Retail Shrinkage Detection via Behavioral Analytics
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Solution Overview
Problem
Current methods for detecting retail shrinkage at point of sale sites primarily focus on transaction characteristics, which are inadequate in identifying irregular behaviors such as sweethearting, as they do not effectively utilize behavioral analytics to detect fraudulent activities.
Innovation Solution
A system comprising sensors (optical and acoustic) that capture human behavior data, processed by a computing device to build and compare behavior models using pattern recognition methods, enabling the detection of irregular behaviors during transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If transaction characteristics are monitored and analyzed to detect retail shrinkage, then transaction data can be processed to identify potential fraud, but irregular behaviors such as sweethearting cannot be effectively detected
Solution Approach 1:
The patent transitions from analyzing transaction data (one dimension) to analyzing behavioral data captured by sensors (another dimension). By introducing sensors to capture human behavior patterns, the system adds a new dimension of observation that enables detection of irregular behaviors like sweethearting that transaction characteristics alone cannot reveal.
Solution Approach 2:
The patent introduces behavior models as an intermediary between raw sensor data and fraud detection. These behavior models process and interpret human behavior patterns captured by sensors, translating them into actionable insights that help identify irregular transactions and retail shrinkage.
2Adaptability or versatility
If sensors are introduced to capture human behavior data, then detection of irregular behaviors improves, but system complexity increases
Solution Approach 1:
The patent employs sensors that can capture multiple types of behavioral data (optical, acoustic, etc.) using a unified sensing approach. This multi-functional capability allows the system to detect various irregular behaviors through a single integrated sensor framework, reducing the need for multiple specialized systems and thereby limiting the increase in overall system complexity.
Solution Approach 2:
The patent creates behavior models that replicate and interpret human behavior patterns. Instead of directly analyzing complex raw sensor data, the system uses these behavioral copies or representations to simplify the detection process, making the system more manageable despite the introduction of sensors.
3Reliability
If behavior models are built and compared using pattern recognition, then detection of sweethearting and fraud improves, but processing time and computational resources increase
Solution Approach 1:
The patent pre-establishes behavior models that capture typical human behavior patterns during transactions. By preparing these models in advance, the system can quickly compare real-time sensor data against the pre-built models during actual transactions, reducing processing time while maintaining high detection accuracy for irregular behaviors.
Data Source
AI summary
Methods, devices, and systems for detecting retail shrinkage using behavior analytics are described herein. The retail shrinkage may be due to, for example, sweethearting, although embodiments of the present disclosure are not so limited and can be used to detect other forms of retail shrinkage as well. One or more device embodiments include a memory, and a processor coupled to the memory. The processor is configured to execute executable instructions stored in the memory to receive data associated with behavior of an individual and use the data associated with the behavior of the individual to determine whether the behavior of the individual is irregular to detect retail shrinkage.