Financial AI Data
Done Right

We use a combination of techniques: individual identifier removal, temporal perturbation within signal-preserving bounds, generalization of rare demographic attributes, and differential privacy mechanisms where required. The key design principle is that anonymization is applied per-feature based on its contribution to the ML signal — high-signal behavioral features are anonymized more carefully to preserve their usefulness. Our compliance team includes specialists in financial data regulation including GDPR Article 89 and relevant banking authority guidelines.

Yes — this is one of the most important aspects of our fraud datasets and one of the hardest things to replicate with synthetic data. We include the full transaction and behavioral sequence in the 30 to 90 days preceding each labeled fraud event. This pre-fraud behavioral signal is where the most useful fraud detection patterns live, and it can only come from real data. Synthetic fraud datasets typically only model the fraud event itself, missing the leading indicators.