We are happy to share that Linkurious Enterprise 2.10 is available in beta. This new version ships with the following new capabilities: Edge grouping Dynamic sizing of nodes Ability to use edges and current users as input in Query Templates This release also brings changes such as improved support for CosmosDB, compatibility with Elasticsearch 7.X, […]
It’s estimated that between $800 billion to $2 trillion is laundered annually. Neutralizing money laundering networks is critical to fighting back against drug trafficking, human exploitation, corruption, and more. Traditional Anti-Money Laundering (AML) technologies can’t keep up with the scale and flexibility required to unmask criminal networks. This article will present the benefits of graph […]
ICIJ’s latest investigation, the FinCEN files, sheds light on how financial criminals use US banks to move money throughout the world. This blog post looks behind the scenes of the investigation to explain how ICIJ used Linkurious Enterprise coupled with the Neo4j graph database and other tools to uncover stories of corruption, fraud and money laundering. NB: […]
Anti-money laundering (AML) and graph analytics is a match made in heaven. A lot of anti-money laundering use cases require identifying suspicious connections whereas graph analytics is designed to analyze complex connections from big data at scale. In this article we will provide a series of examples where graph analytics can be used to fight back […]
With the latest version of Linkurious Enterprise v2.9.5 available to clients and partners, it’s now possible to use the leading detection and investigation software for graph data with the latest version of Neo4j. Neo4j v4 brings multiple improvements, most notably support for multi-tenancy. Neo4j users can now run multiple instances from the same server. With […]
Why should Anti-Money Laundering (AML) teams pay attention to graph analytics? In this article we will dive into the problems with today’s AML compliance technology, how it impacts anti-money laundering and how graph analytics can provide a path forward for a more effective detection and investigation of AML cases.