Using generative adversarial networks for improving classification effectiveness in credit card fraud detection

Fiore, Ugo; Santis, Alfredo De; Perla, Francesca; Zanetti, Paolo; Palmieri, Francesco

doi:10.1016/j.ins.2017.12.030

Cited by 387 publications

(181 citation statements)

References 16 publications

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“…Fiore et al (2019) [139] observed that data imbalance is a crucial issue in payment card fraud detection and that oversampling has some drawbacks. They proposed Generative Adversarial Networks (GAN) for oversampling, where they trained a GAN to output mimicked minority class examples, which were then merged with training data into an augmented training set so that the effectiveness of a classifier can be improved.…”

Section: A Fraud Detection In Financial Servicesmentioning

confidence: 99%

A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends

Sengupta¹,

Basak²,

Saikia³

et al. 2019

Preprint

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Deep learning has taken over - both in problems beyond the realm of traditional, hand-crafted machine learning paradigms as well as in capturing the imagination of the practitioner sitting on top of petabytes of data. While the public perception about the efficacy of deep neural architectures in complex pattern recognition tasks grows, sequentially up-to-date primers on the current state of affairs must follow. In this review, we seek to present a refresher of the many different stacked, connectionist networks that make up the deep learning architectures followed by automatic architecture optimization protocols using multi-agent approaches. Further, since guaranteeing system uptime is fast becoming an indispensable asset across multiple industrial modalities, we include an investigative section on testing neural networks for fault detection and subsequent mitigation. This is followed by an exploratory survey of several application areas where deep learning has emerged as a game-changing technology - be it anomalous behavior detection in financial applications or financial time-series forecasting, predictive and prescriptive analytics, medical imaging, natural language processing or power systems research. The thrust of this review is on outlining emerging areas of application-oriented research within the deep learning community as well as to provide a handy reference to researchers seeking to embrace deep learning in their work for what it is: statistical pattern recognizers with unparalleled hierarchical structure learning capacity with the ability to scale with information.

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Section: A Fraud Detection In Financial Servicesmentioning

confidence: 99%

A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends

Sengupta¹,

Basak²,

Saikia³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…While the original concepts and theory of the artificial neuron and the neural network were developed during the 1940s-1970s, and later refined and improved upon during the 1980s and 1990s [45], the advent and increased availability of powerful computational hardware has led to large increase in the application of ANN in the past 10-20 years. Nowadays, ANN are utilized for many different applications in a wide variety of fields [46][47][48][49][50][51][52]. This chapter aims to give an introduction into the theory and mathematics behind ANN, explain in more detail one specific type of AN, and finally discuss the main methods of training ANN.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Solving vapor-liquid flash problems using artificial neural networks

Poort

Ramdin

Kranendonk³

et al. 2019

Fluid Phase Equilibria

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Vapor-liquid phase equilibrium (flash) calculations largely contribute to the total computation time of many process simulation models. As a result, process simulations, especially dynamic cases, are limited in the amount of detail that can be included due to time restrictions. In addition, under certain conditions flash calculations can fail to provide acceptable results. In this work, artificial neural networks were investigated as a potentially faster and more robust alternative to conventional flash calculation methods. Classification neural networks were used to determine the phase stability of a given mixture of fluids, while regression networks were used to make predictions of thermodynamic property values. In addition to conventional flash types such as the constant pressure, constant temperature (P T ), and constant pressure, constant entropy (P S) flash, neural networks are used to develop two concept flash types: a constant entropy, constant volume (SV ), and a constant enthalpy, constant volume (HV ) flash. All neural networks were trained on, and compared to, data generated using the P T -flash algorithm from the Thermodynamics for Engineering Applications (TEA) property calculator. Data was generated for mixtures of water and methanol over a wide range of pressures and temperatures. The artificial neural networks showed speed improvements over TEA of up to 35 times for phase classification and 15 times for property predictions. Overall phase classification accuracy scores of around 97% were achieved. Average property value prediction errors range between 0.5% and 7% when compared to the spread in magnitude of the test data, and R 2 scores were in the general order of 0.95 and higher, although classification and property prediction in the two-phase region showed markedly higher errors than properties in the pure liquid or vapor regions. Moreover, thermodynamic consistency and the stability of a system consisting of multiple neural network flash types both still require considerable improvement. Finally, this work shows that artificial neural networks can be used to create unconventional flash types such a the SV -and HV -flash.i

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“…We referred to the flow diagram proposed by Fiore [27] and combined it with our work. The framework of the entire detection process was shown in Fig.…”

Section: Detection Processmentioning

confidence: 99%

Detecting Malicious Social Robots with Generative Adversarial Networks

2019

KSII TIIS

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Malicious social robots, which are disseminators of malicious information on social networks, seriously affect information security and network environments. The detection of malicious social robots is a hot topic and a significant concern for researchers. A method based on classification has been widely used for social robot detection. However, this method of classification is limited by an unbalanced data set in which legitimate, negative samples outnumber malicious robots (positive samples), which leads to unsatisfactory detection results. This paper proposes the use of generative adversarial networks (GANs) to extend the unbalanced data sets before training classifiers to improve the detection of social robots. Five popular oversampling algorithms were compared in the experiments, and the effects of imbalance degree and the expansion ratio of the original data on oversampling were studied. The experimental results showed that the proposed method achieved better detection performance compared with other algorithms in terms of the F1 measure. The GAN method also performed well when the imbalance degree was smaller than 15%.

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Using generative adversarial networks for improving classification effectiveness in credit card fraud detection

Cited by 387 publications

References 16 publications

A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends

A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends

Solving vapor-liquid flash problems using artificial neural networks

Detecting Malicious Social Robots with Generative Adversarial Networks

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