Scam Call Detection Using NLP and Naïve Bayes Classifier

Financial fraud, particularly credit card fraud, is a pressing concern in the realm of digital transactions. The number of phone scams is increasing daily as con artists use phone calls to target victims for nefarious ends. Individuals are falling for con artists' proposals, becoming victims and giving up their personal information, leaving them open to abuse. Effective detection techniques are becoming more and more necessary. In this study, we offer an efficient approach to scam call identification utilizing speech-to-text libraries and the machine learning technique Naïve Bayes classifier. Our technology, which translates voice to text, uses this text to evaluate conversations in real time. It looks for trends and suspicious phrases that point to attempted scams, including asking for credit card numbers, passwords, or other sensitive information. The user will be able to decide whether or not to trust and continue with the call by using the alert prompt that appears as a pop-up message if the words are found to be suspicious. The user will take certain measures, such as ending the conversation right away, blocking the number, and reporting it further, if they don't trust the call. Our strategy is to successfully handle scam calls through ongoing adaptation and learning, boosting user security and confidence in phone conversations. The user will be able to decide whether or not to trust and continue with the call by using the alert prompt that appears as a pop-up message if the words are found to be suspicious. The user will take certain measures, such as ending the conversation right away, blocking the number, and reporting it further, if they don't trust the call. Our strategy is to successfully handle scam calls through ongoing adaptation and learning, boosting user security and confidence in phone conversations. Keyword: Spam Detection, Naïve Bayes, Natural Language Processing, Machine Learning.