How Retailers Can Implement Self-Checkout Monitoring
The use of self-checkout monitoring surged during the pandemic, helping retailers navigate staffing shortages and enabling them to limit physical contact with customers. However, self-checkout systems can be vulnerable to fraud by customers, such as using counterfeit coupons or gift cards or ringing up an inexpensive item but taking something more expensive.
Self-checkout monitoring systems use machine learning algorithms to analyze transactions and detect fraudulent activity. The system can then block the transaction or alert staff to act.
Enhanced Video Surveillance Can Support Retail
Video surveillance has been a staple of retail security for decades. “The minute someone can be identified or they know they’re on camera, the rates of theft go way down, and that includes for employees too,” says Witcher.
Today’s enhanced iterations of video surveillance only augment that effect. New technology makes it easier to spot specific individuals and behavior in real time. High-resolution cameras and artificial intelligence-powered analytics and object detection are all available in today’s enhanced video surveillance systems. These technologies allow retailers to monitor their stores and detect unusual behavior, such as customers lingering near high-ticket items or moving in restricted areas.
Some systems are even specially configured to detect certain common retail theft tactics. For example, when a transaction is entered by a store associate without a customer being physically present, it’s often a sign the transaction is fraudulent, but that can be difficult to detect by humans in real time. An AI-powered enhanced video system is designed to take note of such transactions and alert the store’s loss prevention specialists.