UST Loss Prevention Elevates Retail Resilience Against Shrinkage

UST Loss Prevention Solution on AWS is an AI and computer vision–based retail loss prevention platform that helps retailers reduce shrink, protect revenue, and improve store safety across self-checkout, POS, and in-aisle experiences. The solution analyzes live video and transaction data to detect theft, fraud, and suspicious behavior in real time, sending alerts to store teams so they can intervene quickly while maintaining a frictionless experience for honest customers.

The solution combines behavioral analysis, SKU-level checks, and pattern recognition to identify high-risk scenarios such as fake scans, basket mismatches, unpaid carts, and bottom-of-basket items. It also monitors activity in aisles and around high-value categories like drugs and cosmetics to detect pilferage or unusual behavior. Running on AWS, the UST Loss Prevention Solution scales across hundreds or thousands of locations and integrates with existing cameras, self-checkout terminals, and POS systems, avoiding costly rip-and-replace projects.

UST delivers this loss prevention capability as a managed service on AWS. UST teams configure and tune AI and computer vision models, monitor performance, and provide continuous improvements based on store feedback and new fraud patterns. Loss prevention, asset protection, and operations teams gain a modern, data-driven loss prevention platform without needing to build or maintain complex AI infrastructure.

Key Features

• AI and computer vision–based loss detection: Analyzes live video feeds and transaction data using AI and computer vision models to detect fake scans, non-scanned items, basket mismatches, unpaid transactions, bottom-of-basket SKUs, and other loss events in real time.
• Self-checkout and POS monitoring: Monitors self-checkout and manned POS lanes for behaviors such as mimicked scanning, barcode swapping, multiple items scanned as one, and skipped items, generating real-time alerts for store associates at the checkout area.
• In-aisle and high-value product surveillance: Tracks movement and behavior in aisles and around high-value drug and cosmetic products to detect lingering, concealment, or patterns associated with pilferage, helping protect categories most at risk for shrink.
• Automated, real-time alerts and workflows: Delivers configurable alerts to handheld devices, dashboards, or store communication systems so associates can respond quickly. Supports evidence capture and workflow steps to streamline investigations and follow-up.
• Data analytics and pattern insights: Aggregates loss events, alerts, and behavioral data to reveal patterns by store, time, product, and customer behavior. Helps loss prevention and asset protection teams refine policies, staffing, and store layouts.
• Integration with existing retail systems and cameras: Works with retailers’ existing camera networks, SCO devices, and POS systems via APIs and integration connectors, reducing deployment time and cost.
• Scalable, secure architecture on AWS: Leverages AWS services for video ingest, storage, analytics, and machine learning, with built-in encryption, identity and access management, and governance to support enterprise security and compliance needs.

Key Benefits

• Reduce shrink and protect margins: Prevents and detects theft and fraud at SCO, POS, and in aisles, helping retailers materially reduce shrink and protect revenue that would otherwise be lost.
• Improve store safety and security: Provides continuous, automated monitoring and faster detection of high-risk behavior, contributing to a safer environment for shoppers and store associates.
• Increase operational efficiency: Automates routine monitoring tasks, allowing store teams to focus on customers and high-value activities rather than manually watching camera feeds.
• Enhance customer experience: Supports frictionless self-checkout and shopping journeys by enabling targeted, evidence-based interventions instead of blanket security measures that frustrate honest customers.
• Scale modern loss prevention across locations: Standardizes AI-driven loss prevention practices and reporting across diverse store formats, from convenience stores to hypermarkets, using a common platform on AWS.
• Gain data-driven insights for LP strategy: Transforms loss events and behavioral data into insights that inform merchandising, staffing, layout decisions, and long-term loss prevention strategy.
• Accelerate time to value with managed services: Delivers loss prevention as a managed service on AWS so retailers can adopt AI and computer vision without building in-house data science teams or complex infrastructure.