Related papers: Synthetic Data Applications in Finance

Synthetic Data Applications in Finance

URL: http://arxiv.org/abs/2401.00081v2
Date: Wed, 20 Mar 2024 20:21:35 GMT
Title: Synthetic Data Applications in Finance
Authors: Vamsi K. Potluru, Daniel Borrajo, Andrea Coletta, Niccolò Dalmasso, Yousef El-Laham, Elizabeth Fons, Mohsen Ghassemi, Sriram Gopalakrishnan, Vikesh Gosai, Eleonora Kreačić, Ganapathy Mani, Saheed Obitayo, Deepak Paramanand, Natraj Raman, Mikhail Solonin, Srijan Sood, Svitlana Vyetrenko, Haibei Zhu, Manuela Veloso, Tucker Balch,
Abstract summary: We present a broad overview of applications of synthetic data in the financial sector. Synthetic data is a potential approach for dealing with issues related to privacy, fairness, and explainability.
Score: 11.979696873104096
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Synthetic data has made tremendous strides in various commercial settings including finance, healthcare, and virtual reality. We present a broad overview of prototypical applications of synthetic data in the financial sector and in particular provide richer details for a few select ones. These cover a wide variety of data modalities including tabular, time-series, event-series, and unstructured arising from both markets and retail financial applications. Since finance is a highly regulated industry, synthetic data is a potential approach for dealing with issues related to privacy, fairness, and explainability. Various metrics are utilized in evaluating the quality and effectiveness of our approaches in these applications. We conclude with open directions in synthetic data in the context of the financial domain.

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