Anti-Money Laundering 2018-06-13T17:18:26+00:00

Anti-Money Laundering

Anti-money laundering fines are currently over $14B per year and rising, and compliance teams are constantly trying to balance the risk of fines with the high cost of managing false positives. Traditional approaches to anti-money laundering transaction monitoring are prone to false alarms and unable to detect sophisticated money laundering techniques. A recent PWC report estimates that 90-95% of alerts are false positives. Using Unsupervised Machine Learning, DataVisor provides the industry’s most advanced AML transaction monitoring solution that can drastically reduce false positives and false negatives compared to current TMS solutions.

Detect Money Laundering Techniques

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Mule Accounts

Money launderers utilize synthetic or stolen identities to create fake accounts to move funds

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Structuring

Money launderers avoid CTR reporting through depositing funds in small denominations

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Layering

Money launderers transfer funds through multiple mule accounts to mask money flows

Dramatically Reduce False Positives and False Negatives

 

Traditional transaction monitoring solutions rely on rules or supervised machine learning models that require constant tuning as bad actors discover new ways to evade detection. DataVisor’s unsupervised machine learning engine analyzes hundreds of millions of accounts and events to identify hidden patterns between accounts. This approach dramatically reduces false positives while simultaneously increasing detection coverage. In addition, by creating human-understandable rules, DataVisor’s solutions allows clients to meet strict compliance requirements.

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Lower False Positives

Reduce costly investigations driven by false alerts and focus on alerts that lead to substantive SAR filings

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Lower False Negatives

Adapt to new and evolving money laundering techniques
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Automated Rules Engine

Bridge the gap between machine learning techniques and compliance requirements of having human-understandable reasons

Learn how DataVisor Improves Anti-Money Laundering Detection

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Embracing AI & Machine Learning for AML

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eBook: Taming the AML Beast

This ebook discusses how financial institutions can address some of the shortcomings of existing AML alerting tools to detect the ever more sophisticated money laundering networks.

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The DataVisor Platform

Unsupervised Machine Learning Engine

Predict new, unknown threats without labels or training data by analyzing hundreds of millions of accounts and events simultaneously using the industry’s most advanced unsupervised learning technology.

Supervised Machine Learning Engine

Use industry leading supervised machine learning algorithms to augment the unsupervised machine learning detection with client-provided labels.

Automated Rules Engine

Generate and deprecate rules automatically, lowering maintenance costs and improving results explainability.

Global Intelligence Network

Aggregate and analyze the industry’s broadest array of digital fingerprints and signals from billions of users across a variety of industries.

What’s Happening in AML

Threat Blogs

Uncovering Hidden Patterns to Improve AML Detection

Evolving money laundering patterns are leading to huge fines and mounting pressure on FIs to become more vigilant. Learn how unsupervised machine learning and its inherent merits can help FIs to uncover hidden money laundering patterns and improve AML detection.

Read More »
AI & Machine Learning Applied to AML
Threat Blogs

Embracing AI & Machine Learning for AML

Today’s AML & Compliance leaders face dual challenges of increasingly sophisticated digital financial crimes and the threat of growing fines from regulators. Learn how AI and Machine Learning can help FIs detect more crime and better triage alerts.

Read More »

Ready to enhance your detection with unsupervised machine learning?