How Link Analysis Can Help In Anti-Money Laundering Investigations

Link or network analysis can help the compliance risk industry better analyze data sets to discover alarming patterns. This form of analysis allows compliance teams to dive into massive piles of valuable information and process data much faster than holistic methods can. However, according to a report by Oracle, 43% of surveyed C-level financial industry executives in North America lacked the ability to translate the data available to them into actionable insight.

What is Link Analysis?

Link analysis is a part of graph theory - the study of graphs. Graphs are mathematical structures used to model pairwise relations between objects. A graph in this context is made up of vertices, nodes, or points which are connected by edges, arcs, or lines. A graph may be undirected, meaning that there is no distinction between the two vertices associated with each edge, or its edges may be directed from one vertex to another.

Link or network analysis focuses on the relationships between entities rather than attributes of entities. It gives a sense of interdependence at a group level rather than at the individual level.

This is helpful to those in the compliance industry as it enables analysts to process information faster thanks to it's a visual format.

Untangling the AML Web with Link Analysis

When a bank is tasked with an anti-money laundering ("AML") investigation, the task can be daunting because of the sheer amount of information that must be reviewed. Most perpetrators of money laundering do not conduct illegal business during a singular transaction. Instead, they attempt to bury their illicit behavior under a bevy of normal activity. For example, a criminal who is trying to launder their proceeds typically uses several different accounts, and their transaction flow looks vastly different from that of a normal bank account.

Visual link analysis provides an easier way to "follow the money" and identify account flow and account relationships.

At first, it can be overwhelming to look at the intermingling spiderweb-like networks of data. It's often challenging for a compliance professional to determine where to start the analysis.The good news is that there are measures and metrics that can be used to identify the relative importance of an entity within a given network.

Below are a few of them:

• Degree Centrality provides the number of links going in or out of each entity. This metric gives us a count of how many direct connections each entity has to other entities within the network. This is particularly helpful for finding the most connected accounts or entities which are likely acting as a hub, and connecting to a wider network. The layout of the network for the entity can then be quickly compared to the purpose of the account that was given during initial client onboarding to uncover anomalies.
• Betweenness gives the number of times an entity falls on the shortest path between other entities. This metric shows which entity acts as a bridge between other entities. Betweenness can be the starting point to detect any money laundering or suspicious activities.

Link or network analysis is valuable because it allows multiple cross directional account relationships to be revealed quickly and easily. The information is placed into visualization software, analysts can view large amounts of interrelated accounts which indicate a larger cluster. This cluster then has the ability to indicate illegal activity. Now that each transaction is visually represented by a link, it is much more difficult for money launderers to carry out their usual tasks.