Combat Money Laundering with Linkage Analysis and Machine Learning

Money laundering is a complicated and far-reaching problem, equalling an estimated 2-5% of the U.S. GDP each year. Bad actors use a variety of techniques and channels to conceal their identities and wrongdoings, making it harder to accurately identify and prevent money laundering. 

A comprehensive strategy for anti-money laundering (AML) prevention is required. Using AML detection tools that leverage machine learning technology and linkage analysis to understand the context of suspicious activities can help you accurately identify and stop AML in its tracks.

Why Traditional AML Tools Fall Short

While it is true that anti-money laundering technology continues to grow in sophistication, so do the efforts of bad actors to evade AML detection efforts. In many cases, investigation and compliance teams are playing catch-up to adapt their strategies reactively after AML has already occurred. A number of process inefficiencies in traditional AML detection complicate the issue, such as:

High Rates of False Positives

AML detection and prevention tools often follow a check-box approach, where activities are boxed into groups based on specific criteria. However, high false-positive rates can skew these results and don’t account for unknown, rapidly evolving attack patterns.

High Operating Costs

Many traditional solutions lack scalability, forcing investigation and compliance teams to increase headcounts in order to ramp up AML prevention efforts. This approach is both time- and cost-intensive.

Lack of Context

Anti-money laundering activities rarely take a linear approach. Rather, they are often the result of a complex web of activities, each of which may appear legitimate when viewed individually but indicate money laundering when compared to other activities. A lack of context across data points makes such comparisons impossible.

The best approach to fighting multifaceted money laundering is a multifaceted detection strategy. This is the foundation for DataVisor’s multi-layered AML and fraud prevention tools, including Knowledge Graph and unsupervised machine learning solutions.

Using Machine Learning and Linkage Analysis to Fight AML at Scale

DataVisor takes a holistic approach to detecting AML by providing visual connections across millions of real-time data points. These connections are analyzed in real time, enabling teams to move away from the traditional linear models of AML and fraud prevention. 

DataVisor solutions leverage unsupervised machine learning along with supervised machine learning to detect known and unknown attack patterns. This allows AML compliance teams to stay proactive against evolving threats before they’ve had a chance to fine-tune their current models. 

Machine learning automation works in the background in real time, eliminating the need to add more operation team members as your business grows or as attack attempts increase and evolve. 

In addition, DataVisor’s Knowledge Graph provides visual insights to help investigation teams quickly analyze items and take action. The Knowledge Graph shows the contextual relationships between potential acts of money laundering or fraud — acts that may otherwise appear normal when reviewed individually. This additional contextual information gives investigation teams what they need to make rapid decisions to prevent attack and money laundering activity before it happens.

Taking a Multifaceted Approach to Combating AML

It’s important to acknowledge that there are many ways bad actors can affect your business, and each activity may require a unique method of fraud and AML prevention. By leveraging holistic tools like DataVisor’s machine learning and Knowledge Graph, business leaders are better able to see the big picture of anti-money laundering and think long term when it comes to “futureproofing” their AML and fraud prevention strategies. 

Want to see DataVisor’s multilayered AML and fraud solution in action? Experience proactive AI-powered fraud prevention today.