# Complex Systems of Secrecy: The Offshore Networks of Oligarchs

Ho-Chun Herbert Chang1 Brooke Harrington2 Feng Fu3 Daniel Rockmore3,4

###### Abstract

Following the invasion of Ukraine, the US, UK, and EU governments–among
others–sanctioned oligarchs close to Putin. This approach has come under
scrutiny, as evidence has emerged of the oligarchs’ successful evasion of
these punishments. To address this problem, we analyze the role of an
overlooked but highly influential group: the secretive professional
intermediaries who create and administer the oligarchs’ offshore financial
empires. Drawing on the Offshore Leaks Database provided by the International
Consortium of Investigative Journalists (ICIJ), we examine the ties linking
offshore expert advisors (lawyers, accountants, and other wealth management
professionals) to ultra-high-net-worth individuals from four countries:
Russia, China, the United States, and Hong Kong. We find that resulting
nation-level “oligarch networks” share a scale-free structure characterized by
a heterogeneity of heavy-tailed degree distributions of wealth managers;
however, network topologies diverge across clients from democratic versus
autocratic regimes. While generally robust, scale-free networks are fragile
when targeted by attacks on highly-connected nodes. Our “knock-out”
experiments pinpoint this vulnerability to the small group of wealth managers
themselves, suggesting that sanctioning these professional intermediaries may
be more effective and efficient in disrupting dark finance flows than
sanctions on their wealthy clients. This vulnerability is especially
pronounced amongst Russian oligarchs, who concentrate their offshore business
in a handful of boutique wealth management firms. The distinctive patterns we
identify suggest a new approach to sanctions, focused on expert intermediaries
to disrupt the finances and alliances of their wealthy clients. More
generally, our research contributes to the larger body of work on complexity
science and the structures of secrecy.

## Introduction

Russia’s invasion of Ukraine in February 2022 was swiftly met with sanctions
imposed by nations around the world, including the US, the UK, Canada,
Australia, Switzerland, Singapore, the European Union, and its member states.
Among the many sanctions regimes put in place, those targeted at individual
oligarchs close to Putin garnered particular attention. Informed by economic
research showing that Russian elites hold a staggering 60 percent of their
wealth offshore—compared to the 10 percent global average for the ultra-
wealthy (Alstadsæter, Johannesen, and Zucman 2018)—these sanctions yielded
dramatic media coverage of yacht and luxury villa seizures.

But regulatory reviews have exposed major gaps and “shortcomings” (Wolcott,
Wolff, and Ehret ) in these sanctions regimes. Shortly after they were
imposed, evidence emerged showing some prominent Russians evading sanctions
through the assistance of elite intermediaries: trusted experts who secretly
transferred the oligarchs’ financial assets (Gregory ) and spirited away
treasures like priceless art collections beyond the reach of the law. The same
happened following efforts to sanction Russian oligarchs after the 2014
invasion of Crimea (Bertrand ). Oligarchs’ ability to escape sanctions
persists because governments have been “slow to address the enabler problem,”
in part due to the exorbitant money and time required to investigate the
secretive mechanisms used by those intermediaries.

Any efforts to sanction or curb elites’ accumulation and abuses of wealth face
the challenge of secrecy. It is an old problem: the 19th-century sociologist
Georg Simmel observed that secrecy links all groups seeking wealth in order to
escape from the rule of law, whether they be members of the nobility, bandit
gangs, or other “predatory” associations (Simmel 1906). In the contemporary
context of offshore finance, secrecy is the main product sold to elites
(Harrington 2012, 2016; Hoang 2022; Winters 2011). The result is what
anthropologist Bill Maurer calls “non-locatable structures of domination” –a
strategic obscurity surrounding who owns which assets, making it all but
impossible to tax, sanction, or otherwise hold elites accountable (Maurer
1995). This creates considerable uncertainty about whom to target with policy
measures, and how.

In this paper, we introduce a network approach to the study of oligarch
sanctions, with implications for both scholarship and policy-making. Our
analysis offers an efficient alternative strategy for achieving policy
objectives targeting the offshore wealth of oligarchs, particularly those
linked to authoritarian regimes. Using the Offshore Leaks Database provided by
the International Consortium of Investigative Journalists (ICIJ) we create and
analyze the global offshore networks linking high-net-worth individuals to the
professional intermediaries who manage their offshore wealth. We focus on
elite clients from four countries: Russia, China, Hong Kong, and the United
States. While our primary interest is in the recently-sanctioned Russian
elites, this larger group of countries provides a useful general context for
our findings. Recent economic analyses identify these four nations as the most
economically unequal in the world, with the greatest concentration of wealth
in the .001% of the population: the realm of oligarchs (Piketty and Yang
2021).

Our analysis of these “oligarch networks” provides the first quantitative test
of two key implications from prior ethnographic research (Grigoropoulou and
Small 2022) on the offshore fortunes of high-net-worth individuals. This group
includes oligarchs, defined as ultra-wealthy people who use their fortunes to
exert a disproportionate influence on politics—domestic and international. By
building on in-depth ethnography with network analysis using “big data” from
offshore leaks, our investigation answers recent calls to leverage synergies
between qualitative and quantitative research (Grigoropoulou and Small 2022).

The first implication we examine from earlier qualitative studies is that
intermediary wealth managers—rather than the elites they serve—are the
linchpins binding the global system of offshore finance, without whom the
system could not function (Harrington 2016). The second implication we study
is that, despite their many commonalities, there remain key distinctions among
oligarchs from different countries, particularly in the patterns of their
relationships with wealth managers (Harrington and Strike 2018; Harrington
2016).

Our first discovery is that oligarch networks have a “scale-free structure,”
meaning that they are degree-heterogeneous networks with heavy-tailed degree
distributions. Oligarch networks thus possess the characteristic structural
vulnerability of scale-free networks: they can be disrupted most efficiently
through targeted attacks manifested as the deletion of a few highly connected
nodes (Albert, Jeong, and Barabási 2000). This is the so-called, “robust yet
fragile” property of scale-free networks, a term coined by Albert, Barabasi,
and Jeong (Albert, Jeong, and Barabási 2000) and characteristic of many
networks, including the Internet (Doyle et al. 2005). We identify financial
intermediaries—wealth management professionals such as the lawyers,
accountants, bankers and others who specialize in serving the ultra-rich—as a
class of highly connected nodes who constitute an overlooked source of
fragility in the global and nation-level offshore financial networks. This
suggests a surprising result: the most effective and efficient way to punish
oligarchs may be to sanction their offshore intermediaries, the wealth
managers.

These networks can be robust yet “super-fragile”, which is to say that (as
shown in our series of “knockout” experiments) deleting a very small number of
intermediaries can effect significant structural damage to some nation-level
oligarch networks. Furthermore, fine-scale structural differences appear at
the national level, reflected in patterns of relational homophily. These fine-
grained differences across scale-free networks—which we link to divergent
political systems and historical path dependencies—constitute our second
discovery. Our finding extends Simmel’s work on the limits of trust among
elites linked by secrets, and aligns with qualitative research indicating high
levels of distrust among high-net-worth individuals (Sherman 2019). This
distrust can extend to the point of paranoia in some cases, particularly when
it comes to sharing information about the sources and locations of their
wealth—even when dealing with the professional wealth managers who most need
to know (Harrington 2016; Harrington and Strike 2018; Marcus and Hall 1992).
The significance of secrecy and distrust appears to increase in importance
under two conditions characteristic of autocracies: corruption and lack of
confidence in institutions such as the rule of law (Gambetta 2000). Hence,
qualitative research suggests that oligarchs from non-democratic regimes like
Russia tightly restrict their circle of advisors, doling out information only
on a “need to know” basis and exclusively to a handful of trusted
experts—usually chosen by word of mouth from family and friends, rather than
by shopping around for the most knowledgeable or -effective provider
(Harrington 2016).

The relational patterns we discover in these financial networks are
effectively networks of secrecy and are shaped in part by historical path
dependencies—notably the legacy of British colonialism, which created the
legal basis for most of the offshore world (Ogle 2017). More specifically,
since British Common Law is the “operating system” of offshore finance, access
to the system is easier for individuals whose native countries share those
legal institutions and the English language (Harrington 2016; Palan 2002).
These historical path dependencies make it easier for wealthy individuals from
the US and Hong Kong—themselves former British colonies—to obtain expert
offshore advisory services close to home. In contrast, the system is less
accessible to elites from places that were never colonized and don’t share the
language or legal institutions that are dominant offshore (Chavagneux, Murphy,
and Palan 2013; Ogle 2017). This may explain the distinctive patterns of
Chinese and Russian engagement with the offshore financial system. For Chinese
high-net-worth individuals, Hong Kong is their primary access point to expert
wealth managers providing offshore services; as a former British colony and
one of the world’s most established offshore financial centers, Hong Kong is
replete with professionals who speak Chinese, understand Chinese culture and
politics, and therefore can serve Chinese clients better than wealth managers
from outside the region. Since Russian oligarchs have no equivalent to Hong
Kong—that is, no offshore financial center on the borders of their
country—these individuals instead access the system through experts based in a
diverse range of countries, many of which are former British colonies. We
quantify these differences in relationship diversity through a novel
application of the “Herfindahl-Hirschman Index” (HHI), a well-known
diversification metric of market concentration (Rhoades 1993).

More generally, our work fits into the broader body of network science
research that has been instrumental in unveiling secretive, deceptive, and
even criminal behavior. For instance, a network approach has shed light on the
ways criminal alliances can be leveraged for more effective law enforcement
(Klerks 2004; Duijn and Klerks 2014; McIllwain 1999). Other examples include
the study of the structure of state-controlled attacks on social media during
elections (Chang et al. 2021; Chang, Haider, and Ferrara 2021; Ferrara et al.
2020), on the communication patterns within terrorist organizations (Matusitz
2008), and on price-fixing conspiracies (Baker and Faulkner 1993; Enders and
Su 2007). Research on networks of illegal activity has identified brokers–the
operational coordinators of grand larceny and other covert offenses–as the
linchpins of criminal enterprises; their removal devastates the network
overall (Bright et al. 2017; Morselli and Roy 2008; Morselli 2010). These
brokers are characterized by high betweenness centrality, which indicates how
crucial they are to facilitating shortest paths. However, only a handful of
studies (Dominguez et al. 2020; Poon, Tan, and Hamilton 2019) have applied
this analytic technique to the study of offshore wealth management. None as
yet have conducted systematic assessments of the expert networks’ robustness
in the face of disruptions such as sanctions. This is our distinctive
contribution, realized as the deletion of certain expert nodes in the offshore
financial networks of oligarchs.

Our investigation and the research cited above can be viewed as a part of a
growing body of work on what we call the “complex systems of secrecy.” The
scale-free structure we uncover in oligarchs’ offshore networks, particularly
their overlooked vulnerability to attack via removal of central wealth
managers with dense ties, exemplify a phenomenon identified in new research on
“complex secrets” (Rilinger 2019). This novel theory of secrecy builds on
Simmel’s work to posit that the accumulation of wealth and power by elites can
be most effectively achieved when key actors and data necessary to maintaining
secrets are distributed across multiple—often unlikely—locations in a network.
We extend the implications of this theory by showing that wealth managers
represent these overlooked repositories of secrets within the complex networks
of oligarchs’ offshore fortunes. This network framing, characterization of
roles in terms of network position, and distributed structure suggest that our
findings and methods can establish a basis for more effective policy
interventions, and for more fruitful research on secrecy as a complex system.

## Results

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 United States (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.

Our analysis focuses on the network of relationships connecting clients (also
known as beneficiaries), wealth managers (also known as intermediaries and
offshore entities (such as corporate service providers).111We use the terms
”beneficiary,” ”intermediary,” and ”entities” in keeping with the norms of the
financial services industry (Boyd and Prescott 1986; Hoang 2022), as reflected
in the terminology found in the ICIJ dataset. We emphasize that none of the
terms are technical social network measures; for example, ”intermediary” does
not connote centrality in an arbitrary network. That said, the oligarch
networks are bipartite (connecting intermediaries, clients, and offshore
entities) and in this case, intermediaries do in fact lie between clients who
are not connected to each other. In total, the data comprise 1,970,448 such
entities (the nodes in the network), along with 3,273,524 relationships: edges
connecting intermediaries, clients, and entities. From that superset, we
extract a subset of interest, consisting of ultra-high-net worth individuals
from countries known to produce oligarchs—Russia, China, Hong Kong and the
United States—for a total of 79,458 clients and 143,795 edges. Figure 1
visualizes the network of intermediaries and 90,155 of their clients from
Russia (RUS), China (CHN), Hong Kong (HKG), and the United States (USA) (see
data and methods). Centered around these clusters are intermediaries, marked
in yellow.

Country | Clients |  | Inter-
---
med
Edges | LGC Size | % GC
CHN | 32,045 | 1,601 | 48,239 | 22,235 | 66.1%
RUS | 6,311 | 510 | 8,512 | 4,267 | 62.6%
USA | 15,450 | 1,632 | 32,253 | 8,647 | 50.6%
HKG | 25,661 | 3,665 | 54,791 | 15,785 | 53.8%
All 4 | 79,458 | 5,711 | 143,795 | 69,916 | 60.1%
Table 1: Oligarch network statistics. The rightmost column, “% GC” counts the
proportion of the largest connected component (LGC) in each network. Note that
they are each dominated by a large “giant component” (GC).

We further partition this aggregate network into four nation-level oligarch
networks, one each for Russia, China, Hong Kong, and the United States. Each
nation-level network is constructed by first extracting all beneficiaries from
a specific country. We then identify entities they are associated with, and
the intermediaries who created the entities, who are the intermediaries
included in the network. The characteristics of the nation-level networks are
given in Table 1.222We use the terms “intermediaries” and “wealth managers”
interchangeably, and the terms “clients” and “beneficiaries” interchangeably.
Both the terms “intermediaries” and “wealth managers” signify experts whose
formal training is in the law, accountancy, banking, tax advisory services and
related fields. By the same token, the word “beneficiaries” defines “clients”
in relation to the legal structures that wealth managers create—as in the
phrase “beneficiaries of offshore trusts.” In our case-study, we further
define “oligarchs” as a subset of ultra-high-net-worth individuals who devote
some of their time and wealth to influence affairs of state, whether domestic
or international.

### Country-level Comparisons

Table 1 summarizes the characteristics of the nation-level oligarch networks.
All networks are bipartite, connecting clients with intermediaries; while
highly disconnected, they are dominated by a sizable giant (connected)
component. Both the aggregate network and the nation-level networks have
scale-free degree distributions (see Figure S2 in the SI). Clients are
disproportionately represented in the spike at the small degree, reflecting a
strategy of trusting just a handful of intermediaries, with an average degree
of 1.036 across the clients of all countries. Figure 2(a) isolates the degree
distributions of intermediaries for clients based in the US, Hong Kong, China,
and Russia respectively. Each are well-fit by a power law distribution (see
test statistics in Supplementary Information). This is consistent
with—although not required for—a growth process in which one person’s choice
of wealth manager (which is a signal of trust) inspires others to trust in the
same intermediary. This preferential attachment process creates a “Matthew
Effect” for the intermediaries.

Figure 2: A Matthew Effect of trust in accumulated advantage for offshore
wealth managers. A small fraction of these experts serve disproportionately
large numbers of clients. Shown are degree properties by clients of certain
countries. Figure 2(a) shows the degree distribution of these intermediaries.
Figure 2(b) shows the ratio of beneficiaries to wealth
managers/intermediaries.

While the slopes of their fits are close, there is a marked distinction
between the distribution patterns for Russia and China versus those for the
United States and Hong Kong (full robustness checks of the powerlaw fit can be
found in Table S3 of the SI). Indeed, when we consider the kernel density
estimates below, we find the curves for China (black) and Russia (red)
overlapping and much flatter than those of the United States (green) and Hong
Kong (blue). Together, this suggests divergent macroscale behavior between
autocratic and democratic nations. This may be because oligarchs perceive
wealth managers with fewer clients as being at lower risk of breaching
secrecy, simply because there aren’t as many people interacting with the
professionals—hence, there are fewer points of potential data leakage.
Previous research on Mafia networks suggests that this is a particular concern
among clients linked to corruption or criminality (Gambetta 2000).

However, Figure 2(b) shows how the degree distribution may not necessarily
correspond to the ratio between beneficiaries and intermediaries. China has by
far the largest number of clients per intermediary, followed by the United
States, then Russia and Hong Kong. We attribute this variation to supply and
demand for wealth management services. For example, while China now has more
billionaires than any other country, and is producing new billionaires at a
rate three times that of its nearest competitor (the United States) (King
2022), the supply of Chinese-speaking wealth managers has not caught up to
this new demand; therefore, each wealth manager serving Chinese high-net-worth
individuals carries a disproportionately large number of clients. Hong Kong’s
low number of clients per intermediary may reflect the high concentration of
wealth managers working there, creating intense competition for clients. In
any case, these figures show two things: first, beneficiaries greatly
outnumber intermediaries in all cases; second, sanctions on intermediaries
would affect many oligarchs at once.

Previous work by Albert et. al. (Albert, Jeong, and Barabási 2000) shows that
“targeted attacks” on the most highly connected nodes can destroy the
connectivity of a scale-free network. This property obtains for each of the
four nation-level oligarch networks we construct. However, we also find that
Russian and Chinese oligarch networks are characterized by “super fragility”
in the face of such attacks. This means that various connectivity markers of
the networks collapse completely when a very small number of highly-connected
nodes—ties to wealth managers—are removed. Figure 3 shows the results of our
iterative removal of the top three wealth managers (by degree) from the
offshore networks of high-net-worth individuals from each of the four
countries in our study (for the full list of managers, see S2 in the SI). We
then assessed global network metrics, normalized on the values from the
original network. Specifically, we measured the impact of node removal on a)
network size, b) the number of triangles, c) redundancy, and d) the clustering
co-efficient. Size refers to the cardinality of the network, which diminishes
when nodes are removed; however, if a beneficiary is connected to multiple
intermediaries, then removal of one intermediary will not remove the
beneficiary. The term triangles refers to the number of triplets that share
clients. This tells us how diversified clients are: for instance, if client
$a$ and client $b$ both employ wealth managers $x$ and $y$, then even with the
removal of $x$ they will remain part of a triangle ($a$–$y$–$b$). Redundancy
measures the total number of pair-wise paths between beneficiaries. Finally,
the clustering coefficient represents the fraction of completed triangles out
of all possible “triangular” interactions. This is yet another measure of
connectivity (see Methods for detailed descriptions).

The divergent patterns here are quite striking: while the offshore networks of
clients from the US and Hong Kong are relatively unperturbed by the removal of
intermediaries, the networks of clients from Russia and mainland China
collapse after removal of just one or two intermediaries. All four
measures—size, triangles, redundancy, and clustering coefficient—diminish
significantly after the top intermediary nodes are removed.

Our findings are consistent with qualitative research on the central role of
expert wealth managers in the functioning of the offshore system. In
particular, our analyses indicate that these professional intermediaries are
the key nodes in the network of global private wealth: they are the point of
contact uniting multiple oligarchs and offshore structures across national
boundaries (Harrington 2015; Hoang 2022; Winters 2011). Together, these
results suggest why previous sanctions regimes targeting individual oligarchs
or offshore jurisdictions have proved easier to bypass than policy-makers
expected: they have mistakenly targeted the spokes of a wheel rather than the
hub around which the whole system turns.

Figure 3: Wealth managers as the points of vulnerability in oligarchs’
offshore financial networks. Noticeably, the offshore networks of high-net-
worth individuals from China and Russia are much more vulnerable to such
targeted knock-outs of wealth managers than the networks of high-net-worth
individuals from the US and Hong Kong.

Figure 3 further suggests that power is not only a matter of concentration and
quantity of clients, but of (dis)trust and quality of the client relationship.
This is another point suggested by previous qualitative research that we have
been able to test and validate through analysis of big data. This aligns with
Figure 2(b), which shows Russian and Chinese elites share the secrets of their
wealth with a much smaller set of professional intermediaries than do their
peers from relatively more free and democratic societies, such as the US and
Hong Kong.

A natural follow-up question is whether degree centrality is the best
criterion for knock-out from a disruption viewpoint. Degree centrality is a
simple indication of the number of clients an intermediary has. Betweenness
centrality, reflecting the number of shortest paths in which a node lies on,
is another natural choice and might also be used to target nodes which exhibit
high brokerage leverage (in the network scientific term).333We thank one of
the reviewers for this suggestion.

Figure 4: Ratio of knock-out results, based on degree centrality or
betweenness centrality. $r>1$ indicates knocknout based on degree centrality
is more effective; $r<1$ indicates knockout based on betweenness centrality is
more effective. For both USA and HKG, degree-based removal leads to better
knockout performance. For CHN and RUS, betweenness-based removal can yield
equal or better results, if choosing one.

We test this alternative centrality measure by running the same knock-out
experiments as before but based on betweenness. For direct comparison purpose,
we show the ratio, $r$ of the resulting knock-out impacts by choosing
betweenness versus degree centrality, across the four robustness measures.
Here, $r>1$ means greater retention of the disrupted network, hence less
impact on the network, and indicates knockout based on betweenness centrality
is less effective. On the other hand, $r<1$ indicates the opposite is true.
Figure 4 shows these ratios for the four regions.

For Hong Kong and the US, knockout based on degree centrality performs
monotonically better. For China, the first choice in ranking betweenness and
degree centrality coincides, which produces a ratio of $r=1$, but the
performance of degree centrality-based knockout increases significantly
onwards. For Russia however, choosing the intermediary by betweenness
centrality to knock out may actually generate a better first response, as the
ratio dips below $1$, before increasing above $1$.

This extended result affirms that finding the most crucial nodes in knockout
experiments is not a simple matter: the optimal choice is context-dependent.
For instance, a common scheme in computer science is modularity maximization
(Good, De Montjoye, and Clauset 2010), which would ensure the maximal knock
out of client nodes. However, this does not necessarily correlate with
redundancy across clients, which is based on the Cartesian product on other
possible paths.

Thus, in the context of offshore finance, the optimal strategy is likely a
weighted combination of two-step betweenness, degree centrality, and mutual
nodes across the set of knock-out managers. This is because redundancy is
fundamentally defined on both size (how many knocked out) and connectedness
(reduction in the product space), and with each country having its own unique
configuration. The findings illustrated in Figure 3 and 4 represents the
ramifications of the scale-free structure of the oligarch networks, however;
as indicated by Figure 2(b), there are still important distinctions to be
found in the nature of the edge’s spatial relations.

Figure 5: Location of intermediaries for high net-worth clients from (a)
Russia versus (b) China. Whereas Russians employ a geographically diverse set
of intermediaries, Chinese clients employ wealth managers principally located
in Hong Kong. This is equivalent to a diversity index of 0.151 and 0.068 for
Russia and China respectively.

In Figures 5(a) and (b), we drill down into the discrepancy between the power-
law and edge quantity findings in the offshore networks of Russian versus
Chinese oligarchs. As these Figures show, Russian wealthy elites choose from a
far more geographically distributed group of advisors than do the Chinese, who
draw their wealth managers primarily from Hong Kong. This is likely due to the
aforementioned path dependencies, which—for reasons of British colonial
history—put one of the world’s most important offshore financial centers right
on China’s doorstep, complete with wealth managers who can speak Chinese and
understand the cultural, political and legal concerns of Chinese clients.
Russians lack that convenient access portal to the offshore world, and thus by
necessity must seek intermediaries elsewhere. This spatial heterogeneity
should be acknowledged both by scholars and in future sanctions regimes.
Quantitatively, we compute a diversity index (DI) based on the inverse
Herfindahl-Hirschman Index (HHI), where higher DI indicates greater diversity
(see SI). This yields 0.151 and 0.068 for Russia and China respectively,
indicating that Russian oligarchs’ ties to wealth managers are on the order of
four times as diverse as those of Chinese oligarchs.

### Russian Oligarchs Sanctioned Following the 2022 Ukraine Invasion

Figure 6: The offshore networks of the 26 sanctioned Russian oligarchs who
appear in the ICIJ’s offshore leaks database. These oligarchs are connected
through just 124 nodes, forming a rather small social circle of wealth, power
and influence. Blue denotes entities, green denotes officers, cyan denotes
intermediaries, and yellow the oligarchs.

In Figure 6, we isolate the offshore networks of the 26 Russian oligarchs
subject to sanctions on their offshore wealth following the February 2022
invasion of Ukraine; these are a subset of Russians sanctioned by the UK, US
and EU who also appear in the ICIJ’s offshore leaks database (International
Consortium of Investigative Journalists 2022). This small group includes some
of the wealthiest people in the world, and some of the most powerful in
Russia, several of whom have been subject to international sanctions numerous
times over the past decade (Tognini and Hyatt 2022). The relational pattern
shown in Figure 6a, which is the induced network on the 26 oligarchs (see
Methods), underscores the extreme concentration of these oligarchs’ offshore
networks, which can be encapsulated in just 124 nodes. Note, here we expand
our set of nodes to include three additional types. The nodes labeled
“Business entity” represent the low-level offshore service providers wealth
managers hire to do the paperwork of creating trusts and corporations. The
nodes labeled “Nominee Admin” are people who rent their names for use in
public documentation, to preserve the privacy of oligarch clients using
offshore structures. Finally, nodes labeled “Offshore Address” represent the
locations for which trusts and corporations are set up. In these figures, cyan
denotes intermediaries and yellow represents the oligarchs; in addition, blue
nodes represent entities, green denotes nominees, and grey represents offshore
addresses.

Figure 6c shows that intermediary firms such as Markom Management—a boutique
wealth management agency based in London—not only link oligarchs to numerous
offshore entities, but to other oligarchs. For example, Markom works with
three members of the Rotenberg family, who are among the wealthiest people in
Russia as well as being close personal friends of President Vladimir Putin. In
Figure 6d, we see that Boris Rotenberg is linked via an entity—a company in
the British Virgin Islands that does trust services paperwork, presumably
hired for Rotenberg by Markom—to Alisher Usmanov, the oligarch “entrusted with
servicing financial flows” for Putin’s own offshore wealth. The same pathway
links Rotenberg to Vladimir Kiriyenko, son of Putin’s current Deputy Chief of
Staff (Partridge 2022). One might conclude from this that the entities should
be sanctioned; but as we know from qualitative research, entities are
downstream in the system from wealth managers, and much more numerous
(Harrington 2016). Entities providing paperwork are easily replaced if
sanctioned or eliminated, because the skill set they call upon—compliance with
local laws, making sure signatures are in the right places and fees paid to
corporate registrars—is readily replicated. Wealth management skills, however,
are far more complex, cross-national and not easily substitutable. That’s why
oligarchs hire and work with wealth managers, not corporate service providers
or nominees.

Finally, Figure 6e shows the extreme concentration of Alisher Usmanov’s
offshore holdings under the auspices of a single Isle of Man-based wealth
management firm, Bridgewaters Limited. This illustrates what a “highly-
connected node” in a scale-free offshore network looks like in practice. Taken
together, the images in Figure 6 a–e illustrate that wealth management
intermediaries are central not only to Russian oligarchs’ offshore financial
networks, but also to their social and professional networks. The
intermediaries should therefore be a prime target for sanctions and other
efforts aimed at disrupting the activities of those oligarchs. Quantification
of these motifs, at a larger scale, is ripe for future research.

Intermediary | Sanctioned Oligarchs | # Entities | # Clients
---|---|---|---
Markom Management Ltd. | 6 | 140 | 369
American Corporate Services, Inc. | 4 | 311 | 540
Appleby Trust (Isle of Man) Limited | 4 | 458 | 3,566
I&T Consulting Ltd. | 3 | 138 | 483
G.S.L. Law & Consulting | 3 | 2,097 | 3,487
Dr. K. Chrysostomides & Co. | 2 | 69 | 207
Ryon Ltd. | 2 | 305 | 620
Consulco International Ltd. | 2 | 3,168 | 5,499
Appleby Services (Bermuda) Ltd. | 2 | 3,645 | 72,316
Dietrich, Baumgartner & Partner | 1 | 41 | 162
Bridgewater Limited | 1 | 200 | 666
Christodoulos Vassiliades | 1 | 287 | 562
Andersen Business Services, Inc. | 1 | 521 | 1,350
Lotus Holding Company Limited | 1 | 1,218 | 2,797
Table 2: Intermediary firms ordered by the total number of sanctioned Russian
oligarchs they serve. From this limited sample, there is a negative
correlation between the number of sanctioned oligarchs and each firm’s total
number of clients overall, suggesting the special vulnerability of Russian
oligarchs’ offshore networks to interventions focused on their wealth
managers.

Table 2 provides more detail on these concentrated local networks (see
Methods). Sorted by the number of sanctioned oligarchs served by each
management firm, Table 2 shows an inverse relationship between the number of
sanctioned oligarchs and the firms’ total number of clients. In other words,
the more a wealth management firm serves sanctioned Russian oligarchs, the
fewer clients that firm has in total. This supports earlier qualitative
research which suggested that oligarch-intermediary relationships were
characterized by an unusual, almost familial level of intimacy quite unusual
for professional work, but common in criminal networks such as those of Mafia
families (Harrington 2016).

Table 2 shows that the Bermuda-based multinational offshore law firm Appleby
represents the largest number of clients overall in the offshore leaks
database; however, they only represent two sanctioned Russian clients, Kerimov
and Arkady Rotenberg. This may be because large multinationals serving a wide
range of clients—in Appleby’s case including the Queen of England —are careful
about protecting their own reputations through exclusion of clients who might
be tainted by involvement in corrupt or criminal activities.

In contrast, smaller boutique firms such as Markom and American Corporate
Services Inc–which represent a vanishingly small number of clients compared to
Appleby’s–service a disproportionately large segment of the Russian oligarch
market while taking on a much smaller client base overall. From the
intermediaries’ perspective, this suggests a corporate strategy focused on
charging higher fees to legally and reputationally risky clients. From the
perspective of Russian oligarchs, this illustrates an extreme concentration or
localization of trust in the hands of a tiny number of professionals who serve
clusters of closely tied family and friendship groups. This pattern helps
explain why Russian elites’ offshore networks would be so vulnerable to
targeted attacks on their intermediaries (Fig 3).

## Discussion

Our findings suggest that the offshore financial system operates as a scale-
free network, both globally and on the level of nations, and thus shares the
same type of structural vulnerabilities as other scale-free networks, such as
the Internet. They are robust in some respects, enduring through random
deletion, but some are “super fragile” (in terms of the hallmarks of
connectivity) to targeted node removal. Most connectivity is concentrated in a
few nodes: in the case of Russian oligarchs’ offshore networks, the high-
connectivity nodes consist of a small group of wealth managers. The network
structure we have uncovered can explain why earlier rounds of sanctions
directed at those oligarchs—the sparsely connected nodes in our analysis—could
largely be evaded by some prominent individuals, despite direct seizure of
some of their assets. Our findings suggest that future sanctions should be
directed at the professional intermediaries who construct and maintain the
offshore system for the oligarchs’ benefit. We identify these expert advisors
as overlooked chokepoints in the global financial network.

To obtain these results, we used data from the offshore leaks databases
created by ICIJ from the Panama, Paradise and Pandora Papers leaks to conduct
a network attack analysis. We show that for Russian oligarchs—as well as their
peers in China—removal of just one or two intermediaries collapsed their
offshore networks entirely. The fragility of these networks is not a surprise:
offshore finance is by definition an elite phenomenon involving a few hundred
thousand ultra-high-net-worth individuals—including just under 2,700
billionaires (Hernandez 2022)—served by an even smaller group of
intermediaries (Chavagneux, Murphy, and Palan 2013; Harrington 2016; Piketty
and Yang 2021). But the distinctive “super fragility” of some offshore
networks is a novel finding. Our analysis pinpoints the greatest
vulnerabilities in those networks, and identifies important national
variations in their structures, which we expect may be influenced by
geography, history, language, and governance.

Like any study of elite phenomena, our research is limited by data that can
only provide fragmentary snapshots of a system that is observably far larger
than anyone has measured. This is a perennial problem with research on elites,
and particularly on elite wealth: information is extremely scarce because it
is intentionally shrouded in multiple levels of secrecy (Harrington 2016;
Hoang 2022). Thus, data points are few and representativeness difficult to
assess. Our analysis drew from what is now the best data available: the 6.94
terabytes of data and 46.8 million records comprising the Panama, Paradise and
Pandora Papers leaks. We were fortunate to have such information, since so
much previous research on offshore wealth has had to rely on indirect or
imputed measures (Farrell and Newman 2022). Still, there is much more to learn
and our findings should be put to the test against the offshore data likely to
emerge in future. A potential limitation of our study is that some in network
science have questioned the generalizability of scale-free networks (Holme
2019). Fortunately, our results are robust regardless of whether the offshore
network is scale-free; as long as the network structure is characterized by
extreme heterogeneity in the density of intermediary node connectivity, our
findings hold. Our goodness-of-fit appears sufficiently robust, but the
broader take-away is that two characteristics uncovered by our analysis—heavy-
tail behavior and preferential attachment—align closely with the findings of
previous qualitative research, forming a coherent, consistent picture of
oligarchs’ offshore networks.

For scholars of inequality, globalization and finance, our work offers several
new insights that may be fruitful for future research. First, our findings
substantiate the implications of previous qualitative research (Grigoropoulou
and Small 2022) suggesting that professional intermediaries are pivotal in
linking and maintaining the offshore financial system, and thus in producing
some of its most harmful outcomes, such as exploding wealth inequality and
elite corruption (Harrington 2016; Hoang 2022; Winters 2011). There should
therefore be more social scientific attention to such actors.

Second, our study could be particularly useful in building out an emerging
theory of “complex secrets,” which posits that secretive phenomena like
financial corruption have proved robust and resistant to change due to certain
patterns in evolved structures that enable the distribution and flow of
information (Rilinger 2019). Our analysis of ties between oligarchs and their
wealth managers not only represents an empirical case of such a complex system
of secrecy, reified in a network structure, but suggests that such systems may
be characterized by the specific network topology we identify. Our results
diverge from prior findings on criminal networks (Morselli 2010) showing that
the most sensitive nodes are characterized by high betweenness and low degree.
Specifically, we find that if restricted to just one intermediary, choosing by
betweenness centrality may yield greater disruption to the underlying
financial networks of oligarchs from autocratic countries. However, in
general, knock-out based on intermediaries’ degree centrality is more
effective.

Both sets of results suggest that the information structures underlying
complex systems of secrecy are diverse and context-dependent in patterned
ways; future research should explore these patterns in greater depth. Even in
the settings in which scale-free structure is observed our experiments show
that their points of fragility may require new measures for identification. As
we find the set of most sensitive nodes depends on a country’s unique network
configuration, this suggests exciting new directions of inquiry focused on
pinpointing optimal knock-out targets, contingent on characteristics of the
clients’ country of origin. We look forward to extending these analyses,
adding texture to our understanding of the adaptations and variations from
which secrecy networks emerge.

As a practical matter, the results of our attack analysis indicate that future
sanctions strategies against rogue states should consider targeting the
intermediaries serving the country’s elite, instead of or in addition to
sanctions on the individual beneficiaries themselves. We may soon have data to
test this claim. In early June, the US banned the provision of some offshore
expertise to Russian oligarchs—including accountancy and corporate formation,
which are crucial for creating and maintaining the structures that shroud
oligarchs’ assets in secrecy (Amato 2022). Shortly thereafter, the EU and the
UK implemented similar measures, banning international law and tax experts
from those jurisdictions from serving sanctioned Russian oligarchs (Commission
2022; Foregin 2022; Thomas et al. 2022). These bans are backed by penalties
including imprisonment and huge fines.444In the US, current law imposes civil
penalties on individual intermediaries up to $250,000; for criminal
convictions, the penalty is up to $1 million in fines and 20 years in prison
(see 50 USC 1705 at https://uscode.house.gov/). In the UK and EU, such bans on
the provision of expert intermediary services are characterized by the same
basic purpose and mechanisms, as well as similar penalties.

Given our analysis showing Russian elites’ reliance on intermediaries based in
the UK (and its dependent territories) and in Cyprus (an EU country), these
bans could represent a meaningful blow to the oligarchs’ ability to access
their troves of offshore wealth or move them beyond the reach of sanctioning
countries. Crucially, the ban on the provision of intermediary services is
separate from and more extensive than freezing or seizing any particular
asset; rather, the ban means cutting off the expertise pipeline linking
sanctioned individuals to their offshore wealth. It’s a more encompassing
punishment than losing access to a specific bank account or yacht or private
jet.

This approach to sanctions via bans on the provision of expert professional
services has been around for decades, and derives its legal basis from defense
ministry regulations. Such bans were typically imposed on the transfer of data
deemed important to national security and technology related to nuclear,
chemical and biological weaponry (McGowan 2007). That such rules are now being
applied to financial, legal, and accounting expertise indicates growing
recognition by policy practitioners that offshore wealth management can
threaten international security and stability. This is consistent with our
analysis illustrating the significance of these professionals as chokepoints
in global financial networks. Our work thus also motivates a closer look at
country-specific sanctions for more policy-oriented outcomes, and inspires
subsequent work to take up more nation-level policy comparisons (Siegel 2022;
Gould-Davies 2018).

Most importantly, sanctioning professional intermediaries has a solid track
record of effectiveness in achieving policy goals. For example, the current US
policy vis-a-vis intermediaries and Russian oligarchs was preceded by a
similar ban on intermediaries working with government officials in Iran–which
the US sanctions as “State Sponsor of Terrorism.” This approach has been
credited with “contributing to Iran’s decision to enter into a 2015 agreement
that put limits on its nuclear program,” in part because the country’s
leadership “could not access its foreign exchange assets held abroad”(Katzman
).

Finally, the highly tailored impact of this strategy makes it far less morally
fraught than broad-based sanctions which can deprive whole nations of
resources like grain, medical supplies, and fuel. While there is no known data
on the frequency with which bans on elite intermediary services are used as
part of sanctions packages, this strategy is certainly less well-known
publicly than resource sanctions (despite the occasional breathless media
coverage of luxury asset seizures). We hope that our study will contribute to
more widespread knowledge, discussion, and application of this more precisely
targeted tool of international policy.

## Methods

### Dataset

In its natural form, each entry in the IJIP database can be one of five node
classes:

  * •

Officers – Individual people who are related to entities. We further split
this into three classes.Beneficiaries: The direct recipients and benefits of
offshore accounts; Nominees: Individuals who are instated to manage these
accounts; (Officer) Intermediaries: Those employed to set-up these accounts.

  * •

Entities – The businesses set-up for beneficiaries. These can be corporations,
foundations, or trusts.

  * •

Intermediaries – Companies that help set-up these offshore entities.

  * •

Addresses – Addresses registered to the three other types of nodes named
above.

These four node classes are then structured through an edge list. Relational
in nature, the general structure is as followed: directed edges from officers
to entities, intermediaries to entities, and from officers, entities, and
intermediaries to addresses. In this study, we focus especially on the
tripartite structure of officers, entities, and intermediaries, and use this
tripartite network to induce a bipartite network between intermediaries and
officers (for a visualization of the tripartite structure, see Figure S1 in
the SI). We further filter on the officer class, classifying manually the top
fifty most common classes (i.e. shareholder, ultimate beneficiary, nominee
executive). The top 50 classes account for 99.2% of all entries in the
relational edge list (see Table S1 in the SI).

### Country-level comparisons

To assess the country-level behavior of beneficiaries, we first filter on
beneficiary officer nodes, then extract their connected entities. From the
edge list, we then extract the intermediaries connected to these entities.

$\displaystyle B_{c}=\\{b\in B\quad|\quad CNTRY(b)=c\\}$ (1) $\displaystyle\
E_{c}=\\{e\in E\quad|\quad e\in nei(b)\forall b\in B_{c}\\}$ $\displaystyle\
I_{c}=\\{i\in I\quad|\quad I\in nei(e)\forall e\in E_{c}\\}$

Note, it is possible to subset $B_{c}$, $E_{c}$, and $I_{c}$ directly from the
log-level data. Upon acquiring the set of intermediaries tied to beneficiaries
from country $c$, we first consider the ratio of these two sets:
$\frac{B_{c}}{I_{c}}$. We then consider the degree distribution of $I_{c}$ for
each country, which is then plotted as a power law.

### Node-removal Experiment

The purpose of this analysis is to assess the robustness of financial networks
in general. We choose the United States, China, and Russia for comparison,
adding in Hong Kong due to its role in facilitating Chinese investments and
known role as a hub for intermediaries. For each country $c$, we identify the
top 3 frequently occurring intermediaries in $I_{c}$. We then iteratively
remove $i_{1},i_{2},$, and $i_{3}$.

At each iteration, we assess the size, clustering coefficient, number of
triangles, and redundancy. Size denotes the cardinality of the set of nodes,
which serves as one view of redundancy in these networks, since if a client
only invests through one intermediary, they will be removed. The clustering
coefficient is given as:

$C=\frac{\text{Num. of closed triangles}}{\text{Num. of possible triangles}}$
(2)

As such, the number of triangles provides a more raw and less scaled measure
of such redundancy. Intuitively, if clients tend to diversify their
intermediaries, more triangles will be present. Lastly, our own measure of
redundancy counts the possible paths between any two beneficiaries for a given
country. We approximate this by taking the square sum of the cardinality of
all connected components. Formally, let CC(k) denote the set of connected
components when $k$ intermediaries are removed. Then the normalized redundancy
metric $R$ is given by:

$R=\frac{\Sigma_{a\in CC(i)}|a|(|a|-1)}{\Sigma_{b\in CC(0)}|b|(|b|-1)}$ (3)

For size, clustering coefficient, and triangles we use the built-in NetworkX
package for direct computation.

### Sanctioned Oligarch-level Network

Using fuzzy-word matching, we extracted 26 of 41 sanctioned oligarchs. We then
constructed an undirected subgraph between all the 26 oligarchs, restricted to
one degree away (up to one direct intermediary). This yielded connected
components, for which we computed the shortest paths between these connected
components via oligarchs.

Figure 6 shows how the sanctioned oligarchs are directly connected and allow
us to observe the minimal paths among these clusters of oligarchs. Given the
set of intermediaries linked to the Russian Oligarchs (which we denote as
$I_{RO}$), we further expanded the network by one degree to tabulate the
number of entities and clients each of these intermediaries serves, which
gives the power of intermediaries for their sanctioned clients as shown in
Table 2.

Although our analysis is based on the first wave of sanctions—as of March
1st—we do not expect the network structures and patterns we discovered to
change with subsequent waves of sanctions (Butler et al. 2022). We leave this
an empirical question for future research.

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