Synthetic data is artificially generated to resemble real-world data without containing actual real-world records. It is used to fill coverage gaps where real examples are rare, sensitive, or expensive to collect, but typically performs best when validated against a real-data benchmark.
Example
Generating thousands of synthetic transaction records to train a fraud detection model, since real fraud examples are rare and sensitive.