ATAD-Net: An Adaptive Deep Learning Framework for Real-Time Financial Fraud Detection

With the fast growth of financial transaction fraud, there is a need for advanced detection systems capable of real-time analysis. Rule-based and machine-learning approaches to fraud traditionally suffer from being unable to adapt to changing fraud patterns, returning very high back result rates and much inefficiency in the security of financial operations. However, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) methods are suitable, but they lack adaptability and interpretability. This paper proposes an Adaptive Transactional Anomaly Detection Network (ATAD-Net), a new deep learning (DL) framework for improving fraud detection accuracy, minimizing false positives, and guaranteeing real-time adaptability. ATAD-Net dynamically adjusts to evolving fraud tactics by integrating CNNs for local pattern recognition and Long Short-Term Memory (LSTM) for sequential transaction analysis. After training and testing the model using the IEEE CIS Credit Card Fraud Detection Dataset, a large-scale benchmark for evaluating financial fraud detection models, the accuracies of the different models were assessed.