POS Video Verification for Fraud Detection
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Solution Overview
Problem
Current video surveillance and monitoring systems for point of sale (POS) transactions in retail environments are ineffective in detecting employee theft and fraud due to high personnel time requirements, data volume management issues, and the inability to detect internal theft, such as cash fraudulent activities, leading to significant inventory shrinkage and financial losses.
Innovation Solution
A system that combines non-video and video data from POS transactions to generate primitives, which are then used to infer exceptional transactions based on predefined rules, allowing for real-time monitoring and verification of suspicious activities, reducing the need for extensive personnel review and improving fraud detection efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If video surveillance systems are used to monitor POS terminals, then permanent visual records can be kept for evidence, but enormous volumes of data are produced making real-time monitoring difficult
Solution Approach 1:
The system extracts only the essential information from video streams using object detection and recognition algorithms, separating relevant transaction data from the bulk video data. This allows the system to maintain the ability to review complete video records while filtering out unnecessary data volume for real-time analysis.
Solution Approach 2:
The system introduces an intermediary processing layer that includes object detection algorithms, primitive generation modules, and exception rule engines. This intermediary layer processes video data into actionable insights and exceptions, enabling real-time monitoring without requiring direct human review of all video footage.
2Reliability
If exception-based reporting software is used to mine POS data, then potential fraudulent activity can be detected, but evidence collection and review take days or weeks
Solution Approach 1:
The system performs preliminary analysis by continuously monitoring video streams and generating exceptions in real-time as transactions occur. This preliminary action allows the system to identify and flag suspicious activities at the moment they happen, rather than waiting for post-event data mining and analysis that takes days or weeks.
Solution Approach 2:
The system maintains continuous monitoring and analysis of POS transactions through real-time video processing and exception detection. This continuous operation ensures that fraudulent activities are detected immediately as they occur, eliminating the delays inherent in batch processing and post-event review methods.
3Reliability
If passive electronic devices are attached to theft-prone items, then alarms can be triggered, but devices can be deactivated by employees before items leave the store
Solution Approach 1:
The system replaces passive electronic alarm devices with an active video surveillance and analysis system. Instead of relying on mechanical or electronic alarms that can be manually deactivated, the system uses computer vision, object detection algorithms, and real-time exception monitoring to detect theft attempts, making it impossible for employees to disable the detection capability.
4Ease of operation
If employee training programs are provided for loss prevention, then employees can understand transaction rules, but theft still occurs and is difficult to recover
Solution Approach 1:
The system provides self-service monitoring that automatically detects and reports suspicious transactions without requiring employee intervention or knowledge. The exception-based reporting system with video verification autonomously identifies fraudulent activities, freeing employees from the need to actively monitor or report theft while maintaining high detection accuracy.
Data Source
AI summary
Methods and systems are provided for monitoring a point of sale (POS) transaction. Operations performed by the methods and systems include generating POS primitives by processing non-video data of a transaction recorded at POS terminal. The operations also include generating video primitives by processing video data of the transaction recorded at the POS terminal. The operations further include determining that the transaction comprises an exceptional transaction by comparing the non-video data and/or the video data to exceptional transaction rules. Additionally, the operations include determining that the exceptional transaction comprises a verified exceptional transaction by generating a video event based on the video primitives and a corresponding video rule.