Retail Fraud Monitoring With Severity-Based Alert Thresholds
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Solution Overview
Problem
Existing fraud detection systems in retail environments are inadequate in responding promptly to illicit customer behavior, as they do not account for the scale of damage and maliciousness of the behavior effectively.
Innovation Solution
An information processing system that includes detection, determination, and output circuitry to identify illicit behavior, determine its severity based on predefined criteria, and alert in-store personnel, with features to handle customer history and behavior type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fraud detection systems evaluate customer behavior and send alerts for any detected illicit behavior, then detection reliability is improved, but false alerts increase causing reduced operational efficiency
Solution Approach 1:
The system changes the parameter of alert triggering from binary (any detection) to threshold-based (multiple detections or severe single detection). By requiring a predetermined number of detections or evaluating severity levels, the system maintains high detection reliability while reducing false alerts and improving operational efficiency.
Solution Approach 2:
The system applies different response thresholds to different types of illicit behavior. Severe behaviors trigger alerts immediately, while less severe behaviors require multiple detections. This localized quality approach ensures reliable detection of serious offenses while avoiding unnecessary alerts for minor incidents.
2Speed
If actions are taken immediately upon detecting any illicit behavior, then response speed is improved, but resource waste increases due to time-consuming procedures for minor offenses
Solution Approach 1:
The system introduces a severity parameter and response threshold parameter to differentiate between immediate action and monitored action. By changing these parameters based on behavior type and frequency, the system achieves fast response for serious offenses while conserving resources for minor incidents.
Solution Approach 2:
The system applies partial action (monitoring and counting) for less severe behaviors rather than immediate full action (alert and intervention). This allows the system to respond quickly when thresholds are met while avoiding resource waste on minor offenses that may be accidental.
3Productivity
If the system monitors and counts multiple instances of illicit behavior before alerting, then false alert reduction is achieved improving operational efficiency, but response time increases for legitimate severe offenses
Solution Approach 1:
The system applies different monitoring thresholds to different types of illicit behavior. Severe behaviors have a threshold of 1 (immediate alert), while less severe behaviors have higher thresholds. This local quality differentiation resolves the contradiction by ensuring fast response for serious offenses while using counting for minor offenses.
Solution Approach 2:
The system dynamically changes the alert threshold parameter based on the severity classification of detected behavior. This parameter adaptation allows the system to maintain operational efficiency for minor offenses while ensuring rapid response for severe offenses.
Data Source
AI summary
An information processing system includes: illicit behavior detection circuitry configured to detect illicit behavior by a customer regarding purchase of items at a store; determination circuitry configured to determine whether the amount of illicit behavior by the customer detected by the illicit behavior detection circuitry satisfies a predetermined criterion; and first output circuitry configured to, in response to the determination circuitry determining that the amount of illicit behavior by the customer satisfies the criterion, output a first alert to an in-store terminal, the first alert indicating occurrence of the illicit behavior.