Using generative adversarial networks to synthesize artificial financial datasets

Dmitry Efimov, Di Xu, Luyang Kong, Alexey Nefedov and Archana Anandakrishnan

Papers from arXiv.org

Abstract: 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.

Date: 2020-02
New Economics Papers: this item is included in nep-big and nep-net
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Citations: View citations in EconPapers (5)

Published in Robust AI in FS 2019 : NeurIPS 2019 Workshop on Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy, December 2019, Vancouver, Canada

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2002.02271

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