How Artificial Intelligence Can Support Loss Prevention

Like various other areas of retail, wholesale, and logistics, loss prevention faces numerous challenges. One of the main obstacles is finding the balance between demands and available resources, a situation further complicated by frequent staff turnover. Given the growing need for productivity and accuracy, it is crucial to consider how technologies can assist in these processes.

This article aims to spark discussion among diverse teams and groups on how artificial intelligence (AI) can support the loss prevention field. However, as this is a new solution that gained greater relevance with the emergence of platforms such as Microsoft Copilot, OpenAI’s ChatGPT, Anthropic’s Claude, Google’s Gemini, and others, the central question remains: How can we integrate these solutions into our daily operations? The loss prevention area also faces specific challenges that span the entire value chain of operations. Some of these challenges are illustrated in the image below:

With increasing complexity, daily constraints, and the constant need to improve efficiency, AI technologies become essential tools for addressing these challenges, and loss prevention is no exception.

Detailed below are twelve potential AI applications for loss prevention, addressing various categories of problems faced in retail, wholesale, and logistics:

1. Demand Forecasting and Inventory Management: Through predictive and prescriptive modeling, AI adjusts inventory levels based on historical data and market trends, minimizing inventory errors, reducing the risk of loss due to perishability or expiration, and lowering logistics costs related to unnecessary stock movements between distribution centers or stores.
2. Data Analysis for Third-Party Fraud Detection: AI algorithms can analyze large volumes of transactions and communications to detect fraud patterns among suppliers and partners, thereby reducing the potential financial impacts of external fraud and improving supply chain reliability.
3. Internal and External Theft Detection with Video Analysis: AI systems analyze real-time security camera footage and compare it with historical data to identify suspicious behaviors and patterns in theft, such as time of day, location, product, duration of stay, etc., allowing for reduced incidents of internal and external theft through real-time alerts. Tasks previously done manually, and sometimes nearly impossible due to extensive recording periods, can now be automated.
4. Monitoring Storage and Environmental Conditions: IoT sensors can monitor temperature, humidity, and other storage conditions, with AI analyzing the data to detect problems, thereby preventing product deterioration due to improper storage or temperature fluctuations. Additionally, AI can automate preventive or reactive actions, reducing the need for manual intervention, such as dispatching technicians for maintenance.
5. Optimization of Store and Warehouse Layouts: AI can evaluate current layouts and suggest optimizations based on customer flow analysis and storage efficiency, reducing issues related to inadequate exposure, improving product accessibility, and enhancing team productivity.
6. Expiration Monitoring and Management: AI solutions can track product or batch expiration dates based on information collected during receiving, transfers, and shelf life patterns. AI alerts about items nearing expiration, potentially reducing product losses due to expiration and positively impacting margins, as near-expiration items often require specific discounting actions and price adjustments, which can otherwise be time-consuming for teams and affect product margins.
7. Analysis of Customer Feedback and Complaints: Similar to the Net Promoter Score (NPS), AI can process customer feedback and complaints to identify patterns in issues with products or services, helping to adjust processes to prevent damage, inadequate handling, logistical operation costs such as returns, and particularly enhancing the customer experience.
8. Anomaly Detection in Financial Transactions: AI algorithms analyze financial transactions for fraudulent or inconsistent patterns, such as verifying the consistency of transactions with contracts and agreements, and identifying payments that do not match agreed amounts, which can minimize both internal and external fraud by detecting suspicious activities.
9. Loss Prevention in E-Commerce with Data Analysis: AI algorithms analyze online purchasing behaviors to identify fraud, suspicious patterns, and analyze returns and refunds, potentially reducing losses due to fraud, fraudulent returns, or delays and errors in customer service.
10. Team Performance Analysis: AI systems can monitor team activities and performance in real-time, analyzing productivity, communication, and collaboration data. AI algorithms can evaluate activity logs on collaboration and communication platforms to assess team contributions to specific projects, allowing for quick identification of areas needing support and adjustments. AI can also provide personalized feedback for each team member based on their performance and recommend specific training or resources to enhance their skills.
11. Employee Training and Development: Using AI-driven platforms, training modules can be tailored to employee needs based on performance analysis or content absorption evaluation, improving knowledge, compliance, and standardization among employees, and reducing operational, administrative errors, or productivity losses
12. Management of Receiving and Shipping: AI can analyze receiving and shipping documents, comparing them with order information to automatically identify discrepancies or assist in tracking shipments via sensors or trackers. AI can monitor the condition of shipments or suggest routes and shipping methods based on historical data and current conditions, thereby improving shipping efficiency.

These potential applications can be implemented through internal development or by using solutions integrated with AI platforms. Such integrations not only enhance inventory management but also improve safety and operational efficiency, potentially leading to substantial reductions in losses across various retail, wholesale, and logistics sectors.

However, to achieve these benefits, it is essential for professionals in the field to stay updated with technological innovations and be critical in understanding how to apply them to their realities. Many companies face challenges such as outdated ERP systems, a shortage of skilled labor, and data disorganization, especially with multiple platforms in use. Therefore, effective adaptation and integration of these technological solutions is crucial to overcoming such obstacles, optimizing operations, and ensuring success, which will always be validated by defining metrics that show the return on each investment.

Gilberto Quintanilha Júnior has a bachelor’s degree in data processing, a postgraduate degree in advanced business administration, and MBA in internal controls and compliance. He is an executive with over 20 years of experience with companies in Latin America, with strong expertise in risk management, auditing, loss prevention/fraud, ethics hotline, security, and internal controls. Quintanilha Júnior has provided mentorship focused on inventory management and loss prevention to managers and directors of major national retailers. He is the author of articles related to auditing, inventory management, and loss prevention