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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| Accès en ligne: | https://arxiv.org/abs/2401.00081 |
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| _version_ | 1866910376983003136 |
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| author | Potluru, Vamsi K. Borrajo, Daniel Coletta, Andrea Dalmasso, Niccolò El-Laham, Yousef Fons, Elizabeth Ghassemi, Mohsen Gopalakrishnan, Sriram Gosai, Vikesh Kreačić, Eleonora Mani, Ganapathy Obitayo, Saheed Paramanand, Deepak Raman, Natraj Solonin, Mikhail Sood, Srijan Vyetrenko, Svitlana Zhu, Haibei Veloso, Manuela Balch, Tucker |
| author_facet | Potluru, Vamsi K. Borrajo, Daniel Coletta, Andrea Dalmasso, Niccolò El-Laham, Yousef Fons, Elizabeth Ghassemi, Mohsen Gopalakrishnan, Sriram Gosai, Vikesh Kreačić, Eleonora Mani, Ganapathy Obitayo, Saheed Paramanand, Deepak Raman, Natraj Solonin, Mikhail Sood, Srijan Vyetrenko, Svitlana Zhu, Haibei Veloso, Manuela Balch, Tucker |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_00081 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Synthetic Data Applications in Finance Potluru, Vamsi K. Borrajo, Daniel Coletta, Andrea Dalmasso, Niccolò El-Laham, Yousef Fons, Elizabeth Ghassemi, Mohsen Gopalakrishnan, Sriram Gosai, Vikesh Kreačić, Eleonora Mani, Ganapathy Obitayo, Saheed Paramanand, Deepak Raman, Natraj Solonin, Mikhail Sood, Srijan Vyetrenko, Svitlana Zhu, Haibei Veloso, Manuela Balch, Tucker Machine Learning General Finance 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. |
| title | Synthetic Data Applications in Finance |
| topic | Machine Learning General Finance |
| url | https://arxiv.org/abs/2401.00081 |