Privacy Safe Synthetic Data for Healthcare and Finance AI

Why real data is a bottleneck

Medical imaging repositories are fragmented across hospitals. Financial transaction logs sit deep inside banks, each with strict access controls. Gaining broad access to these datasets often requires data use agreements, legal review, and sometimes even anonymization that can still leak information. In healthcare, a single 2023 study found that 90 percent of hospitals reported difficulties in sharing data for research. In finance, banks spend an average of 5 to 10 percent of their AI budget just on data acquisition and governance. The cost and complexity of real data slow down model development and make iterative experimentation painful.

How synthetic data works in practice

Synthetic data is generated by models that learn the statistical patterns of real datasets and then produce new examples that follow those patterns. In healthcare, this can create synthetic EHR entries, lab results, and radiology reports that preserve the joint distribution of diseases, demographics, and outcomes. In finance, synthetic transactions can mimic the timing, amount, and relationship patterns of real credit card usage or loan applications. A 2022 benchmark in JAMA Network Open showed that models trained on synthetic medical imaging achieved performance within 2 percent of models trained on real images when the synthetic data captured key disease prevalence ratios. In finance, synthetic datasets have been used to train fraud detection models with false positive rates within 3 to 5 percent of models trained on real data, provided the synthetic data replicates the class imbalance and transaction volume spikes typical of fraud.

Key privacy techniques you should know

• ●Differential privacy: inject carefully calibrated noise into the training process so that removing or adding a single individual does not change model outputs.
• ●Generative adversarial networks or diffusion models: learn the distribution of features and then sample synthetic points that are statistically indistinguishable from real records.
• ●Semantic preservation: ensure synthetic data keeps domain-relevant constraints, like valid ranges for blood pressure or correct currency formats.
• ●Statistical validation: use metrics such as KL divergence, Wasserstein distance, or feature-wise correlation checks to compare synthetic and real distributions.
• ●Adversarial testing: run models on synthetic data and compare their behavior to models trained on real data to catch hidden biases or performance gaps.

How Coasty fits

Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data and trajectories. This approach makes the synthetic datasets Coasty produces especially valuable for training and evaluating AI models that need to understand complex workflows, user interfaces, and multi-step tasks. Teams can work with the Coasty data team to define the scope, constraints, and formats needed for their use case. The offering is a custom synthetic data service built around your requirements, not a fixed product or public price list.