Most AML alerts are noise.
Spot the real risks.
At most banks, more than 90% of transaction-monitoring alerts turn out to be false positives. Investigators drown, backlogs grow, and real risk hides in the queue. Vyntra combines rules, machine learning models and explainable AI, so your analysts investigate risks, not noise.
Of transaction-monitoring alerts turn out to be false positives at most banks
0
%+
McKinsey, cited in Vyntra’s AML white paper
80-90%
Of alerts that do result in a SAR filing are not acted upon
McKinsey, cited in Vyntra’s AML white paper
2-5%
Of global GDP is laundered every year, by current estimates
UNODC estimate
Why alert volumes keep growing
More rules. More alerts.
Not more detection.
To catch more suspicious activity, banks add more rules. Each rule adds alerts, most of them false positives that investigators must review. As payment volumes grow and real-time rails shrink the time available to act, the backlog grows faster than teams can clear it while the alerts that matter wait in the same queue as the ones that don’t.
Rule creep
Static rule sets grow over time. Every added rule catches a little more risk and a lot more noise, and reviews happen infrequently.
How Vyntra addresses it
- Risk-based scoring analyzes internal and external data through rules and machine learning models together, instead of counting rule hits
- Detection scenarios are managed in a no-code/low-code Modeling Studio, so scenarios can be reviewed and updated as required
Backlogs outgrow teams
Running rules across millions of daily transactions produces thousands of alerts. Hiring more investigators to manage the workload does not scale.
How Vyntra addresses it
- AI models can run along side rules to significantly reduce false positives and boost true positives
- Every score comes with its drivers attached, cutting investigation time per case
Real-time payments shrink the window
Alerting on suspicious transactions after they have settled no longer works in an era of instant payments. Detection has to keep up with the real-time payments.
How Vyntra addresses it
- The same scoring engine runs in batch or real time, so scenarios built for one mode carry over to the other
- Cloud or on-premise deployment integrates directly in the transaction flow, fitting the rails you already run
The explainability barrier
Machine learning detects far better than static rules, but banks hesitate to adopt it out of concern they cannot explain the models to their regulator.
How Vyntra addresses it
- Explainable AI attaches score drivers to every alert, so investigators see why an alert fired
- Compliance can demonstrate to supervisors exactly how detection decisions are made, case by case
Platform approach
Fewer alerts.
Better alerts.
Rules and machine learning together, with explainability built in, so detection improves and the queue shrinks at the same time.
Risk-based scoring
Internal and external data are analyzed through rules and machine learning together. Genuine risk rises to the top of the queue and cases are investigated with a full audit trail.
Explainable AI
Every score comes with its drivers attached. Investigators see why an alert fired, and compliance can demonstrate exactly how decisions are made.
Modeling Studio
Detection scenarios are managed in a no-code/low-code tool. When typologies shift or regulation changes, these can be adapted when needed.
GET IN TOUCH
Give your investigators
their time back.
See how Vyntra cuts false positives without missing risk, with risk-based scoring, explainable AI
and a case manager that centralizes your investigation.
and a case manager that centralizes your investigation.
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