AI-Driven SIM Card Fraud Detection System

Abstract: The exponential growth in SIM card fraud incidents poses significant challenges to telecommunications security, resulting in substantial financial losses and identity theft cases worldwide. This paper proposes a novel hybrid machine learning framework that integrates rule- based filtering with Random Forest classification for effective SIM card fraud detection. The system analyzes four key behavioral parameters: IMEI change frequency, geographical mobility patterns, call activities, and SMS usage behavior. Our approach implements a multi-layer detection architecture that combines the transparency of rule-based systems with the pattern recognition capabilities of machine learning. The framework features an interactive Streamlit- based dashboard providing real-time monitoring, explainable AI insights, and comprehensive analytics. Experimental results demonstrate 92.5% detection accuracy with 85.7% recall rate and processing times under 5 seconds. The proposed solution addresses critical limitations of existing systems and offers a practical, scalable approach for telecom security applications, particularly in the Indian telecommunications context. The system combines rule-based filtering with Random Forest classification to analyze SIM usage patterns including IMEI changes, location behavior, call frequency, and SMS activity. It detects various fraud types such as SIM swapping, cloning, multiple activations, and abnormal usage patterns. Implemented with Python and Streamlit, the solution provides an interactive dashboard for fraud analysis, feature importance visualization, and risk scoring. The model achieves high accuracy in classifying fraudulent SIM cards while maintaining explainability through transparent decision-making processes. This project offers a practical, scalable solution for telecom companies and financial institutions to combat SIM-based fraud, enhancing security in the rapidly evolving digital landscape.

Keywords: SIM Card Fraud, Machine Learning, Hybrid Detection, Random Forest, Explainable AI, Telecommunications Security