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Retailers struggle with “shrink” \u2014 thefts from retailers that includes shoplifting and employee stealing \u2014 to the tune of $46.8 billion in 2017, according to the National Retail Federation<\/a>.<\/p>\n Retailers lose an average of 1.33% of sales, on average annually to shrink.<\/p>\n However, artificial intelligence<\/a> (AI) is now being integrated into technology solutions, which increases retail security and helps prevent theft.<\/p>\n <\/p>\n Video Analytics<\/strong><\/p>\n Video analytics is being transformed by AI.<\/p>\n Because AI does away with the need for pre-programmed algorithms, sensor technology can capture a large amount of metadata in real time.<\/p>\n As a result, AI allows video analytics to do much more than just alert users when a person or object has crossed a barrier; security cameras can specifically identify a person entering a store or department through facial recognition software, for example.<\/p>\n Retailers can even intercept known shoplifters by using cameras running analytics<\/a> software.<\/p>\n The devices quickly and efficiently scan approaching vehicles and their license plates and then cross-reference that information with persons of interest to alert security teams.<\/p>\n By applying advanced video and data analytics to existing POS video and data streams, retailers can also curb loss by identifying the products that go without scanning at check out.<\/p>\n The electronic point-of-sale (POS) system can be connected to the surveillance system to track the entire POS activity and identify potential irregular operations.<\/p>\n Two other major causes of retail shrink result from “sweethearting” and self-checkout.<\/p>\n “Sweethearting”\u2014 when a cashier allows a customer to pass through the register lane without paying for a product \u2014 can include covering barcodes, stacking items one on top of the other, and skipping the scanner and directly bagging the merchandise.<\/p>\n AI fights sweethearting by applying advanced computer vision algorithms to the existing camera feeds, to see and analyze what is happening at the checkout and track each item.<\/p>\n It associates every item within a transaction to the transactional data feed from the POS system to flag anomalies.<\/p>\n Self-checkout fraud includes customers bypassing the scanner, tampering with price codes and leaving merchandise in the basket.<\/p>\n An intelligent computer vision system automatically reconciles what should be on the transaction receipt and the items being scanned in the video.<\/p>\n <\/p>\n Internet of Things (IoT) <\/strong><\/p>\n The IoT connects devices<\/a> as varied as smartphones to wearable devices to products on the retail shelf.<\/p>\n As the use of IoT increases at a business with the expansion of sensors and devices \u2014 including from security-related devices \u2014 the volume of data being created will increase rapidly, which will require AI-enabled software and machine learning to analyze that information meaningfully.<\/p>\n For example, some businesses are implementing wearable devices for employees that can learn habits and create patterns based on usage.<\/p>\n An employee who strays from a regular routine sends a flag to security, which may then choose to investigate.<\/p>\n Despite Hollywood’s eagerness to portray AI and machine learning as dominated by robots, in reality, machine learning is a part of everyday technology.<\/p>\n Its ability to synthesize data and help humans draw conclusions makes it a tool with a bright future in retail.<\/p>\n","protected":false},"excerpt":{"rendered":" Retailers struggle with “shrink” \u2014 thefts from retailers that includes shoplifting and employee stealing \u2014 to the tune of $46.8 billion in 2017, according to the National Retail Federation. Retailers lose an average of 1.33% of sales, on … Continue reading