Financial data built for AML system evaluation
Syntheticr provides high-fidelity synthetic financial data designed specifically for testing, training, and benchmarking AML systems and models.
INTRO
What the data includes
A full testing environment for AML systems
Syntheticr datasets include the full range of datasets required for testing:
Transaction data
Entity and account data
Risk intelligence data
Company registry data
Multi-institution network structures
Together, these components create a realistic environment for evaluating how AML systems behave across the different scenarios and conditions found in the real world.
How the data is designed
Built for controlled testing
Syntheticr data is designed to support objective evaluation, not just realism:
Financial behaviour and transaction flows
Embedded financial crime activity
Known ground truth held internally for evaluation
Multiple customer types, entity relationships, and network structures
Typologies reflecting real-world money laundering behaviour
Realistic-looking data is not enough. The value comes from being able to test systems against embedded activity in a way production data typically cannot support.
COVERAGE
Start with a baseline
For most teams, the right starting point is a performance baseline.
Run a Syntheticr dataset through your current AML system or model and receive a quantified scorecard showing what your system detects, what it misses, and any performance gaps.
GET STARTED
Start with a baseline
For most teams, the right starting point is a performance baseline.
Run a Syntheticr dataset through your current AML system or model and receive a quantified scorecard showing what your system detects, what it misses, and any performance gaps.