Shoplifting Surge: Stores Fight Back with High-Tech Surveillance

Retailers are bolstering their security measures in response to a surge in shoplifting incidents that are hitting their bottom lines hard.

Beyond the usual CCTV cameras and security guards, businesses are now embracing artificial intelligence, data analytics, and automated security systems to tackle everything from petty theft to organized retail crime.

“As retail logistics adapts to meet the booming e-commerce demands and rising customer expectations, distribution centers worldwide are prioritizing safety, security, and efficiency,” explains Louise Hobroh, Global Sales Marketing Manager at Axis Communications, in a post. “Network video, powered by AI and analytics, is helping these centers achieve just that.”

Retail’s billion-dollar theft problem

Retail theft has reached alarming levels. The National Retail Federation (NRF) in the US estimates that shrink—which includes shoplifting, employee theft, and fraud—cost the industry a staggering $112.1 billion in 2022. That’s a significant jump from the $93.9 billion lost in 2021.

Retailers are pointing to this rise in theft as a major reason for declining profit margins and even store closures. Several big names, from large chains to department stores, have cited shoplifting as a key factor driving up their operational costs.

In some urban areas, stores are now forced to keep commonly stolen items, like personal care products and electronics, locked away behind cases, requiring customers to ask for assistance to make a purchase. The increase in organized retail crime (ORC) has also caught the attention of law enforcement, with the Department of Homeland Security (DHS) launching a nationwide initiative to dismantle the criminal networks behind these thefts.

AI-powered video surveillance: the first line of defense

Artificial intelligence-powered video surveillance has become a crucial tool in preventing theft. AI-driven systems can analyze behavior patterns in real time, spotting suspicious activities like loitering, concealed merchandise, or coordinated movements among groups of people.

These analytical tools, integrated with existing IP-based security cameras, can immediately alert security personnel when they detect signs of theft. Unlike traditional surveillance systems that require constant human monitoring, AI-powered systems automate the detection process, reducing the reliance on security personnel and enabling a more proactive approach to loss prevention.

Retailers using AI analytics have reported higher theft detection rates, particularly in high-risk areas like self-checkout stations and fitting rooms. AI also allows for forensic video analysis, helping loss prevention teams quickly review past incidents, identify suspects, and track movement patterns across multiple stores.

Self-checkout theft and automated monitoring

Self-checkout theft has become a major headache for retailers, with some customers exploiting vulnerabilities by switching barcodes or simply not scanning items. To counter this, retailers are using AI-based self-checkout monitoring systems that analyze scanning behavior, detect anomalies, and trigger alerts when they spot discrepancies.

Some systems use computer vision to ensure that the scanned items match what’s in the shopping cart, while others use weight sensors to cross-reference scanned items with the expected weight on the bagging platform.

While these measures are effective in reducing fraud, they have also raised concerns about false positives, with some customers complaining about being wrongly accused of theft. Retailers are working to fine-tune their AI models to minimize errors while maintaining the efficiency of self-checkout operations.

Electronic article surveillance and RFID systems

Electronic Article Surveillance (EAS) and Radio Frequency Identification (RFID) technologies remain essential tools for deterring theft. EAS systems use tags attached to merchandise that trigger alarms if they are removed from stores without being deactivated, while RFID tags allow for real-time tracking of products throughout the store.

Retailers using RFID for theft prevention have also integrated it into their inventory management, reducing stock discrepancies and improving supply chain visibility. RFID adoption has increased in recent years due to falling costs and improved accuracy, making it a practical tool for retailers seeking both security and operational efficiency.

In addition to loss prevention, RFID helps improve stock replenishment, ensuring that popular products remain available on shelves without unnecessary overstocking.

License plate recognition and organized retail crime

Beyond in-store surveillance, retailers are now deploying advanced monitoring systems in their parking lots using license plate recognition (LPR) technology.

LPR systems capture high-resolution images of vehicle license plates and cross-check them against databases of known offenders, allowing security teams to identify repeat shoplifters.

Retailers working with law enforcement have used LPR technology to track organized retail crime groups that operate across multiple locations. While LPR is an effective deterrent against repeat offenders, its use has raised privacy concerns, particularly regarding data retention and the potential for misuse.

Advocacy groups have warned against the risks of mass surveillance, calling for stricter regulations governing the storage and sharing of vehicle data.

Retailers turn to data sharing and incident reporting

Retailers grappling with rising theft-related losses are also turning to incident reporting and data-sharing platforms that allow businesses to document, analyze, and share theft-related intelligence.

These platforms enable loss prevention teams to collaborate, identify emerging theft trends, and coordinate responses with law enforcement agencies.

By pooling data on known offenders, retailers can proactively flag high-risk individuals and adjust their security strategies accordingly.

Law enforcement agencies are increasingly supporting retail data-sharing initiatives, using shared intelligence to track shoplifting patterns across different jurisdictions.

Some retailers are also using predictive analytics to assess theft risk by time of day, store location, and past incident history, optimizing their security personnel deployment accordingly.

Balancing security and customer experience

While advanced surveillance technologies have proven effective in curbing theft, concerns over consumer privacy and data security remain. The use of facial recognition technology in retail stores has drawn criticism from civil rights organizations, with opponents arguing that it disproportionately targets marginalized communities and lacks transparency.

Some cities, including San Francisco and Portland, have banned private entities from using facial recognition due to concerns about racial bias and wrongful identifications.

Retailers face a delicate balancing act between tightening security measures and maintaining a positive customer experience.

Excessively restrictive measures, such as placing high-theft items behind locked cases, have led to customer frustration and longer wait times. Some businesses have experimented with alternative deterrents, such as electronic shelf labels that dynamically update pricing and security features, or mobile checkout solutions that reduce self-checkout fraud.

Transparency in security policies and customer education on loss prevention initiatives have also been emphasized to maintain trust. Some retailers have started posting clear notices about their security measures, reassuring shoppers that monitoring is in place while respecting customer privacy.