A Text Scam Detection Model Using Text-To-Text Transfer Transformer Architecture

The increase in the complexity of text message fraud in online communication endangers privacy, financial stability and user confidence. The traditional approaches to detection, like rule-based systems, keyword matching, and statistical classifiers, have difficulties in adapting to new scam techniques: they tend to end up with high false positive and false negative rates. The proposed work is the development of an improved scam detector using the Text to Text Transfer Transformer (T5) architecture with the Sentence -T5 encoding for semantic data compression. The proposed model was optimized using data comprised of the Kaggle benchmark dataset and scam messages that were previously unseen (collected and directly fed to the model using crowd sourcing and web scraping). Experiments were conducted, and the results were compared with Na¨ıve Bayes, SVM, Decision Tree, Random Forest, LSTM, Logistic Regression and K Nearest Neighbor. The comparative experiments have shown that the proposed model achieved 98% in all aspects of accuracy, precision, recall, and F1 Score, performing better than all the other baseline models. The proposed model displayed good generalization across unseen scam variations and, therefore, can be applied in secure messaging infrastructures. This work presents a robust and scalable, context-aware method to address performance and adaptability problems associated with fraud detection.