Applying Syntheticr to real-world problems

Explore how objective AML performance insights are used across roles, use cases, and industries.

DISCOVER

Use Case / Role / Industry

Use Case

Syntheticr provides realistic synthetic transaction data and scoring-as-a-service for model development, testing and regression, system and vendor benchmarking, AI-recommendation validation, and team training.

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Role

From strategy for AML executives, to model validation for data scientists, Syntheticr supports the objectives that matter to your role. Explore tailored datasets, workflows, and scorecards that help you safely and quickly prove what works.

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Industry

Whether you’re processing payments, building AML solutions, or advising clients on AML optimisation, Syntheticr adapts to your context. Explore industry-specific data, workflows, and scorecards to objectively validate performance.

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Popular Use Cases

For almost every Syntheticr user, this is the first step. Run Syntheticr through your existing AML system to establish a clear, objective view of current performance. You’ll get a ground-truth scorecard that quantifies accuracy and highlights where improvements will have the greatest impact.

Performance Baseline

Syntheticr provides realistic, risk-free data for building and improving detection models. Vendors use it as a core development asset, and financial institutions use it to accelerate in-house innovation. Embedded typologies and scoring-as-a-service, let teams rapidly iterate and prove progress without production data.

Model Development

When evaluating new AML technologies, Syntheticr lets you compare options safely and fairly. Vendors run the same labelled synthetic scenarios, and Syntheticr scores the outputs to show precise uplift, false-positive impact, and ROI. It’s a fast, controlled way to make high-stakes vendor decisions with confidence.

Vendor Assessment

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