Complex Systems of Secrecy: The Offshore Networks of Oligarchs and Money Laundering

Money laundering and tax evasion have long been a challenge for governments and law enforcement agencies worldwide. The use of offshore networks by oligarchs has only added to this challenge. The use of offshore networks is a common tactic used by wealthy individuals to avoid detection and protect their assets.

The exploits of users of offshore finance have been chronicled in many cases, including the Panama Papers and the Pandora Papers, highlighting the vast amounts of money and illegal activities moved through offshore networks.

An article on money laundering from the March 2023 issue of PNAS Nexus, a research publication from the U.S. National Academy of Sciences, caught my attention as we continue to monitor how offshore networks and their attendant activities affect Quantexa customers. The article highlights new and comprehensive research that sheds important light on the complex issue of money laundering and its underlying drivers.

By using a unique dataset and applying rigorous network analysis and investigation, the authors—math and social science professors at Dartmouth College in the U.S.—have provided valuable insights into the patterns and determinants of money laundering activities across different countries and sectors.

The image below illustrates the uniqueness of the Dartmouth professors’ research. We can see within the image the complexity of the networks that prop up money laundering. The tightly grouped areas around a yellow node depict key intermediaries within specific locations and indicate that they could be facilitators of money laundering for sanctioned entities. Using this network approach highlights what makes detecting this type of behavior so challenging to combat these crimes.

Figure 1: Offshore bipartite financial networks constructed using the ICIJ offshore leaks database. For clarity, shown here is the partial network of 79,458 intermediaries and their clients from Russia (RUS/red), China (CHN/purple), Hong Kong (HKG/green), and the USA (USA/blue). Nodes are either beneficiaries or intermediaries, visualized through physics-based verlet integration. This is a subset of a greater network of 1,970,448 nodes and 3,273,524 edges.

The complexity and reach of offshore networks

The complexity and secrecy of offshore networks shows the barriers to reducing money laundering. It’s not a crime that can be solved by taking out a few high-level operatives: The networks are far too decentralized. In the chart below, which I created using some of the researchers’ findings, we can see the location and types of most intermediaries.

Source: Quantexa analysis of PNAS Nexus paper.

The amount of money moving through these offshore networks is massive. According to a report by Global Financial Integrity in 2019, the estimated amount of illicit financial flows globally was between $1.1 and $1.8 trillion annually alone. And a recent study by the Tax Justice Network revealed that the United States, the United Kingdom, Switzerland, and the Cayman Islands are the most significant players in the offshore world, with the British Virgin Islands being the most significant offshore jurisdiction for the rich.

Fighting back against money laundering crimes is also costly. The chart below shows the increase in cost of detecting and identifying money laundering behaviors for the past five years in the United States and the United Kingdom, ordered by country and showing the percentage difference year over year for an average Tier 1 financial institution.

Source: ChatGPT. The figures for cost increase are based on an average Tier 1 financial institution and may vary depending on the size and complexity of the institution. The percentages have been calculated based on the difference in cost increase between the current year and the previous year.

How to effectively combat money laundering

Detecting the activities of oligarchs in offshore networks requires advanced analytical techniques that can identify complex relationships and patterns of behavior. Machine learning (ML) is one such tool that is being used by financial institutions to combat money laundering and other illicit financial activities. ML algorithms can analyze large amounts of data from various sources, such as transaction history and social media, to identify patterns and anomalies that may indicate money laundering or other financial crimes. 

By using ML, financial institutions can identify and investigate these behaviors more efficiently and accurately, reducing the risk of financial crime and the negative impact it has on the economy. However, identifying and tracking oligarchs involved in offshore networks is not an easy task. This is where the expertise of data analysis and investigative companies such as Quantexa comes in.

The collaboration among data analysts, academic researchers, and investigative journalists has proven to be a powerful force in exposing and combating illicit financial flows and money laundering. As we move forward, it is essential to continue to support and invest in data analysis and investigative journalism to uncover the hidden networks of wealth and power that fuel the global problem of illicit financial flows. Through continued collaboration and dedication to this critical issue, we can work towards a more transparent and just financial system for all.

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