Data Science and ML
Solution
Build and validate AML models without production data.
Model teams need realistic labelled data to develop new detection approaches and prove uplift. Syntheticr provides high-fidelity synthetic transactions with embedded typologies and ground truth, enabling rapid iteration through Model Development without relying on production data.
How it works
Generate Syntheticr aligned to your products and risks.
Train/fine-tune models using ground-truth labels.
Score outputs via Syntheticr to measure uplift by typology.
Test robustness and data sensitivity using Data Impact Assessment.
Support safe rollout tuning with System Optimisation where needed.
What you get
High-fidelity training & validation datasets
Embedded criminal networks and labels
Objective scorecards per experiment
Typology-level diagnostics
Safe AI-recommendation validation
Best for
AML Data Scientists
Machine Learning Engineers
Analytics Teams
R&D/Innovation Teams