AI Video Analytics for Theft Prevention in Retail: A CMMI-Based Approach

Retail theft, from opportunistic shoplifting to highly organized retail crime (ORC), has become a significant financial burden for businesses worldwide. ORC networks exploit security vulnerabilities by reselling stolen goods, while individual shoplifters target weak surveillance systems.

In 2022, U.S. retailers lost $112.1 billion to theft, with estimates reaching $150 billion by 2026. A significant portion of these losses comes from large-scale, coordinated theft operations, which cost around $703,320 per $1 billion in sales.

This article explores a strategic approach to preventing ORC and shoplifting by integrating AI-driven technologies such as facial recognition and behavioral analytics into retail asset protection.

We’ll use the Capability Maturity Model Integration (CMMI) framework to structure the retail theft prevention processes, demonstrating how each stage of maturity can benefit from AI Video Analytics. This strategy aims to help retailers reduce theft and strengthen store security.

If you’re familiar with process management, you’ve likely encountered the Capability Maturity Model Integration (CMMI)—a framework designed to improve processes and organizational performance across various industries.

When applied to asset protection and loss prevention in retail, CMMI offers a structured way to track and improve security processes, incorporating AI technologies like facial recognition or behavioral analytics to combat theft at various maturity levels.

Initial Theft Detection

At this stage, theft detection relies on sporadic employee reports and infrequent system checks, typically once a month, leading to delayed responses.

Role of Automation in Theft Detection

Basic systems for accounting and inventory management are in place, and products may be equipped with individual anti-theft devices. A video surveillance camera is also present in stores, but video surveillance footage is stored for only up to 14 days, limiting its use for threat detection.

Manual Investigation and Analysis

Investigations are handled on a case-by-case basis without ongoing analysis of theft patterns, making it difficult to identify trends or optimize security strategies.

Compliance and Reporting

At this level, there are no formal compliance requirements for asset protection and loss prevention processes.

Source of Loss Recovery

Losses are typically shifted onto the employees, with no formal structure in place for compensation recovery.

Initial Theft Detection

Proactive theft detection becomes routine, with incidents identified through both employee reports and daily checks. Investigations are now supported by more formalized processes.

Role of Automation in Theft Detection

Integrated systems for accounting and inventory management are in place, and products are still equipped with anti-theft devices. Video surveillance cameras cover critical areas, including entrances, and their footage is stored for at least 30 days. Additionally, there is now an analytical system that links incident detection with investigations and loss analysis.

Manual Investigation and Analysis

The data collected is now more comprehensive, integrating both manual and automated inventory checks. Theft analytics are compiled based on product groups, locations, and timeframes, linked to incident records to create an initial shoplifter profile. A database of suspicious individuals is established and shared with staff and security personnel.

Compliance and Reporting

Regulatory compliance for asset protection and loss prevention processes is formalized, including compensation investigations. The company tracks incidents and analyzes cases of prevented losses, forming the basis for coordination with law enforcement.

Source of Loss Recovery

Losses are now accounted for as a percentage of profits or revenue, with a separate budget line for prevented losses.

Initial Theft Detection

At this level, the company has formed a dedicated loss prevention team that continuously analyzes video footage and incident reports to identify potential theft before it escalates.

Role of Automation in Theft Detection

The company has robust inventory management systems, anti-theft devices, and surveillance cameras with integrated face recognition systems. Video footage is stored for 90 days. The system automatically detects incidents and analyzes patterns using advanced algorithms. A dedicated team of operators monitors and investigates these incidents in real time.

Manual Investigation and Analysis

Investigations are now driven by data, utilizing both automated and manual inventory checks. Analytics are performed on thefts, categorized by product group, location, and timeframe, and linked to video incident data. The facial recognition system helps identify known criminals, with automated databases of suspicious individuals regularly updated and shared with security personnel.

Compliance and Reporting

Incident tracking and reporting are robust, with full compliance to asset protection standards and frequent collaboration with law enforcement.

Source of Loss Recovery

Losses are categorized in more granular terms, such as specific product groups or categories. Financial departments track prevented losses separately, and Key Performance Indicators (KPIs) are established for reducing incident frequency.

Initial Theft Detection

At the highest level, theft detection becomes a continuous, proactive process. The loss prevention team not only analyzes video footage but also refines processes to detect emerging theft patterns. Automation requirements are regularly updated based on data analysis.

Role of Automation in Theft Detection

The company has an integrated accounting and warehouse management system. Products are equipped with individual anti-theft devices. Surveillance cameras monitor key areas, including entry points, with video recordings stored for at least 90 days. A dedicated set of cameras is allocated for facial recognition. The asset protection and compensation investigation automation subsystem includes a facial recognition system for both real-time and historical analysis, an analytical system that logs incidents and links them to case files, an operational investigation system with a structured data framework, including evidence, video recordings, recognition system alerts, and other relevant data.

Manual Investigation and Analysis

The investigation is conducted proactively based on data from the analytical system, facial recognition, and behavioral analysis. Loss assessment is carried out through automated inventory checks at the end of each shift. Theft analytics are generated by product categories, locations, and timeframes, linked to incident records to create a comprehensive shoplifter profile. An automated database of suspicious individuals is maintained and shared with staff and security personnel.

Compliance and Reporting

The company adheres to strict regulatory requirements, with a fully automated process for incident registration and management. The system generates real-time data that can be transmitted to law enforcement.

Source of Loss Recovery

Losses are tracked as a specific percentage of profits or revenue by product category. Financial departments maintain records of prevented losses, and KPIs are tied to reducing incidents proactively.

Traditional theft prevention tactics—like security guards and standard CCTV—still play a role in retail security, but they often fall short in keeping up with the scale and speed of modern threats.

Solutions like 3DiVi Omni Platform go beyond basic surveillance, using facial recognition and behavioral analytics to swiftly identify known shoplifters, analyze incidents, and accelerate investigations.

Real-Time Face Identification

AI Video Analytics can instantly recognize individuals previously flagged for shoplifting by matching live video feeds against a watchlist. When a match is detected, security teams receive an alert, allowing them to respond immediately—without needing to monitor every feed manually.

Behavioral Analytics for Proactive Detection

Facial recognition is just one piece of the puzzle. AI also analyzes behavior, flagging actions that could indicate theft. For example, prolonged loitering near high-value items or unusual movement patterns can trigger alerts, enabling security teams to assess the situation before a theft occurs.

Automated Incident Analysis

Reviewing security footage manually can be time-consuming and prone to oversight. AI speeds up this process by automatically analyzing video feeds, biometric data, and security logs. This allows security teams to:

• Quickly retrieve relevant footage without sifting through hours of recordings,
• Generate detailed reports for further investigation,
• Make faster, data-driven decisions on handling incidents.

By applying the CMMI framework and integrating AI video analytics into their security processes, retailers can gradually improve the ability to detect and prevent theft incidents.

AI-powered tools like facial recognition and behavioral analytics not only accelerate the detection process but also provide data-driven insights that can help refine security measures over time.

While the journey from basic surveillance to a fully optimized, data-driven approach may take time, the potential for reducing theft losses and strengthening store security is significant.