Privacy Safe Synthetic Data for Healthcare and Finance AI
Building AI models on real healthcare or financial data runs into two hard walls: regulations and economics. HIPAA limits who can access patient records. Banking rules keep transaction histories locked behind strict privacy gates. Real data is often fragmented or too sparse for robust model training. Synthetic data addresses both problems by creating realistic but fictional records that retain statistical properties of the real world.
Regulatory risk keeps models grounded
Healthcare and finance operate under some of the strictest data regimes. In the US, the Health Insurance Portability and Accountability Act (HIPAA) requires safeguards for protected health information (PHI). EU GDPR adds another layer with data minimization and rights to erasure. Real data breaches are common: the 2023 Change Healthcare ransomware attack exposed over 100 million records. Using real records in production or training increases exposure. Synthetic data sidesteps these risks because it contains no real individuals or accounts. Teams can share synthetic sets internally or externally without triggering consent or regulatory review.
Real data is expensive and hard to get right
Beyond compliance, real data access is limited by vendors, contracts, and geography. A 2024 analysis of healthcare data initiatives found that more than 60% of projects cite "restricted data access" as a primary blocker. Data engineering time to join disparate sources and clean messy fields can consume 60-80% of project budgets. Synthetic data reduces that friction. You define schemas, distributions, and constraints once, then generate millions of rows that match realistic patterns. This cuts data acquisition costs and speeds up iteration cycles. Studies in healthcare analytics show synthetic datasets can achieve 95% of the statistical similarity to real populations for key variables like age, gender, and comorbidities when properly generated.
Anonymization is not enough
Many teams assume they can remove names, IDs, and direct identifiers to anonymize records. Research shows that re-identification is possible even after aggressive anonymization. A 2022 study demonstrated that combining de-identified healthcare records with public data can accurately re-identify individuals with over 80% success rates. Synthetic data attacks this problem from a different angle: it never contains real entities. The synthetic population is entirely fictional but statistically faithful. This protects against both internal misuse and external re-identification attempts.
The key advantage: you can train and evaluate AI models on datasets that are statistically realistic and legally safe.
How Coasty fits
Coasty builds computer use agents that run on real desktops and browsers to capture realistic user interactions. This gives Coasty access to rich behavioral data that reflects how people actually work with software. The team can translate those interaction patterns into synthetic trajectories and datasets tailored to specific domains. Coasty offers a custom synthetic data service for healthcare and finance projects. There is no public catalog or fixed pricing. The offering is contact-led and tailored to your use case, compliance requirements, and data needs.
If you need high-quality, privacy-safe data for healthcare or finance AI, explore what Coasty can build for you. Book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call to discuss your requirements.