A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection

Credit cards play an essential role in today’s digital economy, and their usage has recently grown tremendously, accompanied by a corresponding increase credit card fraud. Machine learning (ML) algorithms have been utilized for fraud detection. However, the dynamic shopping patterns of holders class imbalance problem made it difficult ML classifiers to achieve optimal performance. In order solve this problem, paper proposes robust deep-learning approach that consists long short-term memory (LSTM) gated recurrent unit (GRU) neural networks as base learners stacking ensemble framework, with multilayer perceptron (MLP) meta-learner. Meanwhile, hybrid synthetic minority oversampling technique edited nearest neighbor (SMOTE-ENN) method is employed balance distribution dataset. The experimental results showed combining proposed deep SMOTE-ENN achieved sensitivity specificity 1.000 0.997, respectively, which superior other widely used methods literature.