Abstract

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. This study applied the Synthetic Minority Sampling Technique (SMOTE) to address data imbalance and ensure that fraud transactions were represented fairly. Accuracy, precision, recall, and F1 score, as well as real-time processing latency, were used to perform the performance evaluation. The results showed that ATAD-Net performed much better than baseline CNN and RNN models with an accuracy of 98.65%, fewer false positives, and a real-time detection latency of 8.2 milliseconds per transaction. ATAD-Net addresses this by dynamically adapting to evolving financial fraud strategies, thus enhancing financial fraud detection and offering financial institutions a very accurate and efficient real-time financial fraud detection solution.

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