A method utilizing deep learning and reinforcement learning for credit card fraud detection

Abstract

As the world rapidly transitions to digitization and cashless transactions, the use of credit cards has surged. Alongside this growth, fraudulent activities have also increased, leading to significant financial losses for institutions. Therefore, it is crucial to identify and assess fraudulent transactions to distinguish them from legitimate ones. In this study, we propose combining Deep Learning with Reinforcement Learning to effectively detect online payment fraud. Specifically, we employ a Long Short-Term Memory recurrent neural network in conjunction with a Deep Q Network, naming this hybrid model DQN-LSTM. A key challenge in this study is achieving high accuracy using an unbalanced credit card fraud dataset, compared to existing methods. Our main goal is to eliminate false positives and false negatives and improve the overall F1 score without using any data balancing techniques. For feature selection from imbalanced datasets, we utilize a deep autoencoder. The deep auto encoder generates accurate data representations that can be used for classification and reconstruction. Experimental results show that the proposed hybrid model consistently outperforms other classifiers as well as state of the arts, achieving better precision, recall and f1 score using the train-test method. Additionally, our model excels in 5-fold cross-validation, demonstrating its robustness and reliability. SHAP (SHapley Additive exPlanations) values enhance model interpretability by quantifying the contribution of each feature to the model's predictions. The SHAP summary plot provides a global view of feature importance, identifying key drivers such as “V19” and “V15” that influence the model's decisions across the European cardholder dataset. IEEE and German datasets are also tested by SHAP. On the other hand, SHAP force plots focus on local interpretability, illustrating how specific features contribute to individual predictions, making it easier to understand why a particular transaction was flagged as fraudulent.