Credit Card Fraud Detection Using Deep Neural Network With Shapley Additive Explanations

Credit card fraud is a significant challenge for financial security, with traditional detection systems often lacking accuracy and interpretability. Current methods fall short of capturing complex fraud patterns. This research evaluates the effectiveness of deep neural networks in fraud detection, including Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), Recurrent Neural Networks (RNN), Multilayer Perceptrons (MLP), and Deep Belief Networks (DBN). We employ a comprehensive dataset of up to 284,000 transactions for training and further incorporate two external datasets consisting of up to 1.3 million transactions to perform rigorous testing. According to our experiments, the CNN model outperformed LSTM, RNN, MLP and DBN by achieving a remarkable accuracy of up to 99%. Furthermore, we employ Adam optimiser to enhance model performance and SHAP (SHapley Additive exPlanations) analysis to improve the interpretability of the best-performing classifier and to gain insights into feature importance and model decisions. The proposed approach outperforms existing methods, combining high accuracy with model interpretability, and contributes to advancing fraud detection for financial security.