Rare Events and Edge Cases: Where Synthetic Data Wins
Most AI systems excel at common patterns. They struggle when the data distribution shifts to rare events. Real-world examples include fraud in a tiny fraction of transactions, rare medical symptoms, or edge cases in autonomous driving. Collecting enough labeled examples for these cases is expensive, slow, and sometimes unsafe. Synthetic data offers a practical alternative.
Real data is biased toward the majority
Datasets reflect how data naturally flows. In many domains the majority cases dominate. A 2024 study on financial fraud detection found that only 0.15% of transactions were fraudulent. Even large credit card datasets might have fewer than 100,000 fraud examples. Models trained on this imbalance tend to miss the minority class. Synthetic data lets you artificially boost these rare cases.
Synthetic data boosts coverage without bias
With generative techniques you can create thousands of synthetic examples for the rare class. For a rare medical imaging condition, you might generate 10,000 synthetic scans while keeping the original dataset intact. This increases the effective sample size without overrepresenting the condition in real-world data. Synthetic examples also help the model learn subtle variations rather than memorizing a few real cases.
Edge cases need diversity, not just volume
Rare events often come with many edge case variants. Consider a self-driving system encountering unusual weather, a new road layout, or an unexpected obstacle. Synthetic environments can systematically vary these factors. You can generate thousands of scenarios with different lighting, road conditions, and obstacle positions. This diversity is hard to achieve by waiting for real-world encounters.
Evaluate models on out-of-distribution data
A model that looks accurate on the training set may fail on unseen data. Synthetic data helps you simulate out-of-distribution scenarios before deployment. For example, you can generate synthetic user journeys that include malicious behaviors the model has never seen. This lets you stress-test the system and identify blind spots early.
The key takeaway: synthetic data is not a perfect replacement for real data. It is a strategic tool to address coverage gaps, especially for rare events and edge cases.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers. It captures realistic interaction data and can produce custom synthetic datasets and trajectories. This makes it possible to generate synthetic examples that reflect real user workflows, including rare actions and edge case tasks. The service is custom and contact-led, meaning you work directly with the Coasty team to define your data needs and scope the project.
If you need synthetic data for rare events or edge cases, the next step is to book a data call with the Coasty data team. Visit https://cal.com/coasty/coasty-data-call to schedule a conversation about your requirements and explore how Coasty can help.