# Synthetic data for rare events

> Train fraud, defect or anomaly models when real positive examples are rare, sensitive or slow to label.

Source: https://aibyos.com/research/synthetic

Research · Machine learning

# Synthetic data for rare events

Train fraud, defect or anomaly models when real positive examples are rare, sensitive or slow to label.

**weeks**instead of months to first model

**privacy**no real personal data in training

**balanced**classes for rare events

## Pipeline

1.  Profile real data and rare-event patterns
2.  Generate synthetic records with statistical and LLM-based generators
3.  Validate fidelity and privacy (no memorised records)
4.  Train and test on real hold-out data only
5.  Monitor drift once in production

Figures show the typical order of magnitude for this approach compared with calling a large general-purpose model. Actual results depend on the task and data; we measure them on your data during the baseline phase.

[Discuss a project like this](https://aibyos.com/contact)

## Typical tooling

-   SDV
-   PyTorch
-   XGBoost
-   Great Expectations

## Related

[All research](https://aibyos.com/research)[Sovereign GPU compute](https://aibyos.com/gpu)[Fine-tuning](https://aibyos.com/services/fine-tuning)