Summary of Synthetic Data Applications in Finance, by Vamsi K. Potluru et al.
Synthetic Data Applications in Finance
by 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
First submitted to arxiv on: 29 Dec 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: General Finance (q-fin.GN)
GrooveSquid.com Paper Summaries
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary As AI educators write for technical audiences, we can summarize this abstract as follows: The paper presents a comprehensive overview of synthetic data applications in finance, highlighting various modalities such as tabular, time-series, event-series, and unstructured data. It explores the potential of synthetic data to address privacy, fairness, and explainability concerns in this highly regulated industry. Evaluation metrics are used to assess the quality and effectiveness of these approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Synthetic data is like fake money for computers! Imagine if you could create fake financial data that’s just as good as real data, but without actually revealing personal or financial information. This paper shows how synthetic data can be used in finance to solve problems like privacy concerns. It looks at different types of data, like numbers and events, and shows how this fake data can be used to make better decisions. |
Keywords
* Artificial intelligence * Synthetic data * Time series
