Fraud Detection System Using Call Signaling Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing fraud detection systems in telecommunications struggle to automatically identify fraudulent callers without requiring significant resources at call centers, and are vulnerable to sophisticated denial-of-service attacks.
Innovation Solution
A fraud detection system that processes incoming calls to determine a real-time fraud score using the three CLIs, their markings, and other call parameters, trends, and historical data, routing calls to specific operator terminals based on the fraud score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If question/response strategies or voice identification technology are used to detect fraud, then fraud detection capability is improved, but system resource consumption and vulnerability to DoS attacks increase
Solution Approach 1:
The system performs preliminary analysis of call signaling data (CLI authenticity markings, N-CLI vs P-CLI matching, screening indicators) before the call reaches the call center. This advance fraud assessment using readily available signaling information prevents resource-intensive verification processes from being triggered unnecessarily, reducing vulnerability to DoS attacks while maintaining detection capability
Solution Approach 2:
The system introduces an intermediary fraud detection layer that analyzes call signaling data between the network and call center. This intermediary uses automated rules (checking CLI markings, network vs presentation number consistency) to filter suspicious calls before they consume call center resources, resolving the contradiction between detection capability and resource consumption
2Productivity
If automated fraud detection is implemented without significant call center resources, then resource efficiency is improved, but detection accuracy may deteriorate
Solution Approach 1:
The system enables self-service fraud detection by automatically analyzing call signaling data using pre-configured rules (CLI authenticity markings, number matching logic, screening indicators). This automated self-assessment eliminates the need for human operator intervention while maintaining high detection accuracy through systematic evaluation of multiple fraud indicators
Solution Approach 2:
The system uses multi-functionality by leveraging existing call signaling data (already present in the communication protocol) for fraud detection purposes. The same signaling information used for call routing is simultaneously analyzed for fraud indicators (CLI markings, authenticity flags), achieving accurate detection without requiring separate dedicated resources
3Speed
If real-time fraud scoring is performed for all incoming calls, then fraud detection speed is improved, but processing load on the system increases
Solution Approach 1:
The system applies partial action by performing real-time fraud scoring only on calls that meet specific risk criteria (mismatched CLI markings, unusual calling patterns, failed authenticity checks). Not all incoming calls undergo full fraud scoring - only those triggering risk indicators receive intensive real-time analysis, reducing overall processing load while maintaining fast detection for suspicious calls
Data Source
Figure 1
Figure 2
AI summary
A fraud detection system is described that receives incoming calls and determines a fraud score indicating the likelihood that the incoming call is from a fraudulent or malicious caller. The system is able to learn patterns of fraudulent activity from historical call records and feedback provided by a subscriber.