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What Role Can Computer Vision and AI Play in Loss Prevention?


Retail shop margins are already under great stress due to online competition. In addition, a retailer’s bottom line can be damaged due to inventory shortages compared to existing system records, known as “shrinkage.” The reduction is the loss of inventory stock due to theft, employee theft and other reasons. This can happen on the sales floor, at checkout or even at the exit of the store.

Declining numbers leave traders vulnerable, as higher declining amounts mean lower profits. The National Retail Security Survey 2021 found a long-term decline in 2021, accounting for 1.6% of a retailer’s bottom line.

In addition, in a post-COVID era, the desire to digitize the store and provide even more hassle-free and contactless self-service-such as self-checkout alternatives – benefits customers and sellers. However, with new developments comes the possibility of new avenues for fraud.

A Closer Look at the Recession

The basic purpose of loss prevention systems is to reduce losses. Numerous innovations in sales floor monitoring, including cameras, EAS alerts and RFID-enabled loss prevention have been made to monitor products and staff. However, this hinders the capacity of retailers to respond in real time.

In addition, there has been an increase in employee theft, sometimes called “sweethearting,” where employees gift products to friends and family without them being charged or charged for products that are cheaper than the items purchased. Many types of retail theft can occur at checkout, depending on the cooperation of an outside shoplifter and employee of the POS system.

And at self-checkout, the chance of theft increased. Products don’t have to be hidden inside shoppers ’clothes or placed in a bag in the middle of the store aisle. They can be left open in a bag as customers move items across the scanner while they avoid scanning the most valuable items.

Reduce Checkout Fraud with Computer Vision and AI

As a result of these increasing challenges, retailers are investing in new technologies to combat the retail shrink with better solutions to avoid losing the store front. There are new solutions available that use AI and computer vision to help retailers fight shrinkage, reduce theft and ultimately better manage their inventory. They also provide an added benefit of improving the customer experience by preventing unexpected stockouts from occurring as a result of unexpected thefts.

Checkout fraud, including staffed registers and self-checkout, requires the aggregation of data from object level tracking with computer and POS visibility. Comparing object level counts to POS -generated counts can help colleagues discover fraud and act in real time. AI and computer vision help retailers improve checkout processes by making them smarter, reducing theft and improving inventory control.

Here’s an example of what it looks like: a solution could add a camera to existing checkout lanes and use AI to check the number of items scanned, helping to identify and prevent stealing from shoppers. While a customer is scanning an object in the POS system, the camera detects the objects being scanned. It makes the total item count and then sends the number to the integrated POS system. The video camera is tied to the POS system and once the items are scanned, the POS system will count the total number of counts made by the camera.

If the two numbers do not match, a system can be set up to generate a dashboard, POS alert or a mobile alert sent to management staff as a notification for potential theft or incorrect billing. . It allows store associates to intervene or assist with transactions before they are processed, allowing the manager to manually process transactions.

With computer vision and AI, retailers have the opportunity to rectify the situation without having to accuse customers of theft. Store associates can inform shoppers about system differences and give them a chance to pay for unscanned items. In the long run, the process will help to change behavior, which will lead to reduced reductions and cost savings.

In the National Retail Security Survey 2021, it was reported that retailers are actively investing in technology to combat the rise in organized retail crime. Investing in a loss prevention solution that incorporates computer vision and AI not only helps save revenue drop, but also directly and positively impacts the customer experience. This is a win-win for retailers and customers.


Rohan Sanil is the CEO and Co-founder of Deep North. He has over two decades of product, business and business leadership in the video analytics space. He was previously founded by Akiira Media Systems, Atstream Networks and Tri-Cad, where he was instrumental in raising capital and launching products. Prior to co-founding Deep North, he led MetricStream product management and business development at Cambridge Solutions (now part of DXC Technology), building key partnerships with marquee customers such as Polycom, Broadcom, Virgin Mobile, Cisco and Oracle. Sanil holds an MS Degree in Management Science from the University of Dayton, Ohio, and a BS in Mechanical Engineering from Karnataka University, India.



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