Synthetic Data Applications in Finance
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.
Keywords
Cite
@article{arxiv.2401.00081,
title = {Synthetic Data Applications in Finance},
author = {Vamsi K. Potluru and Daniel Borrajo and Andrea Coletta and Niccolò Dalmasso and Yousef El-Laham and Elizabeth Fons and Mohsen Ghassemi and Sriram Gopalakrishnan and Vikesh Gosai and Eleonora Kreačić and Ganapathy Mani and Saheed Obitayo and Deepak Paramanand and Natraj Raman and Mikhail Solonin and Srijan Sood and Svitlana Vyetrenko and Haibei Zhu and Manuela Veloso and Tucker Balch},
journal= {arXiv preprint arXiv:2401.00081},
year = {2024}
}
Comments
50 pages, journal submission; updated 6 privacy levels