Data augmentation is an important procedure in deep learning. GAN-based data augmentation can be utilized in many domains. For instance, in the credit card fraud domain, the imbalanced dataset problem is a major one as the number of credit card fraud cases is in the minority compared to legal payments. On the other hand, generative techniques are considered effective ways to rebalance the imbalanced class issue, as these techniques balance both minority and majority classes before the training. In a more recent period, Generative Adversarial Networks (GANs) are considered one of the most popular data generative techniques as they are used in big data settings. This research aims to present a survey on data augmentation using various GAN variants in the credit card fraud detection domain. In this survey, we offer a comprehensive summary of several peer-reviewed research papers on GAN synthetic generation techniques for fraud detection in the financial sector. In addition, this survey includes various solutions proposed by different researchers to balance imbalanced classes. In the end, this work concludes by pointing out the limitations of the most recent research articles and future research issues, and proposes solutions to address these problems.

Elliptic data—one of the largest Bitcoin transaction graphs—has admitted promising results in many studies using classical supervised learning and graph convolutional network models for anti-money laundering. Despite the promising results provided by these studies, only few have considered the temporal information of this dataset, wherein the results were not very satisfactory. Moreover, there is very sparse existing literature that applies active learning to this type of blockchain dataset. In this paper, we develop a classification model that combines long-short-term memory with GCN—referred to as temporal-GCN—that classifies the illicit transactions of Elliptic data using its transaction’s features only. Subsequently, we present an active learning framework applied to the large-scale Bitcoin transaction graph dataset, unlike previous studies on this dataset. Uncertainties for active learning are obtained using Monte-Carlo dropout (MC-dropout) and Monte-Carlo based adversarial attack (MC-AA) which are Bayesian approximations. Active learning frameworks with these methods are compared using various acquisition functions that appeared in the literature. To the best of our knowledge, MC-AA method is the first time to be examined in the context of active learning. Our main finding is that temporal-GCN model has attained significant success in comparison to the previous studies with the same experimental settings on the same dataset. Moreover, we evaluate the performance of the provided acquisition functions using MC-AA and MC-dropout and compare the result against the baseline random sampling model.

In many industrialized and developing nations, credit cards are one of the most widely used methods of payment for online transactions. Credit card invention has streamlined, facilitated, and enhanced internet transactions. It has, however, also given criminals more opportunities to commit fraud, which has raised the rate of fraud. Credit card fraud has a concerning global impact; many businesses and ordinary users have lost millions of US dollars as a result. Since there is a large number of transactions, many businesses and organizations rely heavily on applying machine learning techniques to automatically classify or identify fraudulent transactions. As the performance of machine learning techniques greatly depends on the quality of the training data, the imbalance in the data is not a trivial issue. In general, only a small percentage of fraudulent transactions are presented in the data. This greatly affects the performance of machine learning classifiers. In order to deal with the rarity of fraudulent occurrences, this paper investigates a variety of data augmentation techniques to address the imbalanced data problem and introduces a new data augmentation model, K-CGAN, for credit card fraud detection. A number of the main classification techniques are then used to evaluate the performance of the augmentation techniques. These results show that B-SMOTE, K-CGAN, and SMOTE have the highest Precision and Recall compared with other augmentation methods. Among those, K-CGAN has the highest F1 Score and Accuracy.

The past few years have witnessed the resurgence of uncertainty estimation generally in neural networks. Providing uncertainty quantification besides the predictive probability is desirable to reflect the degree of belief in the model’s decision about a given input. Recently, Monte-Carlo dropout (MC-dropout) method has been introduced as a probabilistic approach based Bayesian approximation which is computationally efficient than Bayesian neural networks. MC-dropout has revealed promising results on image datasets regarding uncertainty quantification. However, this method has been subjected to criticism regarding the behaviour of MC-dropout and what type of uncertainty it actually captures. For this purpose, we aim to discuss the behaviour of MC-dropout on classification tasks using synthetic and real data. We empirically explain different cases of MC-dropout that reflects the relative merits of this method. Our main finding is that MC-dropout captures datapoints lying on the decision boundary between the opposed classes using synthetic data. On the other hand, we apply MC-dropout method on dataset derived from Bitcoin known as Elliptic data to highlight the outperformance of model with MC-dropout over standard model. A conclusion and possible future directions are proposed.

Breast cancer is one of the most prevalent cancers in women. In recent years, many studies have been conducted in the breast cancer domain. Previous studies have confirmed that timely and accurate breast cancer detection allows patients to undergo early treatment. Recently, Generative Adversarial Networks have been applied in the medical domain to synthetically generate image and non-image data for diagnosis. However, the development of an effective classification model in healthcare is difficult owing to the limited datasets. To address this challenge, we propose a novel K-CGAN method trained in different settings to generate synthetic data. This study applied five classification methods and feature selection to non-image Wisconsin Breast Cancer data of 357 malignant and 212 benign cases for evaluation. Moreover, we used recall, precision, accuracy, and F1 Score on the synthetic data generated by the K-CGAN model to verify the classification performance of our proposed K-CGAN. The empirical study shows that K-CGAN performed well with the highest stability compared to the other GAN variants. Hence, our findings indicate that the synthetic data generated by K-CGAN accurately represent the original data.