Augment fraud transactions using synthetic data in Amazon SageMaker
- The problem
- Fraud models lack enough high-quality fraud examples to train on.
- Why it matters
- Uses synthetic data in SageMaker to augment scarce fraud transactions while respecting privacy.
- Who should read it
- ML engineers, fraud data science
- What you'll get
- A hands-on augmentation pattern.
- Series
- Synthetic Data, Part 2 of 2
The full article lives on AWS Blog. This page is a short guide to what it covers.