Privacy Safe Synthetic Data for Healthcare and Finance AI
Training and evaluating AI in healthcare and finance is a nightmare. You need diverse, realistic data, but you cannot share it. Real patient records and transaction histories are protected by strict laws. Even anonymized data can leak identities through re-identification attacks. And labeling high-quality samples for supervised learning is expensive and slow.
The privacy problem with real data
Healthcare and finance are among the most regulated sectors. HIPAA, GDPR, and other regulations limit what you can collect, store, and share. Real-world datasets are often too small, too biased, or too risky to ship between teams or partners. When you do share data, you must invest heavily in de-identification, which can still fail. The risk of a breach or a privacy lawsuit is real.
What synthetic data actually solves
Synthetic data is generated algorithmically. It mimics the statistical properties and relationships of real data without containing actual records. For machine learning, this means you can train and evaluate models on large, diverse, and perfectly aligned datasets without touching any real private information. Synthetic data also makes it trivial to create edge cases that rarely appear in production, improving model robustness.
Concrete benefits with real numbers
Research shows that synthetic data can match or exceed the performance of real data for many downstream tasks. A 2023 study on healthcare synthetic images found that models trained on synthetic data achieved 94% of the accuracy of models trained on real data, while avoiding all privacy concerns. In finance, synthetic transaction data has been used to train fraud detection systems that generalize to new types of fraud patterns without exposing any real customer records. The key is that the generator must capture the true distribution of the data, including rare events and complex correlations.
Key tradeoffs and techniques
- ●Quality depends on the generator: simple models may miss important patterns, while complex generative models can hallucinate unrealistic relationships.
- ●Evaluation gap: synthetic data may not perfectly match real-world noise, edge cases, or evolving data drift.
- ●Drift management: you must periodically retrain or update the generator to keep synthetic distributions aligned with real-world changes.
- ●Domain alignment: synthetic data should be validated against real data on key metrics, not just superficial statistics.
- ●Legal compliance: synthetic data itself is generally not protected by privacy laws, but you must still follow data governance and consent policies.
Synthetic data solves the privacy and access problem, but only if you build or obtain high-quality generators that truly reflect your data domain.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. That means the synthetic datasets it produces are grounded in actual user workflows, not just tabular statistics. Teams can work with Coasty to build custom synthetic datasets tailored to their specific healthcare or finance use cases. This is a custom, contact-led service, no self-serve platform, no fixed packages. You talk to the Coasty data team to define your data requirements, and they build the synthetic data pipeline around them.
If you need privacy-safe synthetic data for healthcare or finance AI, the first step is to understand how it can fit into your pipeline. Coasty offers a custom synthetic data service that can generate realistic interaction data tailored to your domain. Book a data call with the Coasty data team to explore what’s possible: https://cal.com/coasty/coasty-data-call