Global 
United States 
Introduction to Retail Loss Prevention Systems
In the contemporary retail IT solutions sector, losses due to theft and item misplacement are becoming more prevalent day by day. Complex systems are put into action across this industry to tackle problems such as operational issues and errors, stock shrinkage due to theft, fraudulent transactions, abnormal shopping times, bypassing self-checkout systems, repeat returns, suspicious discount applications and others. As these issues continue to pose challenges for retailers and directly impact their bottom line, brick-and-mortar stores over the years have largely relied on alarm systems, sensors, cameras, security staff etc. Although partially effective, a combination of these physical deterrents acts as temporary solution and are unable to point out the exact cause of losses, shrinkage pattern and more in real-time.
To enhance the capabilities of these traditional solutions, AI-powered tools for loss prevention in retail stores are introduced. It is a transformative approach to counterattack losses faced by retailers for redefining omnichannel operations and seamless customer experiences in retail. These technologies use artificial intelligence, computer vision, machine learning and data analytics to monitor, detect, prevent, analyze and diminish theft and fraud occurrences in retail environments. Therefore, effectively assisting businesses to provide insights into potential risks for taking appropriate and timely actions. The global retail loss prevention market size was valued approximately at US $44.17 million in 2023 and is expected to reach US $108.38 million by 2030 surging at a CAGR of 13.68% during this forecast period.
Growing market size of retail loss prevention solutions from 2023 to 2030
Approaches for Retail Asset Protection
AI-driven retail loss prevention tools leverage huge amounts of data in real-time and analyze customer behaviors for detecting fraud and theft-related activities. These tools can scrutinize transactional patterns and operational anomalies leading to indication of the same. Let us understand a few data-driven approaches that can allow retailers to tackle losses.
1. Monitoring and Alert Generation: The software AI can assist these solutions to conduct real-time monitoring for pro-active loss prevention tactics. These tools are capable of detecting suspicious activities around automated self-checkout systems and point-of-sale regions. This saves time which would have otherwise been consumed in manual footage review while reactive actions might not have prevented the loss. These systems can detect anomalies in successful and voided transactional patterns, stock movement during off-hours. They trigger immediate alerts and warnings for the retail personnel to intervene prior to loss, thus reducing shrinkage and grievance addressal post-theft.
2. Improved Customer Experience: Tools for loss prevention in retail store can enhance the visitor experience without compromising on premise security as compared to traditional methods. This is because these techniques like security personnel and huge cameras are majorly intrusive in nature which may not be favored by customers. At times, an unrecognized item in baskets is scrutinized for several minutes resulting in a negative customer experience. All these issues are resolved by using these tools that run their operations in the backend where customer line sight is absent. It results in quicker checkouts, ease in product returns, theft prevention, and personalized user journey, without any disturbance in the overall shopping experience.
3. Predictive Analytics: These tools identify patterns in purchasing behavior which might be missed manually as they are trained for processing huge datasets. Similarly, they can analyze off-the-hours stock movements and recognize discrepancies related to the same. By using predictive analytics features, these tools can forecast regions that may be prone to losses for optimized resource allocation.
4. Fraud Detection: Many a times, apart from external or physical losses by visitors, cases related to internal fraud by employees and others may also result in retail shrinkage. Retail loss prevention systems have in-built expertise in detecting and identifying outliers indicating suspicious employee behavior such as increase in frequency of cancellations, inconsistency in registered transactions, surging number of refunds processed by any employee in particular etc. These anomalies are effectively flagged for retailers to understand the intricacies and occurrences of internal fraud alongside maintaining discretion accurately.
5. Strategic Insights: These AI-powered systems also offer deep data-driven insights into retail operations that lead to a reduction in stock shrinkage. They recognize loss patterns that in turn help in enhancing marketing, inventory level management and also in optimizing stocks. Retailers can strategize promotions, pricing, product placements, store layouts and more by understanding customer behavior through such data.
Features of Retail Loss Prevention Solutions
AI Tools for Loss Prevention
Loss prevention strategies and AI models integrate seamlessly with self-checkout systems and traditional point-of-sale setups, enhancing security by monitoring for suspicious behavior like item concealment. This proactive approach helps retailers safeguard assets while ensuring a smooth customer experience.
Self-checkout with Loss Prevention
A self-checkout mobile app powered by generative AI in retail that streamlines the purchasing process for customers while integrating advanced loss prevention features. Retailers benefit from real-time monitoring and alerts, ensuring a secure shopping environment while optimizing operational efficiency and minimizing theft.
Retail Media Network Services
Smart shelf shopper analytics leverage advanced technologies to track customer interactions and behaviors with products on shelves. This data-driven approach provides retailers with valuable insights, optimizing inventory management and enhancing the overall shopping experience.
Planogram-based Item Recognition
The planogram of the store is embedded into the solution to map items picked by the person with respect to the items mentioned in the planogram. The solution ensures products are accurately displayed according to store layouts and no product goes missing.
Camera Mapping
As a part of the deployment process, all camera positions are mapped and utilized for developing a rule engine that efficiently tracks the person of interest across multiple cameras. This optimizes surveillance coverage and enhances security monitoring of retail stores.
Person Detection
The solution features a module that can be used for detecting people from overhead cameras by using customized AI models. The development process involves dataset collection of persons, data preparation model finalization, training, fine- tuning and optimization.
Person Tracking
Persons of interest once detected can be tracked across multiple cameras present in the retail store. To achieve this objective, an initial phase of camera mapping will be implemented, followed by the development of a sophisticated rule engine to facilitate this seamless tracking.
Hand & Item Detection
This detection module will differentiate accurately between two classes which are empty hand and item in hand. It identifies the item present in a person’s hand from overhead cameras and alerts store personnel immediately for further actions.
Sample workstream of retail loss prevention systems
Trolley Detection
The trolley detection solution utilizes advanced computer vision technology to identify shopping carts and baskets in retail environments. This capability enhances inventory management, improves customer flow, enables targeted marketing strategies by analyzing trolley usage patterns and shopper behavior.
Item Placement Detection
The AI model is trained to check overlapping of item detected in hand and item placed in trolley or basket. If presence of overlap is confirmed and the hand detection module returns an empty hand value, it establishes the fact that the person has placed the item in trolley or basket and not concealed the same.
Theft Reporting
The solution generates accurate and real-time alerts and reports based on suspicious activities. On tracking a person who exhibits potential theft, the module stops detecting them if they move to self-checkout area. In case they head directly to exit, presence of trolley, basket and empty handedness are checked, and alarms are sent to security personnel for further monitoring.
Dashboard
The AI-based solution reports and logs detailed events in real-time over an intuitive dashboard such as person detection, tracking, item detection in hand and trolley, planogram-based item recognition, theft detection, potential theft activities in self-checkout areas etc.
Shopper Analytics
The solution can effectively generate day and hour-wise heatmaps, footfall, and pinpoint peak operational hours and traffic highpoints. It can categorize shopper visits with respect to demographics, brands, products, paths taken, cross visit patterns between store sections, duration, engagement, visitation uniquity, frequency and recency in the mapped store floors.
Innovate Retail Security with KritiKal
We observed in this blog that retail asset protection solutions can analyze multiple live video streams to trigger real-time alerts related to potential theft and fraud activities. This not only boosts profits and improves customer experience but also enhances operational efficiency and convenience for retailers. KritiKal Solutions has assisted multiple organizations by providing advanced loss prevention systems powered by AI in retail industry and computer vision, that can tackle the limitations of traditional surveillance in retail environments. Through artificial intelligence and computer vision, retailers can transform the in-store customer experience, offering benefits like cost-efficient operations and a competitive edge. By leveraging data-driven analytics, retailers can better engage with customers and meet their needs. Partner with KritiKal to take advantage of our tailored computer vision services for the retail industry. Please get in touch with us at sales@kritikalsolutions.com to know more about our products and realize your automotive requirements.
Krisha Surana currently works as a Software Engineer at KritiKal Solutions. She is proficiently skilled in .NET framework, React.js, Microsoft SQL Server management, CSS, C# and more. With her ability to work efficiently in teams and extensive experience of working with software development, she has assisted KritiKal in delivering various projects to some major clients.