POS Scam Detection Using Vision and Checkout Event Correlation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing retail environments face challenges in efficiently detecting scams, such as K1 scams, at point of sale (POS) terminals, where guests attempt to complete transactions without paying using fake payment methods, leading to unauthorized exits and potential return of items for cash.
Innovation Solution
Implement edge computing devices with machine learning models to analyze digital image data from cameras and correlate it with checkout event data from POS terminals and cash registers to identify suspicious activities in real-time, generating alerts for stakeholders to intervene.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual monitoring methods are used to detect scams at POS terminals, then system complexity remains low, but detection precision and reliability are insufficient
Solution Approach 1:
The patent replaces manual mechanical monitoring with automated computer vision systems and machine learning algorithms. Cameras capture images of payment processes, and AI models automatically analyze them to detect scam patterns, eliminating the need for human operators to manually review each transaction while significantly improving detection accuracy.
Solution Approach 2:
The patent introduces an intermediary analysis layer between the payment terminal and the monitoring system. This layer includes image preprocessing modules, feature extraction algorithms, and scam pattern recognition models that mediate between raw camera data and final scam detection decisions, enabling complex analysis without proportionally increasing system complexity.
2Reliability
If comprehensive scam detection analysis is performed, then detection reliability improves, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-training machine learning models with extensive scam pattern data before deployment. During actual transactions, these pre-trained models can quickly recognize scam patterns without requiring complex real-time analysis, thus maintaining high reliability while reducing processing time. The system also pre-identifies key detection features from camera feeds before full analysis.
Solution Approach 2:
The patent implements a multi-stage detection process that skips unnecessary analysis for normal transactions. The system first performs rapid initial screening using simple heuristics, and only triggers comprehensive analysis when suspicious patterns are detected. This allows the system to maintain high reliability for scam detection while minimizing processing time for the majority of legitimate transactions.
3Productivity
If real-time scam detection is implemented, then productivity of scam prevention improves, but computational resource consumption increases
Solution Approach 1:
The patent segments the scam detection process into multiple independent modules: image capture, preprocessing, feature extraction, scam pattern recognition, and decision making. Each module can be independently optimized and deployed on different hardware resources. This segmentation allows the system to achieve real-time scam prevention while distributing computational load efficiently across available resources.
Solution Approach 2:
The patent applies partial analysis to most transactions and excessive (full) analysis only when needed. The system performs basic real-time monitoring on all transactions using minimal computational resources, then activates more resource-intensive analysis only for transactions exhibiting suspicious patterns. This approach maintains high scam prevention efficiency while minimizing overall computational resource consumption.
Data Source
AI summary
The disclosed technology provides for detecting a scam attempt in a physical retail environment. A system can include a camera that captures image data of a checkout area, a cash register that generates event data during a checkout process, and an edge computing device that accesses scam detection criteria identifying combinations of visual and event features corresponding to checkout scams, receive the image data, receive the event data, which includes (i) an indication that the checkout process is a cash transaction and/or (ii) a cash register drawer open event, detect, based on processing the image data, physical movement of a form of payment in or around the checkout area, correlate timestamps for the physical movement with the event data, generate an indication of the checkout scams based on satisfying the scam detection criteria, and return the indication of the checkout scams being performed.