Privacy Safe Synthetic Data for Healthcare and Finance AI
Healthcare and finance teams need large, high-quality datasets to train AI, but real data is often scarce, fragmented, or legally restricted. Data privacy laws like HIPAA and GDPR make sharing patient records or transaction histories risky. Synthetic data offers a way to train and evaluate models without exposing any real information.
Real privacy savings: no PII exposure
When you generate synthetic records, you never store the original Personally Identifiable Information. A study of 3 million synthetic electronic health records showed a 100% reduction in re-identification risk compared with raw data. In finance, synthetic transaction datasets with identical statistical properties have been used to backtest fraud detection models without ever seeing real IDs. This means you can share data across teams, vendors, or research partnerships without legal concerns.
Statistical fidelity keeps models honest
Good synthetic data preserves the underlying distributions, correlations, and rare events that matter for the task. An experiment with synthetic medical imaging data showed 92% similarity in key clinical metrics versus real data. In finance, synthetic loan portfolios with the same default rates and income distributions achieved comparable model performance on credit scoring. The key is to validate the synthetic data against real benchmarks before trusting the model outputs.
Common pitfalls you should avoid
- ●Assuming any synthetic data is automatically safe, validation against real data is required.
- ●Over-simplifying rare events, which can cause underperformance in critical use cases.
- ●Using synthetic labels without checking that they match the real labeling conventions.
- ●Ignoring correlation structure, leading to unrealistic scenarios that mislead models.
The takeaway: privacy-safe synthetic data lets you train and evaluate AI on statistically realistic data without ever exposing real PII. But quality and alignment with your domain are critical.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers, capturing realistic interaction data across complex workflows. This data can be transformed into custom synthetic datasets and trajectories for training and evaluating agents and models. The service is custom and contact-led, there is no self-serve portal or fixed package. You discuss your use case, domain constraints, and data needs with the team, and they build a synthetic dataset tailored to your requirements.
Ready to explore privacy-safe synthetic data for your healthcare or finance AI project? Book a data call with the Coasty team at https://cal.com/coasty/coasty-data-call to start a conversation.