AUTOMATED SCAM MESSAGE DETECTION USING TEXT ANALYSIS AND MACHINE LEARNING

The rapid increase of scam messages through SMS and online platforms poses a significant
threat to digital communication, causing financial loss and compromising personal data. Many users are
unable to reliably distinguish fraudulent messages from genuine ones, which increases their vulnerability
to cybercrime. To address this challenge, this project proposes an automated system for detecting scam
messages using text analysis techniques and machine learning algorithms. The system processes the
textual content of messages, extracting features such as keyword patterns, message structure, and
contextual cues indicative of fraudulent intent. Using Python, machine learning models are trained
on labelled datasets containing both scam and genuine messages to accurately classify incoming
messages. When a new message is received, the system analyzes it in real time and provides an alert
if it is identified as potentially fraudulent. This automation reduces the reliance on manual inspection,
mitigates financial risk, and enhances user confidence in digital communication. By integrating text
mining and predictive modelling, the proposed system provides a scalable and efficient solution for
scam message detection. It enables users to quickly identify fraudulent messages, safeguards sensitive
information, and contributes to safer online and mobile communication environments. This project
demonstrates how AI-driven text analysis can enhance cybersecurity and protect users from evolving
digital threats.