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.
Highlights
With the rapid development of information technology, the existing financial industry paradigm is changing; the new paradigm, following the evolution of smartphones and mobile technologies, is creating new forms of electronic financial services, increasing the number of non-face-to-face transactions, and simplifying and diversifying payment methods
A data augmentation method can be used to increase the total number of data when datasets are insufficient; this method is applied to the minority class using a generative adversarial networks (GANs) [5]; the augmented data are used for training/test data of the proposed domainadaptation model, and the results are compared with those of SMOTE (Synthetic Minority Oversampling Technique) [6] which is one of oversampling methods
GANs are suitable models for performing data augmentation; it consists of two artificial neural networks (ANNs) that learn by competing against each other: one is a generator that receives random noise as an input and processes it to resemble the distribution of the original data; the other is a discriminator that distinguishes the original data from those created by the generator
Summary
With the rapid development of information technology, the existing financial industry paradigm is changing; the new paradigm, following the evolution of smartphones and mobile technologies, is creating new forms of electronic financial services, increasing the number of non-face-to-face transactions (through the use of various devices and communications technologies), and simplifying and diversifying payment methods. The datasets employed in the proposed domain-adaptation method are generally used in research relating to abnormal-transaction detection; in particular, they are benchmark datasets for fraud detection in credit card [3] and financial [4] datasets Because both datasets feature an unbalanced ratio between the normal transactions and fraudulent or anomalous ones, the classes must be balanced to improve the machine learning performance and ensure smoothly learning. In this study, a GAN and SMOTE are used to solve the class imbalance problem for credit-card and financial-transaction fraud datasets; the domain-adaptation method is used to implement a model for detecting abnormal transactions in the two datasets; the method’s effectiveness is verified through a comparison of its classification performance against those of support vector machine (SVM) [7] and convolutional neural network (CNN) [8] based methods. The remainder of the paper is organized as follows: In Section II, the background and related research are described; in Section III, the model and datasets employed are described in detail; in Section IV, the experimental environment, learning method, and hyperparameters are described; in Section V, the classification performance of the model is compared and analyzed against those of the SVM- and CNN-based models; and in Section VI, the conclusions and limitations of the research are described, and future research directions are considered
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