Real-Time Voice Fraud Detection System
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Solution Overview
Problem
Traditional fraud call detection systems in telecommunications rely on post-call analysis, which is delayed and lacks real-time prevention capabilities, leading to significant financial losses and trust erosion.
Innovation Solution
A system that includes a first device and a server communicatively coupled through a network, where the server analyzes real-time audio signals from a voice call using at least two artificial intelligence models to identify fraudulent activity, such as cloned voices or high-risk scores, and triggers alerts in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If post-call analysis is used for fraud detection, then system complexity is reduced, but detection speed and real-time prevention capability deteriorate
Solution Approach 1:
The system performs preliminary actions by establishing multiple AI analysis models and real-time monitoring mechanisms before fraud occurs. The server continuously analyzes call patterns, voice characteristics, and device information during the call, rather than waiting for post-call analysis. This allows the system to detect and prevent fraud in real-time while maintaining manageable system complexity through automated processes.
2Measurement precision
If real-time analysis with multiple AI models is implemented, then fraud detection accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments fraud detection into multiple specialized AI analysis models, each focusing on specific aspects such as voice cloning detection, call pattern analysis, and device information verification. This segmentation allows the system to achieve high detection accuracy through specialized analysis while managing complexity by dividing the overall detection task into independent, modular components that can be executed separately.
Solution Approach 2:
The server is designed with multi-functionality, serving as a central hub that performs diverse fraud detection tasks using multiple AI models simultaneously. This universal approach allows a single system to handle various types of fraud detection (voice analysis, pattern recognition, device verification) without requiring separate dedicated systems for each function, thereby improving accuracy while controlling overall system complexity.
3Loss of energy
If real-time fraud identification is implemented, then financial losses are reduced, but processing power requirements increase
Solution Approach 1:
The system implements continuous fraud detection during the entire call duration, continuously analyzing audio streams and call patterns in real-time. This continuous monitoring enables the system to identify and prevent fraud as it occurs, minimizing financial losses. The processing power is efficiently utilized by maintaining steady-state analysis operations rather than intermittent batch processing, optimizing the balance between loss prevention and energy consumption.
Data Source
AI summary
A system for identification of a fraudulent voice call in real-time is described. The system includes a first device configured to receive a voice call originating from a second device and generate a call forward request. A server is configured to, in response to the call forward request, divide the voice call into a plurality of audio signals and analyze one or more audio signals to identify whether an audio originating from the second device is a cloned voice or a human voice. The server is configured to analyze the audio signal along with one or more preceding audio signals to determine a risk score associated with an identification of a fraudulent activity during the voice call and identify the voice call as the fraudulent voice call. The server is configured to trigger an alert in the first device to indicate that the voice call is a fraudulent voice call.