Online Fraud Call Detection: A Machine Learning Approach for Real-Time Identification and Prevention

Detection of Online Fraud Calls, A Machine Learning Method for Real-Time Identification and Prevention The rise of telecommunication fraud has become a major issue in the digital era, resulting in annual losses up to billions of dollars due to false calls. This research introduces a robust machine learning system for the real-time detection of online fraudulent calls. Our suggested system amalgamates various detection methodologies, including voice pattern analysis, behavioral profiling, and network traffic surveillance, to discern anomalous calling patterns. The system utilizes a hybrid methodology that integrates Support Vector Machines (SVM), Random Forest, and Deep Neural Networks to get elevated accuracy in fraud detection. Experimental findings indicate that our methodology attains an accuracy of 94.7% with a false positive rate of 2.3%, markedly surpassing conventional rule-based systems. The solution facilitates real-time processing of call data streams, rendering it appropriate for use in telecommunications networks. Keywords: fraud detection, Machine learning MC, Voice analysis, Behavioral profiling, Real-time systems, Telecommunication security.