Fighting financial crime is a daily battle worldwide. Organizations must deploy intelligent systems to prevent and detect wrongdoings, such as anti-money laundering (AML) control frameworks. We’ll see in this blog post how graph technologies can reinforce these systems.
Using graph technologies to fight financial crime
In today’s complex economy, law enforcement and financial organizations fight against a wide range of financial crimes: embezzlement, tax evasion, extortion, corruption, terrorism funding or money laundering, to name a few. While tracking down those activities, governments and financial institutions must deal with a fast-moving financial crime landscape and a growing volume of information of various formats.
Graph technology like Linkurious Enterprise can be a powerful asset to help fight financial crime. It provides exhaustive overviews of the different entities and their connections. And it supports complex pattern queries on large data-sets in a near-real-time environment.
In this article, we’ll focus on anti-money laundering procedures and explore a specific case with a graph approach.
Strengthening AML analysis with a graph-based approach
Money laundering is the act of converting proceeds from criminal activities into legal assets, concealing their true origins. Governments have been steadily strengthening AML rules to prevent those activities. Banking institutions are now required to follow strict AML policies and to report money laundering activity suspicions. Ineffective regulation compliance might be penalized with important financial penalties.
Organizations began to develop risk-based AML analysis frameworks to monitor their customers and financial transactions. But criminals deployed sophisticated tactics to hide their wrongdoings. Shell corporations, tax havens or complex financial schemes are used to prevent identification or tracking of money flows. To thwart such criminal strategies, finding information about a specific suspicious entity is not enough. Financial crime units must investigate the connections between individuals, accounts, companies, locations, to trace complex transactions. This is why network analysis and visualization technologies turned out to be efficient tools to support AML analysis processes.
We explore below how to use Linkurious Enterprise to monitor high-risk customers through various data layers.
Financial activities visualized as networks
Banking institutions keep track of numerous information sources about their customers (individuals or companies) and their financial activities. Graph database technologies like Neo4j, JanusGraph, Stardog, Cosmos DB, or DataStax Enterprise Graph allow to index complex connected data and easily query them to find patterns. With such systems, organizations can bring together various information into a single data model.
Linkurious Enterprise provides an advanced graph visualization and analysis software compatible with numerous graph databases to easily explore and monitor the data.
Identifying money laundering patterns with Linkurious
AML regulations require banks to monitor their high-risk customers. Listed on special watch-lists, those individuals can be identified either by authorities (e.g Politically Exposed Person, Specially Designated National Lists) or by the institution itself (e.g customers with repeated suspicious transactions).
“Are my customers currently involved in activities with flagged individuals?” “If yes, are these activities suspicious?”. Organizations need to be able to answer those questions.
Linkurious Enterprise provides analysts with an interface to monitor at-risk pattern in real-time. They can set up alerts for specific patterns with a graph query language like Cypher. For instance, as an AML analysis specialist, I want to be warned each time there is any type of connection between my customer’s financial activities and my watch-list. I can use the following query to create my alert in the system:
If new data is added to my database (eg transactions, persons, companies or relationships), Linkurious Enterprise will automatically search for and flag suspicious connections. With the graph visualization interface, it’s then easy to investigate and assess the different cases.
Visual investigation of financial activities
The alert system reported several matches to our query. To evaluate the risk-level of the cases, analysts can take advantage of the visualization interface to quickly visualize and investigate the alert (we talk in another article of the benefits of graph visualization). Let’s take a look at one of the alert match:
In a glimpse, I see that Angela Marshall (a fictitious individual who belongs to my watch-list) is indirectly connected to a transaction on a customer’s bank account. She appears to share the same address as my customer, the company Miboo.
This pattern is relatively suspicious. I might want to explore beyond this single connection and see which other entities are linked to this address.
In addition to sharing an address with an individual on a watch-list, our suspicious customer also shares his address with three other companies and two of our internal employees. They are all living in the city of Hongqiao, China on 3557 Straubel Circle.
As an analyst, I might recognize a known pattern of money laundering: different companies registered with a unique address. Also, some employees are connected to a known high-risk customer. This information can be reported to higher authorities to further investigation on the field.
Graph analysis focuses on relationships, therefore helps to discover hidden connections between different entities. Linkurious Enterprise also operates an alert system in a near-real-time environment. That way, financial crime units can identify suspicious activity schemes instantly and reinforce their AML analysis and regulation system.
Leverage today the power of graph analysis and visualization to fight financial crime.