Model Training & Adaptation
Synthetic data
Artificially generated examples used for training, testing, or augmentation.
Example
A simulator creates additional rare fraud examples when real cases are scarce.
Why it matters
It helps cover scarce or difficult-to-collect cases during learning and testing.
You'll hear it
“These practice records were generated, not collected from real customers.”
Can you control it?
Set by the team
The people building or running the AI choose this setup. An everyday user generally needs their help to change how this part works.
Common questions
- Does artificial mean free of privacy concerns?
- No. Generated material can still reveal or reproduce information from sensitive sources.
- Can it make a test unrealistically easy?
- Yes. Repeated artificial patterns may not reflect the messiness of real cases.
- Can too much of it reduce useful variety?
- Yes. A generator can repeat narrow patterns and leave important situations underrepresented.