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Reduce false positives while improving overall accuracy
DataVisor adds customer, device, behavior, and relationship context to every decision and lets you test strategy changes against historical traffic before they go live—so teams can reduce false positives, improve overall accuracy, and focus analysts on activity that warrants review.
See precision improve before you deploy.
What you can accomplish with DataVisor
Add the context that separates good from bad
Bring customer, device, behavior, history, and relationship context into every decision.
Tune with real outcomes
Refine rules, thresholds, models, and workflows using outcomes and performance data.
Send only the right activity to review
Route only appropriate activity to verification, review, or escalation.
Intelligence and testing in one place
Connected identity and risk signals, dEdge, Knowledge Graph, supervised and unsupervised machine learning, rules, decisioning, backtesting, champion–challenger or A/B testing, case outcomes, and Analytics.
A feedback loop for better precision
Capture decision and investigation outcomes.
Analyze precision, recall, false-positive trends, rule and model performance, and review workload.
Test candidate strategy changes against historical or controlled traffic.
Publish approved changes and monitor impact on detection, friction, and operations.
See precision improve before you deploy
Explore a strategy-performance view, a false-positive cohort or rule analysis, a backtest or challenger comparison, and a controlled deployment.
Improve decision accuracy across every workflow
DataVisor combines context-rich intelligence with strategy testing and feedback loops—so improving precision is part of daily operations, not a separate analytics exercise.
Improve precision where volume is highest
Connect decisions, outcomes, and analytics
Identity & Risk Signals, dEdge, Knowledge Graph, Data Orchestration, Rules & Features, supervised and unsupervised machine learning, Decisioning, Case Management, and Analytics & Reporting.
Common questions
What is a false positive in fraud prevention?
A false positive is an alert, decline, hold, or challenge applied to activity later confirmed as legitimate.
How do you reduce false positives while improving overall accuracy?
DataVisor improves precision by testing strategy changes against historical or controlled traffic before they affect live decisions.
How are strategy changes governed?
Changes move through testing, approval, and controlled publishing, with performance monitored after deployment.
Find your biggest precision opportunities
Get a false-positive assessment focused on your highest-volume alerts, declines, or review queues.