Synthetic Data
Measuring and using synthetic data where real data is restricted.
Evaluate → apply
Data scientists, privacy officers
1 → 2
Reading path
Start at Part 1, or jump to the part you needHow to evaluate the quality of synthetic data: fidelity, utility, and privacy
Regulated firms cannot freely use real data, but synthetic data is only useful if you can trust it.
Augment fraud transactions using synthetic data in Amazon SageMaker
Fraud models lack enough high-quality fraud examples to train on.
Other series
The Autonomous NOC
A self-improving AI reasoning layer that turns an alarm storm into one root cause in under 90 seconds, safely enough to act on a live network.
Bank Foundation Models
Why and how a bank should train a foundation model on its own event data, from privacy-first data to Nova Forge.
GAD-P: Governed data access
Natural-language access to enterprise data with governance enforced by code, not prompts.
Dark Web Fraud Signals
Turning dark-web chatter into early-warning signals for bank fraud models.
Real-time Fraud with TGNs
Real-time graph fraud detection with sub-100 ms inference that generalises across payment domains.
The Fast Path
Cutting bank loan decisions from days to minutes in six weeks.
AI Value Strategy
Closing the gap between AI ambition and measurable value, for both customers and partners.