Privacy Safe Synthetic Data for Healthcare and Finance AI
Healthcare and finance organizations are racing to build AI but face a hard choice: they either lock down data and stall progress, or they expose sensitive records and risk fines, lawsuits, reputational damage, and even loss of patient trust. Real-world data is still the main fuel for most models, but it is expensive, hard to label, and heavily regulated. Synthetic data offers a third path: realistic, privacy-safe training and evaluation data that you can generate at scale without ever touching a real record. This approach is now realistic enough to replace real data in many core tasks, and the gap between synthetic and real performance keeps shrinking.
Why synthetic data matters for regulated industries
Regulated domains like healthcare and finance have strict rules around data sharing. HIPAA and GDPR mean you cannot simply move real patient or financial data between departments, institutions, or even countries. Data that does move often requires costly de‑identification, which still leaves residual risk and doesn't help with training complex models that need millions of examples. Synthetic data changes that dynamic. You generate entirely new records that are statistically similar to the real population but have no real individuals attached. This lets you share datasets across teams, partners, and even countries without any privacy risk. In practice, teams using synthetic data have reported up to 80 percent reduction in data access requests and zero confirmed breaches tied to the synthetic set.
How synthetic data is actually built
Modern synthetic data generation relies on models that learn the underlying distribution of real data and then sample from it. Common techniques include conditional generative models, tabular generators, and diffusion models adapted for structured data. For healthcare, generative models are trained on de‑identified EHR tables, including demographics, lab results, diagnoses, and medication history. For finance, they learn patterns from transaction logs, account balances, loan applications, and credit decisions. The models produce new rows that preserve statistical properties like correlations and marginal distributions, but the values are fabricated. One study found that a finance synthetic dataset achieved 96 percent of the predictive power of the original data on a default prediction task while using only synthetic records. The key is tight validation: you compare synthetic and real distributions on dozens of metrics before deploying synthetic data into production.
Real tradeoffs and limitations
Synthetic data is not a magic bullet. Here are the main tradeoffs you need to manage: - Representativeness: Synthetic data can miss rare but important events or subpopulations. You must deliberately oversample those groups during generation. - Correlation fidelity: Some models struggle to preserve complex multi‑variable relationships. Regular validation checks are essential. - Evaluation risk: If you rely on synthetic data for testing, you may miss edge cases that only appear in the real world. - Regulatory acceptance: Regulators are still developing guidance. You must be ready to explain the generation process and validation methodology to auditors. - Integration effort: Synthetic data formats may not match your existing pipelines. You need tooling to transform and ingest them efficiently.
Synthetic data can replace real data for training and evaluation in many core tasks, but you must validate it thoroughly and be prepared to handle edge cases and regulatory scrutiny.
How Coasty fits into the picture
Coasty builds computer use agents that run on real desktops and browsers, capturing realistic interaction data and trajectories across applications and workflows. That experience lets the team create synthetic datasets and trajectories that reflect actual user behavior in complex environments. For healthcare and finance teams, this means you can generate synthetic data that mirrors the way people interact with your systems, from logging into portals to filling forms, navigating compliance screens, and making decisions. Coasty does not offer a self‑serve product or fixed packages. Instead, it provides a custom synthetic data service. You talk to the Coasty data team to define your use case, data requirements, and constraints, and they build a bespoke dataset that fits your workflow and compliance needs.
Privacy safe synthetic data lets healthcare and finance teams train and evaluate AI without exposing real records. If you want to explore how synthetic data can reduce risk and accelerate model development, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call .