Retail loss prevention leaders face a significant roadblock: global shrinkage reached $132 billion in 2024, driven by increasingly sophisticated organized retail crime and internal theft (Source: Cin7). For directors and VPs managing security across multiple locations, the primary hurdle is no longer just about recording incidents—it is about connecting disconnected data silos to identify loss as it happens.

Traditional video systems operate in isolation, capturing footage that sits unwatched until a loss is discovered days or weeks later. Meanwhile, point-of-sale (POS) systems generate millions of transaction logs that lack visual context. The gap between these two systems creates a blind spot where fraud, administrative error, and theft thrive.

The solution lies in the importance of an open API for connecting video AI with retail POS systems. By bridging the gap between transaction data and visual evidence, retailers can transform their security infrastructure from a reactive recording tool into a more proactive analysis tool. This article explores how integrating video AI with POS systems through open APIs cuts shrinkage, accelerates investigations, and uncovers operational inefficiencies.

Understanding the role of an open API in retail security

An open Application Programming Interface (API) serves as a universal translator between different software platforms. In a retail context, an open API allows your video AI platform to "speak" directly to your POS system, access control units, and inventory management software.

Legacy security systems often function as "walled gardens," where data is trapped within a proprietary ecosystem. This forces loss prevention teams to manually cross-reference time-stamps on receipts with video footage—a time-consuming and inefficient process. An open API eliminates this friction by allowing disparate systems to share data events in real time.

Why closed systems fail modern retail

• Data silos: transaction anomalies detected by the POS do not automatically trigger video bookmarks, requiring manual searching.

• Slow response: security teams cannot react to "void" or "no sale" events as they happen.

• Limited scalability: integrating new technology requires expensive hardware replacements rather than simple software connections.

By prioritizing open API architecture, retailers create a single dashboard where visual data and transaction data coexist. This integration is essential for combatting the 93% increase in shoplifting incidents observed between 2019 and 2023 (Source: National Retail Federation).

How connecting video AI with retail POS systems works

The integration process transforms raw video and data into a synchronized timeline of store activity. When an open API connects video AI with a retail POS system, the following workflow occurs:

1. Event generation: a cashier performs a specific action at the terminal, such as a void, refund, or age verification.

2. Data transmission: the POS system sends a structured data packet via the API containing the timestamp, transaction ID, and event type.

3. Visual correlation: the video AI platform receives this signal and swiftly "bookmarks" the corresponding video footage from the camera overlooking that specific register.

4. Intelligent analysis: AI agents analyze the video for specific behaviors—such as "Unattended Checkout" or "Sweethearting"—and cross-reference it with the POS data.

5. Alerting: if the visual evidence conflicts with the transaction record (e.g., a refund is processed but no customer is present), the system sends a real-time alert to the loss prevention manager.

This automated correlation is critical for addressing the $100 billion in annual losses U.S. retailers face due to shrinkage (Source: National Retail Federation).

3 ways API integration cuts retail shrinkage

Integrating video AI with POS systems directly addresses the three primary sources of retail loss: internal theft, external fraud, and administrative error.

1. Detecting internal theft and sweethearting

Internal theft is a significant contributor to retail shrinkage. A common tactic is "sweethearting," where an employee scans a low-cost item but bags a high-value item for a friend, or simply passes items around the scanner.

Without integration, identifying this requires hours of random video audits. With an open API, the system can filter for high-risk transaction types like "voids," "no sales," or "employee discounts." The video AI then verifies if the items visible on the counter match the transaction log. If an employee processes a "no sale" to open the drawer but pockets cash, the video AI correlates the drawer opening with the lack of a legitimate transaction, creating a flagged event for review.

2. Securing self-checkout terminals

Self-checkout environments show markedly higher shrinkage rates than traditional lanes, with barcode fraud contributing an estimated $10 billion annually to global loss (Source: barKoder). Common tactics include "ticket switching" (placing a cheap barcode over an expensive item) or "missed scans."

Video AI integrated with POS data combats this by matching the visual identification of an item with the scanned SKU. If the camera identifies a bottle of premium wine but the POS registers a generic produce item, the system flags the discrepancy in real time. This capability is vital as nearly one in three shoplifting incidents now involves self-checkout manipulation (Source: Security 101).

3. Combatting organized retail crime (ORC)

Organized retail crime has evolved beyond simple grab-and-run tactics. Groups now utilize complex schemes, including gift card fraud and fraudulent returns. 67% of U.S. retailers report involvement of transnational organized retail crime groups (Source: National Retail Federation).

API integration helps track these patterns across multiple locations. If a specific credit card or return pattern is flagged in the POS system associated with ORC activity, the video AI can rapidly pull face images (where legally permissible and compliant) or vehicle descriptions associated with those transactions across all stores. This can significantly cut the time required to assemble evidence packages for law enforcement.

Operational benefits beyond loss prevention

While security is the primary driver, the importance of an open API for connecting video AI with retail POS systems extends to store operations and profitability.

• Improving speed of service: by correlating transaction timestamps with video analytics of queue lengths, retailers can identify exactly when and why bottlenecks occur.

• Verifying transaction compliance: managers can audit age-restricted sales or high-value returns to ensure staff follow Standard Operating Procedures (SOPs) without being physically present.

• Optimizing staffing: insights on customer foot traffic combined with conversion rates helps managers schedule staff more effectively during peak hours.

Comparing video AI platforms for retail integration

When selecting a solution, it is crucial to choose a platform that prioritizes open architecture and ease of use. The table below compares Spot AI against traditional closed-circuit systems.

Best practices for implementing video AI and POS integration

Successful deployment requires careful planning to ensure data security, compliance, and operational adoption.

• Prioritize network security: retailers must ensure that opening APIs does not create vulnerabilities. Best practices include using encrypted connections (HTTPS/TLS) and implementing strict authentication protocols.

• Ensure regulatory compliance: data collection must adhere to GDPR, CCPA, and PCI-DSS standards. Retailers remain responsible for privacy and data retention policies.

• Start with high-risk locations: deploy the integrated solution in stores with the highest shrinkage rates first to show early results. Retailers have reported cutting shrinkage by up to 30% in high-risk stores within the first year of deployment (Source: Security 101).

• Train staff on data usage: loss prevention teams need training to interpret AI alerts effectively. The goal is to move from monitoring screens to managing exceptions.

• Audit regularly: periodic reviews of API connections and user access logs ensure the system remains secure and functional over time.

Key terms to know

• Open API (Application Programming Interface): a software intermediary that allows two applications (like a camera system and a cash register) to talk to each other.

• POS (Point of Sale): the system where retail transactions are completed and recorded.

• Video AI: artificial intelligence applied to video footage to detect objects, behaviors, and anomalies automatically.

• Shrinkage: the difference between recorded inventory and actual inventory, caused by theft, fraud, or error.

• Sweethearting: a form of theft where an employee gives free or discounted merchandise to a friend or family member at the register.

Unifying video and POS data for proactive loss prevention

The retail landscape is becoming increasingly volatile, with shrinkage rates climbing and organized crime tactics evolving. Relying on disconnected systems and manual video review is no longer a sustainable strategy. An open API connecting video AI with retail POS systems is critical—it helps teams move from reacting to loss toward proactively mitigating risk.

By unifying transaction data with visual intelligence, loss prevention leaders can uncover hidden fraud, streamline investigations, and provide a safer shopping environment. This technology helps every camera contribute actionable context, helping teams do more with less while supporting a strong operational case.

Want to see how video AI integration works in real retail environments? Request a Spot AI demo to explore the platform’s capabilities for loss prevention and operations.

Frequently asked questions

How can video AI enhance loss prevention efforts?

Video AI enhances loss prevention by automating the detection of suspicious behaviors. Instead of waiting for a human to watch a screen, AI agents can flag events like "person entering no-go zone," "loitering," or "unattended checkout" in real time. This allows security teams to intervene swiftly, reducing the likelihood of theft through faster response rather than just recording it.

What are the benefits of using open APIs in retail?

Open APIs allow retailers to connect their video security platform with other critical business systems like POS, access control, and inventory management. This interoperability eliminates data silos, enables automated cross-referencing of data (e.g., matching a refund to a video clip), and allows the system to scale easily without being locked into a single hardware vendor.

What are the best practices for integrating POS systems with video analytics?

Best practices include ensuring your network is secure with encryption and proper firewalls, verifying that the solution is PCI-DSS compliant to protect payment data, and defining clear rules for what constitutes a "flagged" event to minimize nuisance alarms and keep staff focused on real issues. It is also helpful to choose a platform that works with your existing cameras to improve value.

How does data analytics improve retail operations?

Beyond security, data analytics from video AI provides insights into store operations. Heatmaps can show which aisles attract the most traffic, queue line analytics can help managers open new registers before lines get too long, and conversion data can help optimize store layouts and staffing schedules for peak efficiency.

What compliance considerations should be taken into account?

Retailers must adhere to privacy regulations such as GDPR and CCPA regarding the collection and storage of video data. Additionally, any integration with POS systems must comply with PCI-DSS standards to ensure customer payment information is never compromised. It is essential to work with vendors who prioritize enterprise-grade security and compliance.

About the author

Joshua Foster is an IT Systems Engineer at Spot AI, where he focuses on designing and securing scalable enterprise networks, managing cloud-integrated infrastructure, and automating system workflows to enhance operational efficiency. He is passionate about cross-functional collaboration and takes pride in delivering robust technical solutions that empower both the Spot AI team and its customers.