Using Synthetic Data to Train AI Models

Synthetic data is artificially generated data that mimics the statistical properties of real data, useful when real examples are too scarce, too sensitive to use directly, or too imbalanced across categories.

For fraud detection, for example, real fraud cases are rare, and generating realistic synthetic fraud patterns helps train a model to recognise categories it would otherwise rarely see.

We use synthetic data as a supplement to real data rather than a replacement, since models trained purely on synthetic examples can miss the nuances of real-world behaviour.