The Definitive Guide to Synthetic Data for Financial Services


Without access to data—critical digital initiatives, such as innovative fintech applications enhancing customer experiences and AI/ML projects pinpointing complex fraud are at a standstill. Organizations face a set of acute challenges when trying to operationalize data including data privacy, data quality, and data quantity. 

Synthetic data helps financial services companies make the most of their data, while safeguarding sensitive information. It is generated by computer simulations or algorithms to resemble the patterns, distributions, and insights held in real-world data without exposing any of the private or sensitive information present in the original dataset.

In this guide, we explore: 

  • The promise of synthetic data and its key benefits for financial services companies
  • Fundamentals to synthetic data including how it works, where it comes from, and how to get started 
  • Practical financial services use cases of synthetic data, including real-world examples of Gretel, the leading synthetic data platform for financial services, leveraged in fintech application
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