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Synthetic Data Applications in Finance (2401.00081v2)

Published 29 Dec 2023 in cs.LG and q-fin.GN

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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Synthetic Data Applications in Finance

The paper "Synthetic Data Applications in Finance" provides a comprehensive review of the uses and implications of synthetic data within the financial sector. The authors highlight the significance of synthetic data, particularly in navigating the regulatory complexities associated with real financial data. The discussion is rooted in the potential of synthetic data to advance privacy, fairness, and explainability in financial applications.

Key Applications of Synthetic Data in Finance

The paper identifies several key applications of synthetic data, emphasizing its broad utility across various financial domains:

  1. Data Liberation: Synthetic data serves as an instrument to ease the restrictions on data usage and sharing imposed by stringent regulatory and privacy requirements. By transforming real data into synthetic forms, financial institutions can circumvent some of the bureaucratic hurdles associated with data privacy, thus facilitating a more seamless integration of AI models into their operations.
  2. Data Augmentation: The paper discusses the role of synthetic data in augmenting datasets to enhance the performance of machine learning models. This is particularly relevant in scenarios where the availability of real data is sparse or imbalanced, as synthetic data can help fill these gaps and diversify training samples.
  3. Counterfactual Scenarios and Testing: Synthetic data provides a controlled environment to test hypotheses and benchmark models against hypothetical market scenarios, which can help in reinforcing the robustness of models to distributional shifts and rare market events.

Practical and Theoretical Implications

The paper underscores synthetic data's potential to revolutionize risk management, trading strategies, and fraud detection. Among the strong numerical results discussed, the deployment of synthetic data significantly improves the generalization capabilities of models trained under limited data conditions, leading to better real-world performance across various financial tasks.

Theoretical advancements are noted in the synthesis of data across multiple modalities, including tabular, time-series, event-series, and unstructured data. The discussion includes innovative approaches such as GANs and variational autoencoders, as well as frameworks to evaluate the epistemic parity of synthetic data against its real counterparts.

Future Directions and Challenges

The paper discusses the ongoing challenges in the field, such as developing metrics to evaluate synthetic data's fidelity and utility, understanding the privacy guarantees of synthetic data, and tackling the ethical considerations surrounding its use. The authors emphasize the need for future research to focus on improving the interpretability and transparency of synthetic data generation methods, as well as exploring the use of synthetic data in more complex, multimodal data scenarios.

In conclusion, the paper presents synthetic data as a pivotal tool in the finance domain, with capabilities to propel innovation while adhering to regulatory standards. The exploration into synthetic data is likely to evolve, encompassing broader applications and more sophisticated generation techniques, making it an area ripe for further research and investment in financial AI.

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Authors (20)
  1. Vamsi K. Potluru (28 papers)
  2. Daniel Borrajo (33 papers)
  3. Andrea Coletta (15 papers)
  4. Niccolò Dalmasso (32 papers)
  5. Yousef El-Laham (16 papers)
  6. Elizabeth Fons (14 papers)
  7. Mohsen Ghassemi (12 papers)
  8. Sriram Gopalakrishnan (23 papers)
  9. Vikesh Gosai (1 paper)
  10. Eleonora Kreačić (12 papers)
  11. Ganapathy Mani (4 papers)
  12. Saheed Obitayo (4 papers)
  13. Deepak Paramanand (2 papers)
  14. Natraj Raman (13 papers)
  15. Mikhail Solonin (2 papers)
  16. Srijan Sood (8 papers)
  17. Svitlana Vyetrenko (39 papers)
  18. Haibei Zhu (7 papers)
  19. Manuela Veloso (105 papers)
  20. Tucker Balch (61 papers)
Citations (10)
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