Advanced Identification of Online Fraudulent Activities Using Deep Learning

Fraudulent activities have significantly increased as a result of the quick growth in online financial transactions, creating major security issues. Significant financial losses frequently occur as a result of traditional Fraud detection (FD) tools' inability to adjust to changing fraud tactics. Long short-term memory networks (LSTM), Convolutional neural networks (CNN), transformers, and neural networks are among the designs evaluated in this study's sophisticated deep learning-based FD methodology. The Transformer model performed the best using the "Online Payments FD Dataset," achieving 99.97% accuracy, 90.38% F1-score, and 84.99% recall. These results demonstrate how well deep learning—in particular, Transformer-based models—identifies intricate fraud behaviors. The suggested method advances the creation of real-time, scalable FD systems, improving financial security in digital transactions.