Re-thinking de-risking: a systems theoretical approach
| DOI | https://doi.org/10.1108/JMLC-04-2021-0030 |
| Published date | 24 July 2021 |
| Date | 24 July 2021 |
| Pages | 27-49 |
| Author | Noémi També Bearpark,Dionysios Demetis |
Re-thinking de-risking: a systems
theoretical approach
Noémi També Bearpark
Luxembourg School of Business, Luxembourg, Luxembourg, and
Dionysios Demetis
Centre for Systems Studies, University of Hull, Hull, UK
Abstract
Purpose –This paper aims to explain the de-risking phenomenonthrough Luhmann’s risk/danger model
and demonstratethat de-risking should be facilitatedand encouraged.
Design/methodology/approach –The paper applies Luhmann’s system theory and more specifically
his risk/danger model to describe the de-riskingphenomenon and identify recommendations to address its
consequences.
Findings –The paper finds that re-defining risk and theanti-money laundering (AML)’s community’s
understandingof it can support key stakeholders’understanding of money laundering(ML) risk and the way
to better addressconsequences of AML decisions.
Practical implications –The paper has implications for the banking and regulatory community in
relation to the interpretationof de-risking. As systems aim to minimise theirexposure to risk, they should not
be preventedfrom de-risking.
Originality/value –This paper aims to move away from a narrativedescription of AML phenomena and
presents a theoretical foundation for the analysis of ML risk. The current response to de-risking which
demonisesit and aims to prevent it is deconstructed through this theoretical lens.
Keywords Risk, Money laundering, Systems theory, Niklas Luhmann, Danger, Decision
Paper type Conceptual paper
Introduction
The domainof anti-money laundering (AML)has seen a number of key transitions in relation
to how the risks associated with money laundering (ML) have been handled. In the rules-
based approach, ML-risk was managed through fixed sets of constructed indicators (Ross
and Hannan, 2007).In the transition to the risk-based approach (RBA) (FATF, 2007), a more
malleable and flexible path was sought, with risk sensitivity and a clusterof additional risk-
related concepts being introduced to allow institutions to express their personalised and
customised risks. These have allowed risk prioritisations and customisations for financial
institutions and other designated non-financial businesses and professions (DNFBPs).
However, this malleability has also led to the emergence of ambiguity, withde-risking as the
pinnacle of suchperceived unintended consequences.Since then, de-riskingin AML has been
vilified and seen as a misunderstanding of the RBA, the refusal to apply it and, increasingly,
the realisationthat the RBA may essentially implya de-risking approach. Admittedly, some
well-founded objections to de-risking included thepossible push of laundered funds into
alternativeunderground remittancesystems and less-monitoredroutes (Ramachandranet al.,
2018). However, overall, is this attitude towards de-risking an accurate reflection of the
complexities of risk? With the entanglement of the RBA and de-risking, the concept of risk
has come to occupy a centralstage in our field. How well do we understand the risk, to begin
with? How can we exploreAML risk and its broader fabric of interferences?
Re-thinking
de-risking
27
Journalof Money Laundering
Control
Vol.25 No. 1, 2022
pp. 27-49
© Emerald Publishing Limited
1368-5201
DOI 10.1108/JMLC-04-2021-0030
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1368-5201.htm
This paper aims to deconstruct the natureof risk within the domain of AML and explore
how different stakeholders observe and manage ML risk, thereby impacting the risk
management initiatives of each other.Greater insight into the nature of AML risk will give
us the tools to:
understand the key drivers of de-risking;
enhance risk management approaches to handle de-risking; and
consider the systemic character of risk in the domain of ML prevention efforts.
It is in this context that this paper seeks to dissolve one of the most recent contemporary
myths in the domain of AML: the myth that de-riskingcan be and more importantly, should
be prevented. Following the review of related work in the next section, the paperdevelops a
systems theoretical treatment of risk that is based on the work of sociologist and systems
theorist, Niklas Luhmann.Based on the insights drawn from applying Luhmann’s work into
Barclays bank’s handling of High-Net-Worth Individuals and an illustrative case of its de-
risking of money services business (MSB) Dahabshiil, the paper posits that de-risking
should not be demonised and shouldbe viewed instead as an essential element within the
broader nexus of risk-basedmanagement and governance of ML risk.
Related work
Current literature on the domain of ML risk, de-risking and risk appetite, fails to develop
theoretical foundationsor frameworks through which AML risk can be reflected upon. Such
frameworks should not only be integrative of the challenges faced by risk practitioners
across various financial institutions but should also capture the perspectives of financial
intelligence units or regulatory bodies. Current scholarly or industry work relatingto AML
is either anecdotal or descriptive on risk, with key strandsrevolving around ML typologies
(Menz, 2019), regulation (Rose, 2019) or the wider financial and economic consequences of
de-risking (Ramachandran et al.,2018). Such work is useful in informing both practitioners
and academics of the latest risk-related developments and in reflecting on regulators’and
obliged entities’initiatives, as well as internal processes (Naheem, 2020). However, more
theoretical work is required so that we can approach the foundational conditions upon
which AML risk is expressed. Without challenging the foundations of risk in AML, it is
difficult to maintainan informed debate around what the future of the RBA should be.
Although there has been an increase in the ML-risk related narrativesobserved over the
past30 years(Hutter, 2005;Le Bouter, 2014;Wildavsky, 1979), with AML regulation the
following suit and becoming risk-based, ML risk and risk appetite has not been
“conceptualised and analysed in much more concrete terms than in the past (Ross and
Hannan, 2007, p. 113). Discussion in current academic and industry literature on the actual
nature of ML risk is disappointinglysparse (Artingstall et al., 2016). Ultimately,ML risk and
its key attributes need to be understood and the RBA simply does not provide the tools to
achieve this.
For instance, the published FATF guidance on the RBA (FATF, 2014) mentions risk
appetite just twice and does not provide guidance as to how an institution could or should
articulate it. Furthermore, the FATF statement concerning ML risk appetite is not specific:
“supervisors have to take steps to check that their staff is equipped to assess whether a
bank’s policies, procedures and controls are appropriate in view of the risks identified
through the risk assessment and its risk appetite”(FATF, 2014, p. 15). Similarly, financial
institutions’remarks regarding ML risk appetite are vague. Financial institutions’“risk
appetite statements often contain broad definitions of acceptable risk such as minimal
JMLC
25,1
28
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