AI-driven embedded vision cameras in self-checkout loss prevention

However, this shift towards automation has brought new challenges for retailers, particularly in loss prevention. Retailers face significant issues with shrinkage, which includes theft, fraud, and errors during the checkout process.

The growing challenge of retail shrinkage

According to the National Retail Federation (NRF), the average shrink rate increased to 1.6% in FY 2022, representing $112.1 billion in losses. Additionally, in 2023, retailers lost approximately $121.6 billion to retail theft, indicating a significant and ongoing issue. This upward trend highlights the urgent need for effective loss prevention strategies. With the rise of AI and vision-based systems, retailers can now tackle these issues more effectively, ensuring both operational efficiency and customer satisfaction.

Common causes of retail loss and shrinkage at self-checkouts

• Missed scans

Intentionally or accidentally, missed scans lead to inventory and revenue discrepancies. This can cause stock-outs or overstock situations, affecting sales data analysis and leading to misguided business decisions.

• Barcode or ticket switching

Shoppers swap barcodes to pay less, causing pricing errors and revenue loss. This fraudulent activity is challenging to detect and, if left unchecked, impacts profit margins.

• Incomplete transactions

Customers sometimes leave without completing payment or overlook items in the basket, resulting in inventory shrinkage and revenue losses.

How AI-based loss prevention systems can solve it

AI and machine learning have become pivotal in addressing these issues by providing real-time monitoring and advanced analytics. Here’s how AI is transforming retail loss prevention:

• Real-time monitoring and insights

AI systems offer unprecedented levels of insight into store operations. They can detect suspicious behaviour and potential theft in real time, allowing for immediate action. These systems analyse patterns that human analysts might miss, providing a comprehensive view of store activities.

• Enhanced security measures

AI enhances traditional security systems by integrating with smart cameras and video analytics. These technologies can recognise suspicious activity, monitor movement patterns, and interface with point-of-sale (POS) systems to prevent fraud and theft.

• Predictive analytics

AI can analyse past incidents of theft and other losses to identify patterns and predict future occurrences. This allows retailers to develop more effective loss prevention strategies and optimise security protocols.

• Automated alerts

When the system detects unusual behaviour, it can automatically alert store personnel, enabling quick responses to prevent theft or fraud.

The role of cameras in AI-based loss prevention systems

Cameras are at the core of effective AI-based loss prevention systems. Their features play a crucial role in ensuring high accuracy and reliability. Key features include:

• High resolution and dynamic range

High-resolution cameras capture detailed images, essential for detecting small product labels and facial features. High Dynamic Range (HDR) ensures clear imaging in various lighting conditions. These features are crucial for identifying theft and ensuring accurate monitoring.

• Customised lens options

Different retail environments require varied lens configurations. Wide-angle lenses provide broad coverage, while zoom lenses focus on specific areas. Custom lens options ensure comprehensive surveillance across the store, making it easier to monitor large areas and detect suspicious activities​.

• Multiple connectivity options

Cameras supporting interfaces such as PoE (Power over Ethernet), USB, Ethernet, and GMSL (Gigabit Multimedia Serial Link) can integrate seamlessly with existing retail infrastructure. This versatility of interfaces simplifies installation and maintenance, making the system more robust and adaptable to different environments​.

• Compact and durable design

Cameras need to be compact and durable to withstand different retail environments. This ensures continuous operation and reliability, reducing downtime and maintenance costs.

• AI and Cloud support

Access to AI model training and cloud management services enhances the system’s capability to handle complex loss prevention tasks. Cloud support ensures that the AI algorithms are continuously updated and refined, improving their effectiveness over time. This feature also allows for remote monitoring and management, adding an extra layer of convenience and efficiency.

Conclusion

AI is revolutionising retail loss prevention by providing real-time insights and enhancing traditional security measures. As retailers continue to invest in AI infrastructure, the potential for reducing shrinkage and improving operational efficiency becomes increasingly attainable. By leveraging advanced camera technologies in partnership with AI solutions and retail analytics providers, retailers not only protect their assets but also enhance the overall shopping experience for customers.