Synthetizor
Test and train your machine learning controls with high-fidelity data
Key benefits
-
Boost detection accuracy
Enriched datasets lift true‑positive rates while trimming false positives and alert noise
-
Reduce losses and manual effort
Earlier, sharper detection cuts write‑offs, fines and time spent on reviews
-
Strengthen machine‑learning controls
Train on privacy‑safe data that blends historic cases with emerging threat simulations already seen in the wider market but not yet encountered in your organisation, so models recognise new typologies early
-
Share and innovate safely
Synthetic twins can be exchanged with internal teams, vendors and regulators without exposing personal information, accelerating collaboration and approval cycles
How does Synthetizor work?
Step 1: Connect your existing data sources
Securely connect your internal datasets. Synthetizor profiles the structure, removes personal identifiers and learns statistical relationships.
Step 2: Create an enriched digital twin of your data
Produce high‑fidelity synthetic copies, then overlay labelled financial‑crime scenarios from the Simulation Library or your own designs. All without risking sensitive data.
Step 3: Test, train, and improve your controls
Use the enriched datasets with your analytics sandboxes or detection tools, train and tune controls, measure uplift and share results safely across teams.
