Privacy Safe Synthetic Data for Healthcare and Finance AI

The real cost of real training data

Healthcare data alone is projected to reach 40 petabytes by 2025, and most of it stays locked in silos. Finance firms spend millions on data acquisition and de-identification because a single breach can trigger penalties of hundreds of millions of dollars. Even when you can access data, you often lack the labels and diversity needed to build robust models.

What synthetic data actually is

Synthetic data is generated algorithmically rather than collected from people. It mimics the statistical properties of real data, ranges, distributions, correlations, and rare events. The goal is to produce outputs that look and behave like the real thing so models can learn patterns without seeing any actual user records. Modern techniques like generative models and copula-based methods have reached a point where synthetic records can pass basic statistical tests with human observers often unable to distinguish them from real data.

Why privacy and compliance improve with synthetic data

Because synthetic records never came from a real person, you do not need consent, de-identification, or complex governance pipelines. Healthcare providers can share synthetic datasets for collaboration without risking HIPAA violations. Financial institutions can use synthetic data for stress testing and fraud detection without exposing transaction histories. In practice, synthetic data reduces the legal risk surface dramatically, most compliance frameworks treat well-designed synthetic datasets as distinct from real personal data.

Real tradeoffs you need to know

• ●Synthetic data can miss rare events or edge cases that appear only in real-world data.
• ●Models trained primarily on synthetic data may underperform on real-world distribution shifts.
• ●High-quality generation requires careful modeling of domain knowledge and legal constraints.
• ●Data scientists must validate synthetic outputs against real benchmarks to avoid bias and drift.

How Coasty fits

Coasty runs computer use agents on real desktops and browsers, which lets the team capture realistic interaction data and produce synthetic datasets and trajectories for training and evaluating AI. This approach can help teams create custom synthetic datasets that reflect the complexity of healthcare workflows, financial processes, and other domain-specific tasks. Coasty’s offering is custom and contact-led: no self-serve platform, no fixed packages, and no public price list. To explore what is possible for your use case, you talk directly with the Coasty data team.