awareness and gives clip evidence. Intervention policy (greet, delay, call security) is yours. Treat detections as leads, not verdicts.
What about cashier fraud?
Join POS exceptions — no-sale, void-after-tender, sweethearting patterns — to the register camera. Video without POS is guesswork; POS without video is a spreadsheet.
Will this accuse innocent shoppers?
False positives happen. That is why Slinai verifies events and sends clips for a human to decide. Do not auto-punish from a model score.
Is facial recognition required?
No. Behavioural patterns and POS correlation are enough for most store LP programmes. Slinai does not enable facial recognition by default.
How do we compare vendors?
Ask for false-positive rates on your footage, POS join, and alert path (WhatsApp vs email dump). Start with the compare hub and the India buyer’s guide.
Related pages
Guides in this cluster point to live product, industry and use-case pages.
Related guides
Shrink and after-hours security in dark stores
Dark store shrink analytics flags high-value cages, irregular bags and after-hours doors on existing CCTV — clip proof, not a month-end stock-take surprise.
Retail queue analytics and checkout wait time
Retail queue analytics measures checkout wait time on existing cameras and alerts managers before lines drive walkaways and abandoned baskets.
Retail conversion funnel: visitors to billed
Retail conversion funnel analytics maps visitors to billed on CCTV plus POS. Find drop-off by zone, hour and store — not sales guesswork.