An Efficient Domain-Adaptation Method using GAN for Fraud Detection

Abstract

In this paper, an efficient domain-adaptation method is proposed for fraud detection. The proposed method employs the discriminative characteristics used in feature maps and generative adversarial networks (GANs), to minimize the deviation that occurs when a common feature is shifted between two domains. To solve class imbalance problem and increase the model’s detection accuracy, new data samples are generated by applying a minority class data augmentation method, which uses a GAN. We evaluate the classification performance of the proposed domain-adaption model by comparing it against support vector machine (SVM) and convolutional neural network (CNN) models, using classification performance evaluation indicators. The experimental results indicated that the proposed model is applicable to both test datasets; furthermore, it requires less time for learning. Although the SVM offers a better detection performance than the CNN and proposed domain-adaptation model, its learning time exceeds those of the other two models when dataset increases. Also, although the detection performance of the CNN-based model is similar to that of the proposed domain-adaptation model, its learning process is longer. In addition, although the GAN used to solve the class imbalance problem of the two datasets requires slightly more time than SMOTE (synthetic minority oversampling technique), it shows a better classification performance and is effective for datasets featuring class imbalances.