Using Synthetic Data to Train AI Models

Synthetic data, artificially generated data that mimics the statistical properties of real data, has become a practical tool for training AI models when real examples are too scarce, too sensitive to use directly, or too imbalanced across categories to train a reliable model. Used well, it fills specific gaps in a training dataset rather than replacing real data entirely.

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.

Synthetic data is particularly valuable in three situations: rare-event classes like fraud or equipment failure, where real examples are too few to train on reliably; privacy-sensitive domains like healthcare, where sharing real patient data across teams or vendors raises real compliance concerns; and class imbalance more generally, where one category dominates the dataset so heavily that a model trained on it alone would simply predict the majority class most of the time.

The risk with synthetic data is that it can encode the assumptions of whatever process generated it, rather than the true complexity of real-world behaviour, which means a model trained purely on synthetic examples can perform well on synthetic test data while missing subtle real-world patterns entirely. Validating synthetic-trained models against a held-out set of real examples, wherever those exist, is essential before trusting the results.

We typically use synthetic data to augment a real dataset's weak spots, rather than as the primary training source, and we validate the resulting model's performance specifically on the rare categories the synthetic data was meant to help with, not just on overall accuracy.