Financial data is often sensitive and subject to strict privacy laws, making it difficult to use and distribute for research purposes outside of financial institutions.
An alternative solution is to generate artificial data that mimics the characteristics of real data, protecting the confidentiality of customers’ personal information.
Researchers identified three key requirements for generative frameworks to create synthetic financial data and explored various techniques and methods used to generate and evaluate synthetic financial data, such as supervised and unsupervised machine learning approaches.
The use of synthetic data in finance has the potential to revolutionize the industry and facilitate research and development while still prioritizing customer privacy.
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