Scamshield – Catch Scammers with Autorecorded Calls: Review Paper
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The rise of digital communication has been accompanied by a significant increase in fraudulent and scam phone calls, causing both financial and emotional damage to individuals and organizations. Traditional rule-based detection systems have become insufficient in combating these evolving scams, which often rely on voice manipulation and social engineering techniques. This review paper explores recent research and technological advancements in artificial intelligence (AI), voice biometrics, and data mining methods for scam and fraud call detection. It surveys existing literature in the domain of telecommunication fraud prevention, highlighting methods such as machine learning–based classification, real-time voice recognition, and behavioral pattern analysis. The proposed system, ScamShield, integrates these ideas to create a lightweight Android-based application capable of identifying suspicious calls through voice and keyword analysis, storing call metadata, and providing scam-awareness alerts. The review aims to bridge existing research gaps by summarizing multiple approaches to telecommunication fraud detection and offering insights for future AI-powered solutions that ensure secure and trustworthy communication networks.
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