Data validation and advisory
Synthetic data is only as good as its ability to behave like the real thing. We don’t stop at generation — we ensure every dataset performs under real analytical pressure.
Multi-Layer Validation Framework
We employ a combination of statistical, structural, and behavioral validation to ensure synthetic datasets are reliable for financial modeling.
Statistical Alignment
Distributions and correlations are tested with KS, KL divergence, and Pearson metrics to match real-world baselines.
Structural Integrity
All entities are validated through referential constraints (e.g. transaction.account_id ∈ accounts). No orphan records.
Behavioral Realism
Models simulate real transaction cycles. If a credit model trained on synthetic data yields AUC within 5% of real-data, it passes.
Stress Testing for
Quant & Risk Models
We build validation scenarios that mirror extreme financial conditions — liquidity shocks, default cascades, or sudden volatility spikes.
By injecting anomalies into synthetic datasets, institutions can evaluate model robustness without regulatory risk.
Integration & Compliance
We advise clients on how to integrate synthetic datasets into their pipelines without breaching audit or GDPR requirements.
Documentation
Establishing data lineage and reproducibility documentation.
Sandboxing
Creating environments with restricted data propagation.
Audit Tracking
Reproducible results via deterministic seeds.
Continuous Improvement Loop
Every dataset is evaluated, improved, and versioned based on client feedback and evolving regulatory standards (EBA, Basel III, GDPR).
CYCLE
