Anomaly Detection for Telecom Data Revenue Leakage
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Solution Overview
Problem
Telecom operators face challenges in accurately billing and detecting revenue leakage due to data discrepancies across complex networks, making it difficult to identify fraudulent users or faulty nodes, and current monitoring systems either fail to detect anomalies or add additional load on network elements.
Innovation Solution
A method and system that captures subscriber usage data independently from the network, reconciles it with charging systems, and identifies volume gaps and root causes using a processor-based system that aggregates, categorizes, and analyzes data records from multiple sources to detect anomalies and minimize revenue leakage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional computing mechanisms are used to analyze data, then the system is simple to implement, but it cannot accurately detect revenue leakage in terabytes of data
Solution Approach 1:
The patent segments the analysis by introducing a probabilistic threshold mechanism that divides detection into different levels of confidence. The system calculates probability scores for each detected anomaly and applies threshold-based filtering, which simplifies the decision-making process while maintaining high detection accuracy across terabytes of data.
Solution Approach 2:
The patent introduces a probabilistic scoring mechanism as an intermediary between raw data analysis and final anomaly detection. This intermediary layer calculates probability scores that bridge the gap between complex data patterns and simple detection decisions, enabling accurate revenue leakage detection without requiring overly complex system architecture.
2Reliability
If monitoring systems are added to detect data discrepancies, then detection capability is improved, but additional load is added on network elements
Solution Approach 1:
The patent enables the network elements to perform self-monitoring by leveraging existing network data flows and protocols. The system uses idle network capacity and existing signaling mechanisms to collect and analyze data, allowing network elements to detect revenue leakage without requiring additional dedicated monitoring infrastructure or increasing network load.
Solution Approach 2:
The patent makes existing network elements multi-functional by enabling them to perform both their primary functions and revenue leakage detection simultaneously. The system utilizes existing network protocols and data flows for dual purposes: normal network operation and anomaly detection, thereby improving reliability without adding dedicated monitoring infrastructure.
3Measurement precision
If comprehensive data analysis is performed across all systems, then billing accuracy is improved, but the complexity of integrating data from multiple sources increases
Solution Approach 1:
The patent changes the approach from analyzing complete raw datasets to analyzing probability scores and aggregated metrics. By transforming detailed multi-source data into consolidated probability indicators, the system achieves high billing accuracy while significantly reducing the complexity of data integration and analysis.
Solution Approach 2:
The patent extracts only the essential probability indicators and key metrics from comprehensive multi-source data, rather than analyzing all raw data in detail. This extraction approach maintains billing accuracy by focusing on critical anomaly indicators while simplifying the integration complexity by eliminating the need to process and correlate every detail from multiple data sources.
4Quantity of substance
If manual surveillance is used to analyze data, then the system is simple to implement, but it is impossible to analyze terabytes of data comprehensively
Solution Approach 1:
The patent replaces manual surveillance mechanics with automated probabilistic analysis algorithms. The system uses computational probability scoring and threshold-based detection to automatically analyze terabytes of data, substituting human capacity limitations with automated processing that can handle large-scale data volumes while maintaining detection accuracy.
Solution Approach 2:
The patent transforms the analysis approach by changing from manual inspection of individual data points to automated probabilistic aggregation of large datasets. This parameter change enables the system to process terabytes of data by converting detailed information into consolidated probability metrics, achieving comprehensive analysis capacity through automation.
Data Source
AI summary
System and method for detecting anomalies in the recorded consumption of data volume and charging of data services in a communication network is described. Data records for each session may be captured from multiple sources. The data records may comprise parameters indicating usage volume pertaining to services being consumed for each session. Further, the data records may be aggregated and reconciled to detect volume gap in each session. Each session may be categorized into a session category based upon the detection of the volume gap. The data records may further be enriched by tagging each data record with the session category. The data records enriched may then be aggregated across the parameters. Finally, a root-cause parameter for the volume gap pertaining to each session may be identified by computing a total volume, a total volume gap and a probability of gap root-cause for each parameter using the aggregated data records.