Communication Event Embeddings for User Account Anomaly Detection
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Solution Overview
Problem
Existing systems struggle to efficiently detect and mitigate unauthorized activities, such as fraud and churn, associated with user accounts and communication devices, often requiring significant resources and being costly on an individual basis.
Innovation Solution
An anomaly detection management component (ADMC) uses neural networks trained with embedded arrays of user data to identify patterns and anomalies, including fraudulent activities, by analyzing communication events and interactions, and provides alerts for mitigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional anomaly detection methods are used to detect unauthorized activities, then detection capability is provided, but resource consumption and cost increase significantly
Solution Approach 1:
The patent replaces traditional mechanical anomaly detection systems with an information embedding approach using neural networks. The system embeds anomaly detection logic directly into the information processing architecture, substituting resource-intensive external detection mechanisms with integrated, efficient neural network-based detection that operates within the existing communication infrastructure.
Solution Approach 2:
The patent creates embedded representations (copies) of communication patterns and user behaviors that can be analyzed for anomalies without requiring full replication of the original communication data. These compressed embeddings capture essential characteristics while consuming minimal resources, enabling efficient anomaly detection through pattern matching against the embedded representations.
2Measurement precision
If comprehensive user data analysis is performed to detect fraudulent activities, then detection accuracy improves, but processing time and computational complexity increase
Solution Approach 1:
The patent extracts only the essential features and patterns from comprehensive user data that are relevant for anomaly detection. Instead of analyzing all raw communication data, the system extracts key behavioral indicators and embeds them into compact representations, maintaining detection accuracy while dramatically reducing processing time and computational requirements.
Solution Approach 2:
The patent transforms raw communication data into different parameter representations through neural network embedding. By changing the parameter space from raw data to embedded feature vectors, the system achieves both high detection accuracy and efficient processing, as the transformed parameters capture essential patterns in a compressed, computationally efficient format.
3Reliability
If manual monitoring of user accounts is implemented to prevent fraud, then detection thoroughness is maintained, but operational cost and complexity increase
Solution Approach 1:
The patent implements self-service anomaly detection where the system automatically monitors and detects fraudulent activities without requiring manual intervention. The neural network-based embedding system continuously analyzes communication patterns and autonomously identifies anomalies, replacing complex manual monitoring processes with automated, low-complexity operations that maintain high detection effectiveness.
Data Source
AI summary
Anomalies associated with events relating to users or user accounts can be detected. An anomaly detection management component (ADMC) determines embedded arrays comprising data bit groups representative of groups of properties and groups of relationships between properties associated with users, based on analysis of data related to events associated with users. ADMC trains a neural network (NN) based on applying embedded arrays to NN, in accordance with an artificial intelligence (AI) analysis process. ADMC determines an embedded array comprising data bits representative of properties and relationships between properties associated with a user based on analysis of data associated with the user. Trained NN can determine a pattern relating to the properties and relationships associated with the user based on AI-based analysis of the embedded array. Trained NN can detect an anomaly in the pattern based on AI-based analysis of the pattern, wherein the anomaly relates to an event.