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Synthetic Data Applications in Finance

Author

Listed:
  • Vamsi K. Potluru
  • Daniel Borrajo
  • Andrea Coletta
  • Niccol`o Dalmasso
  • Yousef El-Laham
  • Elizabeth Fons
  • Mohsen Ghassemi
  • Sriram Gopalakrishnan
  • Vikesh Gosai
  • Eleonora Kreav{c}i'c
  • Ganapathy Mani
  • Saheed Obitayo
  • Deepak Paramanand
  • Natraj Raman
  • Mikhail Solonin
  • Srijan Sood
  • Svitlana Vyetrenko
  • Haibei Zhu
  • Manuela Veloso
  • Tucker Balch

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.

Suggested Citation

  • Vamsi K. Potluru & Daniel Borrajo & Andrea Coletta & Niccol`o Dalmasso & Yousef El-Laham & Elizabeth Fons & Mohsen Ghassemi & Sriram Gopalakrishnan & Vikesh Gosai & Eleonora Kreav{c}i'c & Ganapathy Ma, 2023. "Synthetic Data Applications in Finance," Papers 2401.00081, arXiv.org, revised Mar 2024.
    • Handle: RePEc:arx:papers:2401.00081
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    References listed on IDEAS

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