AML testing without production data
Measure, compare, and improve AML system performance with high-fidelity synthetic datasets and objective scorecards.
MISSION
AML teams lack objective feedback loops
Anti‑money laundering systems are notoriously difficult to test and optimise objectively.
Production data wasn’t built for testing, and relying on it is a liability. Historic alerts, SARs, and confirmed outcomes are hard to access, incomplete, biased, and out of date, making them unreliable for measuring and improving performance.
Without an objective feedback loop, teams struggle to:
Validate current system performance
Compare vendors and solutions fairly
Measure the impact of data or model changes
Release changes and detect drift early
Syntheticr was built to solve this problem.
PRODUCT
Why teams choose Syntheticr
Syntheticr is an AML testing platform that enables objective performance assessment without relying on production data.
Every Syntheticr assessment produces a quantified, decision-grade scorecard showing what an AML system detects and misses across scenarios. Results are consistent, comparable, and support critical decisions.
Objective Scorecards
A national-scale financial ecosystem with millions of entities, billions of transactions, embedded criminal networks and realistic money laundering behaviours. Precisely engineered for testing AML systems.
High-Fidelity Synthetic Data
Syntheticr can be used ad hoc or embedded into workflows. Teams can start with a single assessment and, as needs grow, use APIs to generate machine-readable scorecards for continuous testing and feedback loops.
APIs for Automation
GET STARTED
Performance Baseline
Run the Syntheticr datasets through your existing AML system to get a clear, objective view of current performance. You’ll receive a quantified scorecard showing what the system detects, what it misses, and where improvements will have the greatest impact.
The first step for almost every Syntheticr user.
CUSTOMERS
Trusted by leading organisations
Used by tier-1 banks, technology providers, and regulators to test, train and benchmark AML systems.
SOLUTIONS
Use cases across the AML lifecycle
System Optimisation
Optimise alert volumes and accuracy by validating performance before and after release. Create feedback loops for continuous testing, with APIs available as needed.
Data Impact Assessment
Quantify the performance impact of data scope or quality changes before investing. Test configurations and prioritise data spend based on measurable impact.
System Benchmark
Track performance over time to prove progress and detect drift early. Every Syntheticr assessment is recorded, enabling clear comparison across releases.
Vendor Assessment
Compare AML technologies safely and fairly. Vendors process identical synthetic data and are scored with precise, objective metrics, enabling confident and defensible decisions.
Model Development
Used by technology companies and financial institutions to rapidly iterate, accelerate innovation, and validate progress without production data.
How it works
Sign up
Choose a plan to start a free 30-day trial and gain access to the synthetic datasets.
Detect criminal networks
Process the synthetic datasets through an AML system or model to generate alerts.
Get your scorecard
Upload your alerts to get an objective, quantified performance scorecard.
SUBSCRIPTIONS
Choose the right plan for you
Data for one financial institution, with risk intelligence and one basic PDF scorecard per month.
Use cases:
Performance baseline
Single vendor assessment
Occasional testing as systems change
Best for:
Smaller AML teams, innovation teams, and early evaluations.
Starter
Data for two financial institutions, additional AML alerts context, and three detailed PDF scorecards per month.
Use cases:
System and model optimisation
Data quality impact assessment
Multiple vendor comparison
Best for:
AML teams and product organisations making regular changes.
Pro
Data for the entire financial ecosystem with API-based workflows and detailed PDF or API-based scorecards.
Use cases:
System and model development
System benchmarking
Technology evaluations
Best for:
Large AML teams and software/AI companies developing AML capabilities.
Enterprise
SOLUTIONS
Industries we support
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Syntheticr enables large financial institutions to objectively test, validate, and benchmark AML systems without relying on production data. Teams use Syntheticr to establish performance baselines, compare solutions, and assess changes with clear, defensible evidence.
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Syntheticr helps technology companies develop, test, and demonstrate AML capabilities without access to customer data. Teams use Syntheticr to validate detection performance, iterate quickly, and provide objective evidence to clients and partners.
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Syntheticr provides consultancies and regulators with a safe, independent way to assess and benchmark AML systems. Objective scorecards enable fair comparison, assurance, and evidence-based recommendations across institutions and technologies.
If you need to choose a system, train a model, reduce error rates, or provide objective guidance, Syntheticr can help.
Experience Syntheticr Today
Start a 30-day trial and get your first performance scorecard for free - no production data required.