Objective Performance Measurement
Syntheticr scorecards provide a structured, objective view of how your AML system performs across scenarios, segments, and typologies.
WHAT YOU GET
A quantified view of detection performance
Syntheticr scorecards support real decisions
Scorecards provide:
Overall performance
Alert/ranking precision
Performance by:
Institution
Typology
Transaction type
Network type
Performance relative to risk intelligence
How performance is measured
Evaluation against known ground truth
Syntheticr evaluates your system outputs against known financial crime activity within the dataset to calculate:
True positives
False positives
False negatives
Detection coverage
These measurements are simply not possible with production data, which lacks reliable ground-truth at the scale needed to accurately test AML systems and models.
Why it matters
Designed for real AML decisions
Scorecards are used to support decisions such as:
Validating model or rule changes
Comparing vendors on a like-for-like basis
Identifying performance gaps
Tracking performance changes over time
Each scorecard provides evidence that can be used to support internal and external stakeholders.
FORMATS
Built for analysis and workflows
Syntheticr scorecards can be provided in PDF, or machine-readable formats via API, making it easy to support critical decision-making.
PDF Scorecards
Structured reports that can easily be shared for review and decision-making
Machine-Readable Scorecards
Designed for integration into development, validation, and monitoring workflows.
RESULTS
From baseline to benchmark
Understand performance trade-offs.
Compare, track, and improve. Continuously.
Syntheticr scorecards show both effectiveness and trade-offs:
High detection with low precision indicates over-alerting
High precision with low detection indicates under-detection
Typology-level results highlight specific weaknesses
Network-level results show how complex behaviours are detected
Each scorecard provides evidence of AML system and model performance.
Teams use Syntheticr scorecards to:
Establish a baseline
Compare systems and vendors
Track performance across releases
Detect performance drift
Validate improvements
This enables targeted, measurable improvement in your AML systems and models.
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.