Segmented Telecom Usage Forecasts for Anomaly Detection
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Solution Overview
Problem
Existing telecommunication service providers face challenges in identifying anomalous user behavior, which can lead to revenue leakage, fraudulent activity, and inefficient resource allocation, particularly in detecting stolen devices and correcting billing errors.
Innovation Solution
The system segments users based on Recency-Frequency-Usage (RFU) metrics, trains machine learning models for each segment, generates forecasts, and compares actual usage against these forecasts to identify anomalous behavior, enabling actions such as device authentication, deactivation, and billing corrections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If telecommunication service providers use traditional monitoring methods to track service usage, then basic usage tracking is maintained, but the ability to detect anomalous behavior and prevent revenue leakage is insufficient
Solution Approach 1:
The patent segments users into distinct categories (e.g., prepaid users, postpaid users, roaming users, international users) based on their service characteristics and usage patterns. This segmentation enables tailored anomaly detection models for each user group, improving detection accuracy while allowing efficient resource allocation by focusing monitoring efforts on high-risk segments rather than treating all users uniformly.
Solution Approach 2:
The system performs preliminary actions by establishing baseline usage patterns and predictive models for each user segment before anomalies occur. Historical data is analyzed to create expected behavior profiles, and the system continuously monitors for deviations from these baselines. This proactive approach enables early detection of anomalous behavior such as stolen device usage or billing errors before significant revenue loss occurs.
2Reliability
If comprehensive monitoring of all service usage is implemented to prevent fraudulent activity, then detection capability improves, but system complexity and processing requirements increase
Solution Approach 1:
By dividing the user base into segments with similar characteristics and risk profiles, the system applies monitoring complexity only where needed. Each segment can have customized monitoring rules and anomaly detection algorithms tailored to its specific patterns, reducing overall system complexity compared to a monolithic approach that would need to handle all user types uniformly.
Solution Approach 2:
The patent implements local quality by applying different monitoring intensities and detection algorithms to different user segments based on their risk profiles. High-risk segments (e.g., international users, roaming users) receive enhanced monitoring, while low-risk segments use simpler monitoring mechanisms. This differentiated approach maintains high detection capability for fraudulent activity while reducing system complexity by avoiding uniform heavy-handed monitoring across all users.
3Loss of time
If real-time analysis of service usage data is performed to identify anomalies, then response time to fraudulent activity increases, but processing speed and computational resources are consumed
Solution Approach 1:
The system performs preliminary computation by pre-calculating baseline usage patterns, statistical parameters, and risk thresholds for each user segment during off-peak times or using historical data. This preliminary action reduces the computational burden during real-time anomaly detection, as the system only needs to compare current usage against pre-established baselines rather than performing complex analyses in real-time, thereby reducing processing speed requirements while maintaining fast response time.
Solution Approach 2:
The patent applies local quality by optimizing computational resources for each user segment based on its specific needs and risk profile. Rather than applying uniform heavy computational processing to all users, the system allocates processing intensity locally to segments where it is most needed. This enables real-time anomaly detection with reduced overall computational resource consumption by focusing intensive analysis only on high-risk segments.
Data Source
AI summary
One or more computing devices, systems, and/or methods for identifying anomalous behavior of users are provided. In an example, users of a telecommunication service provider may be segmented into a plurality of user segments based upon telecommunication service metrics associated with the users. A machine learning model may be trained using telecommunication service information associated with users of the first user segment to generate a trained machine learning model. Using the trained machine learning model, a forecast of telecommunication service usage associated with a first user segment of the plurality of user segments. A telecommunication service usage metric, associated with a user belonging to the first user segment, may be compared with a range indicated by the forecast. The user may be flagged as having anomalous behavior based upon a determination that one or more telecommunication usage metrics, associated with the user, are outside one or more ranges indicated by the forecast.