A Robust Intelligent Deep Learning Framework for the Detection of Credit Card Fraud Using Sequential Financial Transaction Data

Financial organizations are extremely concerned about the sharp rise in fraudulent financial transactions brought about by the quick growth of credit card usage and digital payment technologies.Severe class imbalance and the intricate temporal features of transaction data are frequently too much for current rulebased procedures and traditional machine learning models to handle.This study offers a clever deep learning approach for detecting credit card fraud that makes use of sequential financial transaction data in order to address these issues.Instead of using recurrent neural network architectures, the proposed model is built on a Temporal Convolutional Network combined with an attention mechanism to effectively capture long-term temporal relationships within transaction sequences.The convolution-based architecture allows efficient parallel processing, improved learning stability, and faster model training while preserving high detection performance.To efficiently capture long-term temporal correlations within transaction sequences, the suggested model is based on a Temporal Convolutional Network in conjunction with an attention mechanism, as opposed to recurrent neural network topologies.While maintaining high detection performance, the convolution-based architecture enables effective parallel processing, enhanced learning stability, and quicker model training.According to experimental research, the suggested method outperforms recurrent deep learning models and conventional machine learning techniques in terms of accuracy, precision, recall, and F1-score.The outcomes show how convolution-based deep learning models may be used to create safe, scalable, and effective fraud detection systems.