Roaming SIM Fraud Detection Using 5G Core Network Signals
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Solution Overview
Problem
Existing systems struggle to detect and remediate fraudulent SIM card activity during roaming outside a telecommunications network, which can lead to high network usage spikes, revenue loss, and illegal activities due to latency in accounting for network activity.
Innovation Solution
A fraud detection system that utilizes potential fraud signals, applies machine learning models to predict fraudulent SIM card activity, and executes remediation actions such as blocking or adding to a fraud watchlist.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a mobile device connects to a network outside the carrier network while roaming, then the device can access network services, but there is a window of time before usage data is synchronized that can be exploited for fraudulent activities
Solution Approach 1:
The system performs preliminary actions by detecting potential fraud signals and generating fraud predictions before fraudulent activity can complete. The fraud detection system monitors roaming devices and predicts fraudulent behavior during the synchronization window, enabling preemptive remediation actions to be taken before the fraud can be executed
Solution Approach 2:
The system implements feedback by continuously monitoring fraud signals from roaming devices and using machine learning models to generate predictions that feed back into the network management system. This feedback loop enables real-time detection and response to fraudulent behavior patterns, allowing the system to adapt and respond dynamically to emerging threats
2Reliability
If the system monitors and detects fraudulent activity in real-time, then fraudulent transactions can be prevented, but additional system complexity and computational resources are required
Solution Approach 1:
The system introduces an intermediary fraud detection layer that sits between the network infrastructure and roaming devices. This intermediary component collects fraud signals, applies machine learning models, and generates predictions without requiring fundamental changes to existing network infrastructure, thereby managing complexity while maintaining high detection accuracy
Solution Approach 2:
The system replaces traditional rule-based fraud detection mechanisms with machine learning models that can automatically learn and adapt to fraudulent patterns. This substitution reduces the need for manual rule configuration and complex human oversight, allowing the system to maintain high detection accuracy with managed complexity through automated intelligent algorithms
Data Source
AI summary
The present disclosure generally relates to predicting and remediating fraudulent activity associated with a roaming SIM card. Systems described herein involve detecting potential fraud signals associated with a roaming SIM card and applying one or more machine learning models to those signals to generate a fraud prediction. In one or more examples, the potential fraud signals are detected in connection with one or more core network components of a 5G telecommunication network and provide unique insight into the network activities of a user equipment where the roaming SIM card is installed. As such, the systems described herein generate fraud predictions that take into account signals that are not necessarily indicative of fraud by themselves. Once fraud is predicted, the systems described herein execute various remediation actions to correct the fraudulent activities.