AML webinar highlights

· 2 min read

Last week we at GraphAware hosted yet another webinar. This time we talked about Money Laundering and how Hume can help you detect money laundering activities. Don’t worry if you missed it; here is your summary of what we covered.

What is Money Laundering

Simply put, Money Laundering is the process of disguising profits made from illicit activities (e.g., drug trafficking) in order to make them appear legitimate. Money laundering usually occurs in three stages: placement - the money is ingested into the financial system; layering - disguising the money trail - moving the money further from the person who originally placed it into the financial system by, e.g., splitting it into smaller sums, moving it through many different bank accounts etc.; and integration - the money seems legitimate, re-enters the economy and is invested or used to buy luxury assets.

Money laundering is connected to tax evasion and usually results in an individual enriching oneself. The Panama Papers uncovered many individuals who have done just that. However, sometimes the laundered money does not end up being used to buy a yacht or a nice house, but paying for weapons, drugs, or other illicit activities.

All of the above are reasons why there is an increasing amount of laws and regulations aiming to detect money laundering. Failure to comply with these regulations, or failure to detect money laundering activities, can result in enormous fines for banks and other financial institutions.

How Hume can help detect money laundering activities

Hume is a graph-powered insights engine that can help you detect money laundering by automating the monitoring of the developments in your data, uncovering relationships among it, and helping you compile evidence and communicate your findings to others. Here are some of the Hume features that are especially useful in detecting money laundering activities:

Data orchestration allows you to ingest and manipulate data before it is persisted to Neo4j.

Automated Alerts scan data for changes that match a suspicious pattern and alert investigators.

Actions - custom queries that speed up your investigations.

Styles help you understand patterns immediately after looking at the data.

Temporal view shows how the data changed in a specific period of time.

Exporting and sharing details and findings that are uncovered during the investigation eases your collaboration with others and compiling reports of your analyses.

Comments allow you to add your own comments about the data in the graph and share your insights with others.

Snapshots allow you to save the current view of your data for downstream processes.


In the demo, we showed how you can set up Alerts and get notified of suspicious developments in your data and how to leverage Styles to get insights from your data visually. We used Actions and Advanced Expand to analyse the data in the graph and finished the investigation by taking a Snapshot of the graph, Commenting on it, and Sharing it with a colleague so he could continue the analysis further.

Watch the recording from the webinar, including the demo:

Wonder if Hume is the right solution for you? Request a demo and speak to one of our industry experts.

Alexandra Klacanova