Prove how your technology performs
Syntheticr helps AML technology companies develop, test and demonstrate their systems using synthetic data and objective performance scorecards
Your buyers increasingly expect more than a demo.
They want to understand how your system actually performs.
PROCESS
How it works
Syntheticr provides high-fidelity synthetic transaction data generated from a simulation of legitimate activity and embedded money laundering networks. You run it through your platform as if it were production data, and Syntheticr provides an objective scorecard detailing how your system performed.
What’s in the box:
Realistic synthetic datasets covering all major transaction types and suitable for testing and demonstrating AML systems
Includes entity profiles, transactions, and risk intelligence to support end-to-end evaluation of system behaviour
Embedded financial crime typologies and network behaviour reflecting realistic detection scenarios
A defined greenfield period to enable unbiased model development and performance assessment
Objective performance scorecards showing what your system detects, misses, and flags incorrectly
Comparative reporting across time, systems, and versions to support development, validation, and client discussions
Optional extensions including low-volume labelled subsets for training, custom data variations, and multi-bank datasets
SYSTEM TYPES
Where Syntheticr is used
Syntheticr is used by teams building, improving and selling AML technologies, including:
Transaction Monitoring
Systems that generate alerts from financial transaction behaviour and need to validate detection coverage
Financial Crime Analytics
Tools focused on network analysis and financial crime investigations that need to demonstrate capability
Alert Triage and Workflow
Platforms that prioritise and manage alerts and need to improve alert handling and investigation efficiency
USE CASES
Syntheticr for Technology Companies
Performance Baseline
Establish an objective baseline for optimising your AML capabilities.
Model Development
Rapidly develop effective models without production data.
Data Impact Assessment
Objectively measure how changes to data quality/scope affect accuracy.
Technology Demonstrations
Build realistic, evidence-based demonstrations to accelerate sales.
CASE STUDY
Global Cloud Provider
Syntheticr was used to develop and validate a machine learning product for transaction monitoring before it was officially launched.
APPROACH
Why teams choose Syntheticr
Validate with confidence
Know how your systems actually perform, not just how you think they do.
Safe, realistic testing
High-fidelity synthetic transaction data simulates real behaviour without privacy risk.
Defensible evidence
Objective scorecards you can present to prospects and clients.
Demonstrating your system?
Syntheticr can be used to create repeatable, evidence-based demos grounded in realistic financial crime scenarios.
Experience how easy it can be to test and train your AML system
Start a free trial to get an objective performance insight without production data.