Fraudulent Subscription Detection Model for SIM Box
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Solution Overview
Problem
Existing SIM-box detection systems are inefficient in identifying fraudulent subscriptions, leading to delayed detection and high false alarm rates, resulting in significant revenue loss for mobile network operators due to the need for manual analysis and the time-consuming process of gathering evidence.
Innovation Solution
A fraudulent subscription detection system that generates a model based on historical network data of a freshly identified SIM-box subscription, allowing for real-time identification of replacement subscriptions by analyzing live network data, thereby reducing detection time and increasing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If existing SIM-box detection systems are used, then fraud detection capability is provided, but detection time is delayed (taking days instead of hours) and false alarm rate is high
Solution Approach 1:
The system performs preliminary actions by automatically generating a detection model based on historical network data of a freshly identified fraudulent subscription before the fraudulent activity continues. This model is then used to immediately detect replacement subscriptions, eliminating the need for manual analysis and reducing detection time from days to hours while improving accuracy.
Solution Approach 2:
The system implements feedback by using the historical network data of the freshly identified fraudulent subscription to generate a detection model, which then provides feedback on live network data to detect replacement subscriptions. This closed-loop approach continuously improves detection accuracy and reduces false alarms by adapting to the specific fraud pattern.
2Productivity
If manual analysis is performed for fraud detection, then accuracy can be maintained, but productivity decreases due to time-consuming process
Solution Approach 1:
The system enables self-service by automatically generating the detection model using historical network data without requiring manual intervention. The model then autonomously analyzes live network data to detect replacement subscriptions, dramatically improving productivity from manual analysis to automated detection while maintaining high accuracy.
Solution Approach 2:
The patent replaces the mechanical manual analysis process with an automated computational model that processes network data automatically. This substitution of mechanical manual inspection with automated algorithmic detection significantly increases productivity and detection speed while reducing the complexity burden on operators.
3Ease of manufacture
If detection systems are deployed with minimal modifications to existing systems, then ease of implementation improves, but detection capability may be limited
Solution Approach 1:
The system achieves universality by designing a detection model that can be applied to detect various types of replacement subscriptions within the existing network infrastructure. The model uses general patterns from historical data that can be universally applied to identify different fraud scenarios, maintaining high detection accuracy while requiring minimal modifications to existing systems.
Data Source
AI summary
Arrangements are provided for identifying a second fraudulent subscription replacing a first fraudulent subscription. A method is performed by a fraudulent subscription detection system. The method includes obtaining notification of the first fraudulent subscription having been identified in a SIM box. The method comprises obtaining historical network data of the first fraudulent subscription. The method com includes prises generating a model based on the historical network data. The method includes identifying the second fraudulent subscription replacing the first fraudulent subscription in the SIM box upon providing live network data as input to the model. The method includes providing an identification of the second fraudulent subscription to at least one of a subscription manager entity and a user interface of a Manual Analysis component.