Machine Learning Theft Detection Using Transaction Data
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Solution Overview
Problem
Retailers face challenges in detecting cashier theft, known as 'sweet hearting,' due to the difficulty in real-time evaluation of video feeds and the lack of necessary video equipment and computing resources, leading to substantial losses from this type of fraud.
Innovation Solution
A data-driven machine learning system that uses transaction item details to predict theft by training a machine-learning algorithm with historical transaction data, allowing for the identification of potentially fraudulent transactions without relying on video analysis, and integrating the prediction into the transaction processing workflow to flag suspicious activities for manual review.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If video feeds are evaluated in real-time to detect sweet hearting, then detection capability is improved, but the complexity of image processing and computing resources required increase significantly
Solution Approach 1:
The patent extracts only the essential features needed for theft detection from the complex video feed data, rather than processing the entire video stream. By identifying and extracting specific indicators of sweet hearting behavior, the system achieves effective detection while minimizing the computational burden of image processing.
Solution Approach 2:
The system performs preliminary analysis of transaction data and video feeds to identify suspicious patterns before full detection is required. By pre-processing data and flagging potential cases of sweet hearting based on initial analysis, the system reduces the amount of complex processing needed during real-time evaluation.
2Reliability
If manual review of video feeds is performed to detect sweet hearting, then detection accuracy is improved, but the time and effort required increase substantially
Solution Approach 1:
The system enables automated self-detection of sweet hearting cases by training machine learning models to independently analyze transaction data and video feeds. The trained model automatically identifies and flags suspicious transactions without requiring manual review, thereby maintaining high detection accuracy while eliminating the time-consuming manual evaluation process.
3Reliability
If comprehensive video equipment and computing hardware are deployed to detect sweet hearting, then detection capability is improved, but the cost and infrastructure requirements increase
Solution Approach 1:
The patent replaces expensive, complex video analysis infrastructure with simpler, more affordable transaction data processing systems. By using readily available transaction data from point-of-sale systems and applying machine learning algorithms, the system achieves effective theft detection without requiring costly video equipment and high-performance computing hardware, making the solution accessible to smaller retailers.
4Reliability
If video storage is implemented to review past transactions for theft detection, then detection capability is improved, but storage requirements and processing overhead increase
Solution Approach 1:
The system extracts only the necessary transaction data elements from video feeds and stores them in a compact format suitable for machine learning analysis. By extracting key features such as item details, transaction amounts, and temporal patterns rather than storing complete video recordings, the system maintains detection capability while dramatically reducing storage requirements.
Data Source
AI summary
A machine-learning algorithm is trained with features relevant to basket data for items of transactions. The trained algorithm is trained to predict whether a given transaction is more or less likely to be associated with theft being engaged in by a transaction operator for the transaction. The trained algorithm is then provided basket data for a given transaction and produces as output a theft prediction value. When the theft prediction value exceeds a configured threshold value, the transaction is flagged for manual intervention or the transaction is flagged for subsequent manual verification.