Modelling in finance is a challenging task: the data often has complex
statistical properties and its inner workings are largely unknown. Deep
learning algorithms are making progress in the field of data-driven modelling,
but the lack of sufficient data to train these models is currently holding back
several new applications. Generative Adversarial Networks (GANs) are a neural
network architecture family that has achieved good results in image generation
and is being successfully applied to generate time series and other types of
financial data. The purpose of this study is to present an overview of how
these GANs work, their capabilities and limitations in the current state of
research with financial data, and present some practical applications in the
industry. As a proof of concept, three known GAN architectures were tested on
financial time series, and the generated data was evaluated on its statistical
properties, yielding solid results. Finally, it was shown that GANs have made
considerable progress in their finance applications and can be a solid
additional tool for data scientists in this field
