Crack down on retail inventory shrinkage with computer vision

Retail theft, which contributes to inventory shrinkage, costs retailers $132 billion in 2024. As shoplifting tactics become more sophisticated, retailers are under pressure to curb losses, reduce shrinkage, and maintain profitability.

To meet this challenge, retailers are turning to cutting-edge technology, including computer vision—advanced AI models that merge image recognition with natural language processing. Computer vision offers a powerful approach to theft prevention, helping retailers detect suspicious behavior, enhance security, and reduce inventory shrinkage.

Retail inventory shrinkage hurts retailers in many ways

Retail inventory shrinkage occurs when the amount of stock recorded in a retailer’s system doesn’t match what’s physically available. This discrepancy leads to lost revenue, operational inefficiencies, and higher costs for businesses and consumers alike. Shrinkage stems from several sources, including theft, administrative errors, vendor fraud, and product damage, making it one of the most persistent challenges in retail.

Theft, both from customers and employees, remains a major reason for why shrinkage occurs. Shoplifting alone accounts for more a third of all losses, with offenders ranging from casual opportunists to organized retail crime rings that systematically steal high-value goods.

Employee theft, with some workers stealing cash, manipulating returns, or bypassing scanning procedures to benefit friends or family, is a threat. Vendor fraud, such as inaccurate shipments or false invoicing, adds another layer of loss. Meanwhile, administrative errors—miscounts, pricing mistakes, or data entry issues—can inflate shrinkage numbers without any actual theft occurring.

As retail inventory shrinkage continues to vex retailers, they are under pressure to adopt new strategies to protect their inventory and bottom line.

Computer vision is a powerful tool to fight retail inventory shrinkage

Traditional surveillance systems rely on fixed cameras and human monitoring to identify suspicious activity, but they often miss critical moments or fail to detect more sophisticated crimes. Computer vision improves this process by bringing AI-driven real-time analysis into the mix. By integrating intelligent cameras that process visual data instantly, retailers can flag suspicious behavior.  Computer vision uncovers subtle patterns Computer vision can detect subtle patterns, like a customer lingering in restricted areas or discreetly concealing merchandise. It can also interpret facial expressions, gestures, and other behavioral cues that might indicate an attempted theft.

With these insights, security teams can respond faster and more effectively. A 2024 implementation of computer vision in European electronics stores reduced concealment-based theft by 41% through real-time alerts when customers:

• Repeatedly glanced at security mirrors while handling merchandise.

• Used outer garments to obscure product movements.

• Lingered near emergency exits with unpurchased items.

Facial expression recognition enhances detection accuracy by correlating micro-expressions like suppressed smiles or increased blink rates with concealed items, achieving 89% prediction accuracy in controlled trials.

Computer vision fights loss from the delivery dock to the self-checkout station

Retailers are also deploying computer vision at self-checkout stations, which are vulnerable to various fraudulent activities, including item misrepresentation or non-scanning.

Computer vision can monitor the stations to help ensure that all items are scanned correctly. For example, AI-powered cameras can detect when a product is placed in a bag without being scanned, immediately notifying staff to intervene. This technology has been implemented in stores to enhance the accuracy of self-checkout systems and reduce associated shrinkage.

When it comes to stopping employee theft, computer vision can track inventory movement from delivery docks to sales floors using fish-eye cameras with 99.8% item recognition accuracy Computer vision detects shrinkage occurring during overnight restocking, the loss differential between supervised and unsupervised storage areas, and more. Through the use of hand movement analytics, computer vision can also identify types of employee theft such as fake product returns using stolen merchandise.

Beyond real-time monitoring, computer vision is also transforming how retailers handle repeat offenders. By using advanced facial recognition, computer vision can match individuals against databases of known shoplifters or fraudsters.

If a flagged individual enters the store, security personnel may receive instant alerts, allowing them to take preventative measures, whether that’s keeping a closer watch or involving law enforcement. This proactive approach not only deters crime but also builds a safer shopping environment for both customers and employees.

The use of cameras across multiple stores can be effective in fighting repeat offenders involved in organized retail crime. How? by analyzing variables such as recurring vehicle license plates in parking logs, synchronized entry/exit timestamps across locations, and consistently targeted products.

By integrating point-of-sale data with facial recognition, computer vision can identify known offenders (from police databases) attempting to test security protocols at different stores.

Computer vision does more than fight crime

Retail inventory shrinkage is a bigger problem than theft. It’s also about errors and inefficiencies in inventory management. Computer vision strengthens loss prevention efforts by automatically tracking stock levels, monitoring product movement, and detecting discrepancies in real time.

Cameras powered by AI can identify when items are misplaced, removed without purchase, or even damaged, helping to ensure that potential losses are addressed before they escalate. This level of visibility helps retailers maintain tighter control over their inventory.

Computer vision delivers strong benefits

One of the most compelling advantages of computer vision is its predictive capability. By analyzing trends in crime data—such as peak theft hours, high-risk locations, and recurring patterns—computer vision can anticipate where and when incidents are likely to occur. Armed with these insights, retailers can optimize security measures, adjusting staffing or surveillance coverage to match risk levels.

While security is the primary focus, the benefits of computer vision extend beyond theft prevention. By reducing crime, retailers create a more inviting and stress-free environment for customers. Shoppers are more likely to return to stores where they feel safe, reinforcing brand trust and loyalty.

And AI-powered visual analysis can enhance the shopping experience by recognizing customer preferences and behaviors, enabling stores to deliver personalized recommendations and promotions. What started as a tool for deterring theft becomes an asset for strengthening customer relationships and driving long-term growth.

A frontier AI data foundry makes computer vision more effective

Computer vision becomes even more powerful when combined with other AI-driven technologies such as a frontier AI data foundry platform. Such a platform consists of a centralized system designed to manage, process, and analyze data from diverse sources, including computer vision. A frontier AI data foundry platform:

• Delivers actionable insights derived from various data sources, such as computer vision, which is needed to observe customer journeys in the store.

• Provide notifications and analytics, which are critical for your store to act on AI-generated insights in a timely manner.

Among other benefits, a frontier AI data foundry makes it possible for retailers to integrate predictive models that identify vulnerabilities in store layouts, such as blind spots that encourage theft or shelving arrangements that make shoplifting easier.

With this intelligence, your business can refine store designs to discourage criminal activity before it even happens, turning loss prevention into a proactive strategy rather than a reactive one.

Create a shrinkage prevention plan

To maximize the benefits of AI to fight inventory shrinkage, you need a plan. A well-structured loss prevention strategy is your most effective approach to minimizing losses in your retail shop. This is the only way to successfully tackle issues such as shoplifting and employee theft. Your plan should map out how computer vision can best assist you in the context of several other steps, which we’ll discuss next.

Deploy item tracking and inventory management technology

Monitoring items is straightforward and can assist in identifying where a product went missing, whether in the warehouse or on the sales floor. Attaching RFID tags to products allows for tracking via radio frequencies, enabling automatic tracking of inventory and stock levels.

Using inventory management software instead of relying on notebooks or spreadsheets can reduce the likelihood of human error in stock management; doing so also provides you with tools, data, and reports to manage inventory more effectively and maximize your investment returns.

Count inventory frequently

While conducting a complete inventory count may take up too much time to do on a regular basis, you might consider performing partial inventory counts periodically instead. With technologies from vendors like Shopify, it is possible to compare the physical stock levels in your store with the inventory levels recorded in your point-of-sale system, allowing you to reconcile and update recorded stock levels when discrepancies are found.

Train employees

Training employees is one of the most effective ways to reduce retail shrinkage by helping to ensure store associates are equipped to prevent both accidental losses and deliberate theft. A well-trained team understands proper inventory handling procedures, minimizing errors in stock management that can contribute to shrinkage.

Employees should also be educated on how to recognize suspicious behavior, from subtle shoplifting tactics and fraud to signs of organized retail crime, so that they can take appropriate action without escalating situations unnecessarily.

Start succeeding with computer vision

Your game plan for fighting inventory shrinkage needs to encompass many more elements beyond the scope of this blog post. But the bottom line is that computer vision offers a promising approach to addressing the rising problem of theft and crime in retail stores. The outlook for retail security appears more optimistic than ever.

A frontier AI data foundry platform like Centific’s can help you craft workplace safety solutions tailored to your business needs to help reduce incidents. With a focus on foresight and prevention, security moves beyond surveillance to proactively anticipate risks before they happen.

Learn how businesses are transforming retail with AI-driven solutions.