Real-Time Phone Fraud Detection and Prevention Based on Artificial Intelligence Tools

Telephone fraud poses significant threats to telecommunications network users, causing both financial loss and emotional stress. The aim of this study was to develop and evaluate a real-time telephone fraud detection and prevention system, based on the principle of phone conversation content analysis. The study included an empirical study to select optimal AI tools for system implementation. A system prototype was developed, integrating the selected automatic speech recognition tool and large language model with a specific prompt, to analyze phone conversation content in real-time. The system’s effectiveness was evaluated in a simulated environment reflecting real-time conditions, using an expanded dataset with various fraud scenarios and languages. The results indicate high classification effectiveness of the system, achieving an accuracy of 90,4% and a 91,2% F1 score, indicating the system’s efficacy in real-time telephone fraud detection. The prevention rate reached 69,8%, demonstrating the system’s potential in real-time telephone fraud prevention.