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Retail verified Verified Outcome TRL 9

Computer Vision for Loss Prevention (Self-Checkout)

domain Client: A major US grocery retailer handshake Provider: Everseen / Zebra schedule Deploy: Q2 2023 (Expansion)
91 Impact
Enterprise Ready
Evidence Score: 5/10
Strength: High

Executive Summary

ANALYST: COI RESEARCH

To combat the rising rate of 'shrink' (theft/error) at self-checkout terminals, the retailer integrated computer vision software into the camera feed above the scanner. The system detects 'non-scans' (items passed around the scanner) and 'ticket switching' (scanning a cheap item while bagging an expensive one) in real-time, pausing the transaction for staff intervention.

rate_review Analyst Verdict

"A critical defensive technology. As self-checkout adoption grows, so does the opportunity for theft. This 'Nudge Tech' is highly effective because it corrects honest mistakes without confrontation and deters theft by showing the customer they are being watched (via a 'Did you miss this?' screen)."

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Full Audit Report Available Includes Risk Register, Technical Specs & Compliance Data.

warning The Challenge

Self-checkout terminals rely on the honor system. Malicious actors exploit this by 'fake scanning' items or swapping barcodes. Honest customers often accidentally miss scans. Traditional weight scales were too sensitive and caused frustrating 'unexpected item in bagging area' alerts.

psychology The Solution

The solution overlays an AI vision model on the existing overhead security camera. It correlates the visual motion of a hand moving an object with the POS transaction log. If a hand moves a steak into the bag but the POS registers a banana (or nothing), the system freezes the screen.

settings_suggest Technical & Deployment Specs

Integrations
NCR/Toshiba POS
Deployment Model
Edge AI
Data Classification
Video Feed / POS Data
Estimated TCO / ROI
Medium
POC Summary (2019-01-01 to 2020-01-01)

"Initial pilot in high-theft urban locations."

shield Risk Register & Mitigation

Risk Factor Severity Mitigation Strategy
Customer Friction Medium Soft-touch UI ('It looks like an item wasn't scanned') rather than accusation.
Profiling Bias High Algorithm analyzes motion/objects, not faces/demographics.

trending_up Impact Trajectory

Audited value realization curve

Intervention in >1,000 incidents per week per store Verified Outcome
Primary KPIReduction in inventory shrinkage
Audit CycleDecrease in false-positive security alerts

policy Compliance & Gov

  • Standards: GDPR (Video Surveillance)
  • Maturity (TRL): 9
  • Evidence Score: 5/10
  • Data Class: Video Feed / POS Data

folder_shared Verified Assets

description
Verified Case Study
PDF • Version 1
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Technical Audit
PDF • Audited
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Security Architecture

The "Blind Verification" Protocol

How we verified these outcomes for A major US grocery retailer without exposing sensitive IP or identities.

Private
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1. Raw Evidence

Audit ID: #PRIV-823
Evidence: Direct SQL Logs
Public
public

2. Verified Asset

Outcome: Verified
Ref ID: #COI-823

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