DataVisor for AML Transaction Monitoring

Make false positives a thing of the past.

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A Growing Plague of False Positives

AML fines are currently over $14B per year and on the rise. All signs of potential AML activity have to be monitored, which puts a massive burden on investigation teams.

But traditional approaches to AML transaction monitoring are rigid, prone to false alarms and missing true incidents of money laundering. According to a recent PWC report, over 90-95% of alerts are false positives. Rules-based and supervised machine learning models require constant tuning as bad actors discover new ways to evade them. Every false positive means wasted investigation cycles. Every false negative is an existential risk to your business. The good news is that we can help.

“90 percent to 95 percent of all alerts generated by AML alert engines are false positives.”

Source: PriceWaterhouseCooper, From Source to Surveillance: The Hidden Risk in AML Monitoring System Optimization

Next Generation Transaction Monitoring

DataVisor provides the most advanced AML transaction monitoring solution for financial institutions using unsupervised machine learning. Combined with the scalability of our Big Data architecture, our unsupervised machine learning model analyzes billions of pieces of user attribute data and transaction data to uncovers hidden links in even the most sophisticated money laundering schemes, drastically reducing false positives and finding the most damaging hidden networks. Without requiring labels or training data, our AML solution can easily adapt to unknown money laundering techniques and changing product types.

Benefits of Unsupervised Machine Learning

reduction in false positives

Lower False Positives

Reduce costly investigations driven by false alerts, allowing you to focus on investigating high risk score accounts.

reduction in model tuning time

Automatic Rule Tuning

Spend less time managing rules by automatically generating rules derived from unsupervised detection results.

additional detection

Reduce False Negatives

Detect known and unknown threats for all product types, geographies and people by looking at all events for all accounts.

Key Use Cases

Detect Networks of Suspicious Accounts

Automatically link suspicious accounts, customers and beneficiaries that are working in conjunction with one another using DataVisor’s unsupervised machine learning.

Tune AML Rules Automatically

Reduce time spent creating and tuning new rules to comply with regulations by using DataVisor’s Automatic Rule Generation Engine, which derives highly accurate rules from unsupervised detection results and explains them in terms a judge can understand.

Simplify Case Management

Manage AML alerts efficiently by viewing groups of connected money launderers all together instead of one at a time, reducing manual review time by 8x over traditional TMS systems.

Visualize Threats Across Geos

Prevent money launderers from falling through the cracks by viewing all activity for all accounts across all branches and geographies in a global AML intelligence console.

Adapt Easily to New Data Types

Protect varied lines of business and digest new product types automatically with DataVisor’s data-agnostic unsupervised detection engine.

Key Product Features

Case Management Console

Manage cases by risk score, hidden network size, and amount laundered. Review account linkage and money flow with campaign visualization. Create SARs, pre-populated with information, and e-file directly to FinCEN.

Unsupervised Machine Learning (UML) Engine

Lowers false positives to reduces costly investigations and lower false negatives to avoid regulatory fines. Links suspicious accounts together, reducing investigation time needed.

Automated Rules Engine

Auto-maintain regulatory compliant rules (powered by UML) to eliminate the need for manual tuning and easy reporting to auditors. Allow users to manually create rules, test effectiveness, then deploy.

Account linkage view to discover hidden links between money laundering accounts.

Read Datasheet

Introducing the Automated Rules Engine

Rule-based models are popular solutions for AML transaction monitoring. They are easy to create, modify and deploy. However, they’re naive, with many false positives and false negatives. Introducing DataVisor’s patent-pending Automated Rules Engine – an AI-based rules engine that suggests rules to add, modify, and deprecate based on attributes provided by our Unsupervised Machine Learning Engine. DataVisor’s TMS, now powered by our Automated Rule Engine, allows AML teams to keep the advantages of a rules-based TMS, while simultaneously leveraging the latest machine learning technology.. Ready to redefine (minimize) your transaction monitoring efforts?

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What’s Happening in AML


Guest Post: End the False Positive Alerts Plague in Anti-Money Laundering (AML) Systems

Traditional transaction monitoring systems (TMS) 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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Getting Started

Want to get started and find out how DataVisor can help reduce the influx of false AML alerts? Request a security assessment today!

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