Anti Money Laundering

End the Plague of False Positives and False Negatives

AML 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 AML 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.

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Detect Various Money Laundering Techniques

Mule Accounts

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

Structuring

Money launderers avoid CTR reporting through depositing funds in small denominations

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.

Lower False Positives

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

Lower False Negatives

Adapt to new and evolving money laundering techniques

Automated Rules Engine

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

Learn how DataVisor Fights Money Laundering

WHITE PAPER

Using AI to Reduce False Alerts and Improve Compliance

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WEBINAR

Practical Approaches to Apply Machine Learning to AML

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DATA SHEET

Learn how UML can reduce false positives and increase coverage

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Key Features

Discover Stealthy Money Laundering Techniques

Uncover sophisticated money laundering activity without needing training data or labels.

Reduce False Positives

Improve accuracy of existing solutions by using an additional powerful signal, the DataVisor Score.

Generate Compliance-Friendly Results

DataVisor models are transformed into human-understandable rules, so that results are transparent to operations team and regulators.

Integrate to Existing Solutions

DataVisor’s detection results can be used to augment existing TMS outputs for improved performance.

Account linkage view to discover hidden links between money laundering accounts

What’s Happening in AML

BLOG POST

Guest Post: End the False Positives Plague in AML Systems

Traditional transaction monitoring systems suffer a plague of false positives. In this guest post from Keith Furst, founder of Data Derivatives, Keith discusses the problem with false positives and how banks should embrace unsupervised machine learning to raise the stakes in the fight against money laundering.

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BLOG POST

Guest Post: The Other Elephant in the Room: False Negatives in AML Systems

False positives have a terrible reputation among anti-money laundering circles. As mentioned in Keith’s previous article on ending the false positive alerts plague, approximately 90-95 percent of alerts generated by TMSs are false positives. So, why don’t we tighten our rule thresholds to let fewer alerts through?

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BLOG POST

Guest Post: AML Data Quality – Fitting a Square Peg into a Round Hole

Traditional rule-based transaction monitoring systems have architectural limitations which make them prone to false positives and false negatives. This post focuses on the third drawback of these solutions: how their inflexible data models lead to poor data quality, resulting in additional false positives and false negatives.

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Getting Started

Want to get started and find out how DataVisor can help find malicious accounts hiding inside your online service? Request a security assessment today!

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