01Challenge
Employees Cannot Visually Monitor Every Transaction
When one employee supervises several self-checkout stations, it is difficult to observe every item scan and customer action simultaneously.
Distinguishing between an unintentional operating mistake and behavior that may require further review can also be challenging.
Applying strict verification to every customer may increase checkout times and negatively affect the shopping experience.
02Solution
Identify Transactions Requiring Review Using POS and Video Data
Within the permitted scope, AI can analyze checkout video and POS activity to identify potentially unscanned items, unusual cancellations, and unexpected quantity changes.
When review is required, the system can notify an employee and display the relevant transaction details and verification points.
For example, the following workflow can be established:
- 1Checkout Activity and Video Data Collection
- 2AI and Rule-Based Analysis
- 3Review Target Detection
- 4Employee Notification
- 5Situation Review and Customer Assistance
- 6Response History Storage
A transaction should not be classified as fraudulent based solely on AI detection. Store employees review the situation and respond appropriately, including helping customers use the checkout system correctly.
03Use Cases
Potential Applications
- Detecting potentially unscanned products
- Identifying possible incorrect product registrations
- Detecting unusual cancellations and quantity changes
- Providing employee support for self-checkout users
- Notifying employees about transactions requiring review
- Analyzing error trends by checkout station and time
- Identifying screens and procedures associated with frequent mistakes
- Recording response actions and results
04Expected Benefits
Balance a Smooth Checkout Experience with Effective Loss Prevention
Identifying transactions requiring review allows employees to provide support when necessary without continuously monitoring every customer.
Retailers can reduce unintentional operating errors while detecting unusual checkout activity at an earlier stage.
Robo Lab uses AI video analytics, POS data analysis, and automated notifications to support loss prevention while maintaining a positive customer experience.