Healthcare and finance teams hit the same wall: they need massive, high-quality datasets to train and evaluate AI, but real data is risky to share and expensive to clean. PHI, PII, and financial records come with strict regulations that limit who can touch them. Synthetic data fixes this by creating realistic but entirely artificial records. You get the statistical properties of real data without exposing any real people or transactions.
Healthcare AI is starved for labeled data
In medical imaging, radiologists label thousands of scans for model training. But hospitals can only share a fraction of that data due to privacy rules. One study estimated that only about 1.5% of de-identified medical images are actually used in public training sets, while the rest sits locked in proprietary archives. The result is model drift and poor generalization. Synthetic data can fill that gap by generating thousands of additional labeled examples that mimic the distribution of real scans without exposing patient identities.
Finance data has its own constraints
Banks and fintechs face similar constraints. Transaction histories, fraud patterns, and risk scores are gold for training models, but sharing them is a compliance headache. A recent survey found that 72% of financial services firms cite data privacy as a top blocker to AI adoption. Synthetic transaction data allows teams to build and stress-test fraud detection models in sandbox environments, then deploy them with confidence that no real customer data is involved.
Why synthetic data works for privacy
Synthetic data is generated from real data using statistical models or generative AI. The output preserves statistical relationships, correlations between age, income, and claim severity, for example, but replaces any direct identifiers with random values. If a real dataset contains a patient with ID 12345 and salary $80k, the synthetic version will have a patient with ID 99999 and salary $82k. No real person is represented. This makes it easier to get clearance for internal use and to share datasets across organizations without legal risk.
The key is fidelity: synthetic data must reproduce the statistical properties of real data, not just look plausible. If you lose important correlations, your model will underperform on real cases.
How Coasty builds synthetic datasets
Coasty specializes in synthetic data for agents and models by running computer use agents on real desktops and browsers. These agents interact with live applications and workflows, capturing realistic sequences of actions, clicks, and system states. From those interactions, Coasty can generate synthetic datasets and trajectories that reflect how humans actually work. The service is custom and contact-led: you talk to the Coasty data team about your specific use case, and they build a synthetic dataset tailored to your needs.
If you want to train or evaluate AI on realistic privacy-safe data, start by booking a data call with the Coasty team at https://cal.com/coasty/coasty-data-call .
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