To overcome the restrictions on the use of personal data under the privacy regulations (GDPR CCPA, etc.), we generate high‑quality synthetic data that accurately represents the production data without the risk of PII leakage or privacy regulation breaches.
Our solution helps financial institutions to cope with the following:
- Development and training of AI/ML: replacing real sensitive data with a view to extracting patterns and insights using machine learning techniques. Moreover, our solution may be used for data augmentation in order to improve ML-based results, and to easily acquire labelled training data which is expensive to annotat manually.
- Agile development and DevOps: artificially generated data is the best choice, as it eliminates the need to wait for ‘real’ data also known as ‘test data’. This will lead to decreased test time and increased flexibility and agility during development.
- Marketing: synthetic data allows marketing units to run detailed, individual-level simulations to improve marketing expenditure. Such simulations would not be allowed without user consent due to GDPR, whereas synthetic data, which mimic the properties of real data, can be reliably used in simulation
- Research: to help better understand the format of real sensitive data that cannot be shared, develop an understanding of its specific statistical properties, fine-tune parameters for related algorithms, or build preliminary models