  Generative Adversarial Networks (GANs) became very popular for generation of
realistically looking images. In this paper, we propose to use GANs to
synthesize artificial financial data for research and benchmarking purposes. We
test this approach on three American Express datasets, and show that properly
trained GANs can replicate these datasets with high fidelity. For our
experiments, we define a novel type of GAN, and suggest methods for data
preprocessing that allow good training and testing performance of GANs. We also
discuss methods for evaluating the quality of generated data, and their
comparison with the original real data.
