Journal of Financial Regulation and Compliance
The determinants of anti-money laundering compliance among the Financial
Action Task Force (FATF) member states
Emmanuel Senanu Mekpor, Anthony Aboagye, Jonathan Welbeck,
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Journal of Financial Regulation and Compliance, Vol. 26 Issue: 3, pp.442-459, https://doi.org/10.1108/
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JFRC
26,3 The determinants of anti-money
laundering compliance among the
Financial Action Task Force
442 (FATF) member states
Emmanuel Senanu Mekpor and Anthony Aboagye
Department of Finance, University of Ghana Business School, Accra, Ghana, and
Jonathan Welbeck
Bank of Ghana, Accra, Ghana
Abstract
Purpose – This paper aims to compute a measure for anti-money laundering/counter-financing of terrorism
(AML/CFT) compliance and investigate its determinants.
Design/methodology/approach – Using the Financial Action Task Force (FATF) recommendations
and assigning weights to them, the study computes a measure for AML compliance. Further, the
determinants of AML compliance were investigated using ordinary least squares (OLS) data of 155 countries
between 2004 and 2016.
Findings – The findings suggest that AML compliance have slightly improved over the years. Further, the
OLS regression results show that technology, regulatory quality, bank concentration, trade openness and
financial intelligence center significantly determined and improved AML compliance.
Practical implications – From the findings, it is evident that countries that wish to improve the AML
compliance should focus more on technology, regulatory quality, structure of the banking sector, size of the
economy and institution of financial intelligence center so as to enhance AML compliance.
Originality/value – To the best of the author’s knowledge, this paper reveals a first AML/CFT compliance
index that measures the cross-country level of AML/CFT compliance from the year 2004 to 2016.
Subsequently, this paper adopted an OLS econometric model to identify the key determinants of AML/CFT
compliance amongmember states of FATF.
Keywords Anti-money laundering, Money laundering, Terrorist financing,
Financial Action Task Force
Paper type Research paper
Introduction
A new era of globalization has emerged, and it is consolidating the continents and
re-engineering local politics and international relationships. Globalization demands the
international amalgamation of information, capital and technology, leading to a sole global
market and, to a large extent, a global village (Schroeder, 2001). Although globalization has
expanded opportunities leading to free market capitalism, these same opportunities have
resulted in new risks across countries (Schroeder, 2001). Notable among these risks is the
Journal of Financial Regulation increase in the incidence of money laundering and terrorist financing. Money laundering is
and Compliance
Vol. 26 No. 3, 2018 the process of disguising the origin of ill-gotten money to make it seem as though such funds
pp. 442-459
© EmeraldPublishingLimited were obtained from legitimate sources. Simply put, money laundering is the process of
1358-1988
DOI 10.1108/JFRC-11-2017-0103 washing “dirty money” to make it look “clean”. Money laundering activities do not only
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threaten the criminal justice systems, but they have the capacity to destabilize financial Anti-money
institutions and entire financial system. laundering
In addition to the main threats mentioned above, money laundering also undermines the
integrity of the private sector (Unger and Den Hertog, 2012), undermines democracy and the compliance
rule of law (Diamond, 2016) and leads to reputational damages (Unger, 2014). Anti-money
laundering (AML) policy dates as far back as the 1980s when governments and private
actors saw the need to combat the cancer of money laundering (Verhage, 2011). The
Financial Action Task Force (FATF) is the main body tasked by the seven largest 443
economies (G-7) with the mandate of fighting money laundering and terrorist financing
across the globe. Terrorist financing therefore encapsulates any activity that provides
financial support or funding either in part or whole to terrorists or terrorist organizations to
perpetuate acts of terrorism (Zdanowicz, 2004). Other international bodies such as the
International Monetary Fund (IMF), the World Bank and United Nations Office on Drugs
and Crime (UNODC) and all the FATF-style regional bodies (FSRBs) are partners with the
FATF in this global crusade. However, in recent times, the significant increase in the
number of predicate crimes coupled with the discovery of new techniques and methods of
money laundering has been a huge source of concern to stakeholders (FATF, 2013a, 2013b,
2013c- TF inWA).
It is worrying to know that given the persistent increase in the scale of money
laundering, compliance cost increased by a rate of 53 per cent globally for just banking
institutions and show no signs of slowing down (PricewaterhouseCoopers, 2012). Also, the
biennium budgetary allocation by the UNODC alone for AML activities in 2014-2015 was
$760.1m; this includes about $88.9m (11.7 per cent) from the UN’s regular budget (UNODC,
2014).
It is very obvious that on September 11, 2001, the world changed, the horrifying events
that plagued the USA completely altered the existing approaches to combatting money
laundering/terrorist financing. Nearly every country introduced, revised or strengthened its
AML regime, with reports being churned out by various countries annually to reveal the
level of progress beingmade in the fight against the global menace.
However, with the continuous and persistent increase in funds laundered across the
globe, fueled by increase in technology, coupled with the increase in terrorist activities
across the globe, there still exists a lot of pessimism as far as AML and counter-financing of
terrorism is concerned (Demetis, 2010). The statistics provided above indicate that millions
of dollars and a great deal of effort are committed to AML activities. However, amidst all the
efforts and funds invested in AML/CFT activities, there still remains a lot of work to be done
in the area, especially where compliance is concerned (Yepes, 2011). This study investigates
whether there exist country-specific factors that enhance or inhibit compliance with the
FATF Recommendations enacted to combat global money laundering and terrorist
financing. The findings from this research pose significant policy implications at both
national and global levels: at the national level, countries are well equipped to fight money
laundering as this study makes knowledge available on the factors that drive and inhibit
AML compliance; hence, they know where to direct their efforts. At the global level,
international organizations and stakeholders are awakened to the efforts made by countries
and the factors that affect country-level compliance, as well as alterations that should be
made to the current AML/CFT framework, to ensure effectiveness of the global regulatory
framework. This paper is organized into four sections; the next section outlines the
theoretical and empirical framework on which this research is built. The section that follows
discusses the methodology adopted in this research, followed by a next section that
enumerates the conclusion and recommendations of the research.
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JFRC Literature review
26,3 Anti-money laundering policy
The inception of AML policy can be traced to the 1980s, where the council of Europe
strongly advocated that the governments of its member states ratify the measures set to
combat money laundering, especially through banks (Gill and Taylor, 2004). In 1988, the UN
and the Basel Committee also commissioned measures to combat money laundering, with
444 the former doing so through a convention held in Vienna and the latter commissioning a
Basel Committee on Banking Supervision with the purpose of protecting the banking
system from being used as a conduit for money launderers (Viritha et al., 2015).
Aside the 40 FATF Recommendations, most countries also have enacted stringent laws
to combat money laundering; a notable example is the US Patriot Act (Abel, 2002; Costanzo,
2013), which gave a detailed account of a shift in money laundering policy from a rule-based
approach to a risk-based approach. In the early days of AML policy, rule-based system was
characterized by strict regulations under which responsible agencies are to report money
laundering activities to the corresponding financial intelligence units. The major downside
with the rule-based approach is the countless tons of transactions that financial institutions
had to deal with. The rule-based approach was also circumvented by launderers through a
process known as smurfing, where criminals send cash transactions just below the
threshold, say $990, to avoid such transactions being scrutinized (if the rule is to scrutinize
$1,000 and above). This necessitated a shift from the rule-based approach to another method
of AML policy known as the risk-based approach, which puts an obligation on not only
financial institutions but also on other agencies such as real estate agencies, law firms and
notaries to document the transactions or activities they deem as high risk relating to their
unique professions. According to Costanzo (2013), a major disadvantage with the risk-based
method is the high cost associated with the transfiguration of AML policy from rule-based
to risk-based method. However, Costanzo (2013) exhibited a high degree of optimism about
the prospects of AML policy and indicated that the rewards from a more suitable
identification of money laundering risks will recompense for these high costs associated
with the risk-based method in the long term.
The Financial Action Task Force
FATF was inaugurated by the member states of the G-7 (USA, UK, France, Germany, Italy,
Canada and Japan) at a summit held in the year 1989 in Paris. The FATF was established
because of growing concerns over the risks faced by the global financial system, especially
the banking system (FATF, 2007a, 2007b). After its inception, the FATF was tasked with
analyzing money laundering trends and techniques, studying financial, legislative and law
enforcement programs sanctioned at national and international levels, assessing and
monitoring country-level compliance and giving technical assistance where necessary
(FATF, 2010). As an inter-governmental decision-making body, FATF comprises of 35
member states, two regional organizations, nine associate members and several observer
organizations (e.g. World Bank, IMF, INTERPOL and UN) affiliated to it, which are known
as FSRBs (FATF, 2010). The members of FATF and the FSRBs all sum up to over 180
jurisdictions across the globe.
The Financial Action Task Force Recommendations
The FATF combats global money laundering through recommendations or standards it
enacts (Terry, 2010). In its maiden year, FATF presented a report bearing 40
recommendations to facilitate the fight against money laundering. In the year 2003, the 40
recommendations were reviewed to cater for the changing trends and methods adopted by
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launderers mainly due to globalization (FATF, 2003). The revision was also necessitated by Anti-money
the petrifying events of 9/11 in 2001, which caused the expansion of the recommendations to laundering
include 9 extra recommendations to address terrorist financing activities, making it the 40þ
9 FATF Recommendations. The most recent review of the recommendations took place in compliance
2012; this time, the recommendations were consolidated instead of expanded, where the 40
recommendations absorbed the extra 9 recommendations, bringing it back to 40 FATF
Recommendations to date (FATF, 2012). These recommendations are well drawn as FATF
has a responsibility to protect civil liberties, respect individual state sovereignty and adhere 445
to the norm of deliberative equality in its quest to fight money laundering and terrorist
financing (Wessel, 2006).
Anti-money laundering/counter-financing of terrorism compliance index
Yepes (2011) proposed an index that was used in a country-level analysis of AML
compliance. The index covered the seven components of the FATF Recommendations. This
index was based on AML data from 2004 to 2011. The AML data available during the period
that Yepes (2011) conducted the study were also based on the old set of AML
Recommendations by FATF in 2004. The currently available revised version of the FATF
Recommendations renders Yepes’ (2011) AML compliance index impractical in measuring
compliance levels of countries from the year 2004 to 2012. This necessitated the construction
of the new AML/CFT compliance index proposed by this study that measured country-level
compliance from 2004 to 2016 based on the 2004 FATF Recommendations and the revised
Recommendations in 2012.
Anti-money laundering compliance
Many individuals and corporations assert that AML and combating the financing of
terrorism are overrated topics, with Savona and ISPAC (1997) positing that “the economic
cost of controls based on obligations and prohibitions is often underestimated, while the
benefits they produce tend to be overrated”. Geiger and Wuensch (2007) also argued that
AML regulation could lead to distortion of competition among firms, which poses danger to
firms worldwide as it could retard the progress of society toward wealth creation and also
diminish the annual productivity of firms. They added that AML costs are unnecessary
transaction costs that burden most banks, with smaller banks suffering the most as they feel
the burden twice as much as bigger banks feel it. To some others, it is not AML in its generic
sense that is unnecessary, but the current strategies adopted in AML programs that bear the
problems (Turner, 2004; Beekarry, 2011). If these arguments against the importance of
AML/CFT policies hold, why all the fuss about AML/CFT?
Among many other advantages of AML compliance, the most important of all is that
AML compliance safeguards integrity of the global financial system. Young (1979) defined
compliance as “the actual behavior of a given subject conforming to prescribed behavior”.
Young (1979) also argued that non-compliance occurs when actual behavior departs
significantly from prescribed behavior. Therefore AML compliance can be defined as all
actions by reporting institutions that conform to standards, rules, objectives, laws and
regulations set by authorities put in place to check against money laundering (Choo et al.,
2014). As the techniques deployed by money launderers evolve, so do the FATF
Recommendations evolve to be able to address modern trends and vice versa. According to
Putman (1988), the efficacy of a global AML/CFT framework mainly hinges on the efficacy
of its components and vice versa. Putman (1988) argues further that global collaboration
should be seen as a “two-level game”, where compliance of local governments to AML policy
is considered the first step to a successful global AML policy.
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JFRC Yepes (2011) asserts that although there has been progress across the various categories
26,3 of the 40 þ 9 FATF Recommendations, compliance with the AML/CFT standards is
generally low among countries. This conclusion was reached in a study that conducted a
cross-country analysis of countries’ compliance with the FATF recommendations. The 87-
country study conducted by Yepes (2011) found that institutional factors are very influential
in AML compliance. Yepes (2011) further explained that institutional factors play a key role
446 in AML compliance because they provide conditions under which policy reforms or
amendments are defined and therefore, accommodate, restrain or digress their
implementation. Extant literature provides some key factors that either enhance or impede
AML compliance. Some of these factors are discussed in the subsequent paragraphs.
Determinants of anti-money laundering/counter-financing of terrorism compliance
One of the determinants of AML/CFT compliance discussed in literature is regulatory
quality, which measures how well the leadership of a country is able to develop laws and
execute sound policies that create an enabling environment for the private sector to thrive.
Sharman (2008) posits that the quality of International AML Policy is to a large extent
dependent on the quality of domestic regulatory frameworks. Thus, no matter how good the
FATF Recommendations are, they tend to be ineffective when applied to a local jurisdiction
with a weak regulatory framework.
Secondly, technology is used as a determinant of AML/CFT compliance because with an
increase in regulatory initiatives, many more institutions have joined in the fight against
money laundering, creating new challenges for regulators and other AML agencies tasked
with ensuring compliance with AML regulations. It is interesting to know that although
launderers have taken advantage of the internet to enhance their activities, in most cases at
both national and regional levels, online investigation of money launderers’ activities is not
common among many regulatory agencies tasked with fighting money laundering (Wall
and Williams, 2013). Building the online or internet capacity of reporting agencies,
regulatory agencies and law enforcement agencies could give a major boost to country
compliance with AML laws.
Foreign direct investment (FDI) has also been linked to AML/CFT compliance in extant
literature. Naheem (2015) likened investment laundering to the layering and integration
stages of money laundering. Yepes (2011) had a contrasting view on the effect of FDI on AML
compliance. He found that FDI net inflows have a positive impact on compliance, particularly
compliance with designated non-financial businesses and persons (DNFBPs) prevention and
entity transparency components of the FATF Recommendations. More so, the level of trade
openness of countries is also considered a key determinant of compliance with AML policy,
with Ferwerda et al. (2013) confirming that trade-based money laundering (TBML) is very
prominent between the USA and countries that have low levels of compliance with AML
regulations. Literature also establishes a nexus between bank concentration and AML/CFT
compliance. Rawlings and Unger (2005) postulated that a high level of bank competition
exists for laundered funds. They added that banks compete for laundered funds through the
implementation of high-grade bank secrecy policies in a bid to attract illicit funds. Rawlings
and Unger (2005) pointed out the threats that bank concentration presents to the global AML
framework and called for the intervention of the FATF.
Given all the diverse and contrasting views on the determinants of AML/CFT
compliance, it will be interesting to uncover if there is a nexus between other socio-economic
factors such as technology, criminalization of money laundering and education on AML/
CFT compliance and the extent to which these factors affect the compliance levels of
countries.
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Methodology Anti-money
This paper uses an AML/CFT compliance index to measure the compliance levels of the laundering
member states of FATF. Borrowing from a similar AML/CFT compliance index composed
by Yepes (2011), which measures country compliance from 2004 to 2008, this paper reveals a compliance
newly composed AML/CFT compliance index that measures the cross-country level of
AML/CFT compliance from the year 2004 to 2016. Subsequently, this paper adopted an
ordinary least squares (OLS) econometric model to identify the key determinants of AML/
CFT compliance amongmember states of FATF. The OLS approach was adopted due to the 447
lack of high-frequency AML/CFT data of the countries under study. For most of the
countries included in the study, mutual evaluations had been conducted just once, which
made the OLS approach a more appropriate choice for this study. More so, no sampling
technique was needed, as all the countries used in this research are those that have
successfully participated in at least one round of mutual evaluation and have submitted a
mutual evaluation report (MER) to FATF. Consequently, the countries not represented are
those who have no record of MER with the FATF. A total number of 155 countries provided
207 MERs between the period of 2004 and 2016 for our analysis.
Construction of anti-money laundering/counter-financing of terrorism index
To measure the level of compliance of individual countries to AML/CFT Recommendations
set by FATF, there was the need for an index to be constructed to measure the compliance
level of countries from the year 2004 to 2016. The AML/CFT compliance index covers the
seven components of the 2004 FATF Recommendations (legal factors, institutional
measures, financial institution prevention measures, DNFBPs prevention, informal sector
prevention, entity transparency and international cooperation) and seven components of the
2012 FATF Recommendations (AML/CFT policies and coordination, money laundering and
confiscation, terrorist financing and financing of proliferation, preventive measures,
transparency and beneficial ownership of legal persons, powers and responsibilities of
competent authorities and international cooperation). To provide a quantitative basis for the
ratings for calculation purposes, numerical values were assigned to each rating. A value of 1
was assigned to recommendations where countries were compliant, 0.66 to largely
compliant, 0.33 to partially compliant and 0.00 to non-compliant.
The numeric values for the individual recommendation under a component are summed
up to determine the total score for that particular component. The aggregate of the seven
components constituted the AML/CFT compliance index or score for a particular country,
for a particular year.
The model
The econometric model used in this study is quite similar to the model was used by Yepes
(2011) in a similar investigation. Themodel is as follows:
AMLT=CFTCompliance Indexi ¼ b 0 þ b 1CrimML=FTi þ b 2RQi þ b 3COi
þ b 4BFATFMemberi þ b 5IDIi
þ b 6M2=GDPI þ b 7TradeOpi þ b 8Nimi
þ b 9FDI=GDPi þ DUMMYYEAR2007þ « i
A cross-sectional model is ideal in this case due to lack of consistent AML compliance data
from 2004 to 2016. The AML/CFT compliance index composed represented the dependent
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JFRC variable in the regression, while the country-specific determinants identified earlier in
26,3 Literature review constituted the explanatory variables. The model is as follows:
AML :CFTCIi ¼ b 1FDIi þ b 2TradeOpi þ b 3COi þ b 4RQi þ b 5BCi þ b 6gdppercapi
þ b 7Techi þ b 8CrimDrugMLi þ b 9FICi þ « i
448 where:
AML/CFTCI represents the Anti-money laundering (AML) and counter-financing of
terrorism (CFT) deduced from the compliance index composed to measure the various levels
of country compliance with the FATF Recommendations between 2004 and 2016.
Compliance scores range from 0 to 49; the interpretation here is that the higher the AML.
CFTCI, the higher the degree of country compliance to the FATF Recommendations.
However, because of lack of data in recent years, the regression considered index scores
between 2004 and 2012.
FDI is proxied by FDI Net inflows as a percentage of gross domestic product (GDP). The
determinant of FDI Net inflows is the aggregate of equity capital, reinvestment of returns on
investment and other short and long-term capital investments. FDI Net inflows is used
because of the keen interest in researching the existence and degree of relationship between
the entry of foreign capital and AML compliance. The relationship between FDI and
compliance is inconclusive in extant literature; hence, the expected symbol for the FDI
coefficient for this study is positive or negative.
TradeOp is used to represent trade openness and measures the importance of foreign
markets, particularly in relation to imports and exports on AML compliance. This study
used trade of good and services as a percentage of GDP to proxy trade openness. The
assumption here is that the more a country is open to other foreign economies, the more
susceptible it is to TBML and the more difficult it is for such a country to effectively comply
with AML/CFT standards (Unger et al., 2014). Therefore, the expected sign for trade
openness is negative.
Co represents corruption, which is named among the money laundering typologies, and
could also be considered a predicate offence of money laundering. Corruption could be
defined as the abuse of public position for individual or private gain (Tupman, 2005).
Tupman (2005) further asserts that corruption is mainly perpetuated by multinational
companies and politicians, the two players who are major influencers of public policy. Hence,
there exists a relationship between corruption and AML compliance, which is confirmed in
literature (Yepes, 2011). Control of corruption (estimate) is the indicator used to proxy
corruption. Control of corruption indicates the degree to which people perceive that pubic
power is exercised for individual gains in a particular country. The control of corruption
indicator captures both small and major forms of corruption. The estimate shows a country’s
aggregate score in units of a standard normal distribution ranging from approximately2.5
to 2.5. Therefore, a positive figure suggests a high corruption perception, hence a negative
relationship with AML compliance. The expected sign of the CO coefficient is negative.
RQ is used in the model to represent quality of domestic regulation. This variable is
proxied by regulatory quality (estimate). Regulatory quality captures perception of the
capacity of the lawmakers of countries to enact sound policies, laws and regulations that
create a favorable and conducive environment that enhance private sector development. RQ
Estimate shows the country’s score on the aggregate indicator in units of a standard normal
distribution, i.e. ranging from approximately 2.5 to 2.5. The expected sign for regulatory
quality is positive.
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National incomeGDP per capita was used as a proxy for national income. GDP per capita Anti-money
is the total GDP of a country divided by the population of that country. GDP per capita is laundering
included in the model to test whether the income of a country determines the level of AML/ compliance
CFT compliance in that country. GDP per capita was included as one of the explanatory
variables due to the fact that AML policy requires some capital outlay. Anti-money
laundering expenses incurred by countries include costs incurred in setting up a financial
intelligence unit to hiring of staff and purchasing tools and equipment to ensure the effective
operation of financial intelligence centers (FICs) and other agencies tasked with ensuring 449
compliance. The assumption is that wealthier countries have the capacity to provide all the
resources needed to enhance compliance. Therefore, the expected sign is positive in this
case.
Technology
Technology is a very important determinant of AML compliance, as pointed out earlier in
the paper authored by Lavorgna (2015). From the detection stage to the reporting stage, the
investigation stage through to the prosecution, technology is a necessary resource to have.
Fixed broadband subscription is used as a proxy for technology. The expectation here is
that the higher the level of technology, the better that country is equipped to comply with
AMLRecommendations.
Criminalization of money laundering
Criminalization of money laundering is one of the variables used in the model. This variable
is a dummy variable indicating whether a jurisdiction has enacted laws criminalizing all
offences of money laundering or otherwise.
FIC is used in the model to represent financial intelligence unit/center; another dummy
variable indicating whether a country has established an operative central, national agency
responsible for receiving (and, as permitted, requesting), analyzing and disseminating to the
competent authorities disclosures of financial information concerning suspected proceeds of
crime, or required by national legislation or regulation, to counter money laundering. These
reflect those jurisdictions that are members of the Egmont Group.
Bank concentration
Number of commercial bank branches per every 100,000 adults is used as a proxy for bank
concentration. This proxy is chosen because it indicates the level of concentration in the
financial sector of countries in terms of the number of customers served by commercial
banks. The variable is also an indicator of the level of competition in the financial sector.
The idea is that if the level of bank concentration is high, then it indicates the likelihood of
banks being overburdened with Know Your Customer and customer due diligence
requirements and vice versa. On the other hand, one could argue that the higher the bank
concentration, the easier it is for AML agencies to monitor their activities. Therefore, either a
negative or positive sign was expected for this variable.
Government expenditure on education
This variable is used to measure a countries’ level of education. Government expenditure on
education as a percentage of GDP is used to represent this variable. This variable is an
indicator for the educational level of countries. The variable is included in the model to
determine whether the compliance level of a country can be influenced by that country’s
budgetary allocation to education.
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JFRC Results and discussions
26,3 Four different model specifications were reported, including a principal component analysis
(PCA) model, where the author used PCA to compose the AML/CFT index. The author’s
model of interest is the first model as Models 2 and 3 are only different in the sense that they
contain the two other variables (GDP per capita and corruption), which the author found to
be highly correlated with the regulatory quality variable in the main model. In Model 1,
450 control of corruption and GDP per capita were dropped, regulatory quality and GDP per
capita were dropped for Model 2 and regulatory quality and control of corruption were
dropped in Model 3. The OLS technique was used to estimate the variables. The OLS
technique was adopted due to the lack of high frequency AML data. However, the study
based its analysis on results from Model 1, because Model 1 featured the regulatory quality
variable, which is sometimes used in extant literature to explain control of corruption (see:
Thomas, 2010 and Berg et al., 2012).
Descriptive statistics WERE used to summarize the data and to also give a better
understanding of the entire data set. The descriptive statistics presented in Table I above
confirm the absence of outliers in the data set that have the propensity to influence the
consistency, efficiency and biasedness of the results. Specifically, the descriptive statistics
featured the standard deviations, maximum and minimum values for the determinants of
AML Compliance.
The AML/CFTCI that represents the dependent variables recorded a mean of 40.24 per
cent. This means that the average percentage level of compliance among all the countries
included in the study is 40.24 per cent. Trade openness recorded a mean value of 92.18 per
cent, a minimum value of 0.18 per cent and a maximum of 439.66 per cent, signifying that on
average, the total amount of international trade (sum of export and import of goods and
services) conducted by the countries under this study as a percentage of GDP stood at 92.18
per cent. FDI (net inflows as per cent of GDP) had a mean value of 9.07 per cent, while the
minimum and maximum statistics were 43.46 and 451.72 per cent, respectively. These
figures indicate that the average percentage of FDI (net inflows) to GDP across all
observations is 9.07 per cent. The next variable is the regulatory quality (RQ) variable,
which recorded a mean value of 0.11, a minimum value of 2.41 and a maximum of 1.97.
The control of corruption variable recorded 0.06,1.63 and 2.55 for the mean, minimum and
maximum values, respectively. These values reveal that on average, the corruption
perception of the countries being studied was 0.06. Government expenditure on education
recorded a mean value of 4.56 per cent, a minimum value of 1.1 per cent and a maximum
value of 10 per cent. This is an indication that on average, the lowest percentage of GDP
Variable Observation Mean SD Minimum Maximum SWILK VIF
AML.CFTCI (%) 175 40.24 16.41 6.06 79.29 0.01 –
FDI 138 9.07 39.31 43.46 451.72 0.00 1.12
TradeOp 159 92.18 64.84 0.18 439.66 0.00 1.31
RQ 170 0.11 0.99 2.41 1.97 0.01 1.82
Baco 132 22.78 28.87 1.22 264.53 0.00 1.39
FIC 168 0.69 0.46 0 1 0.41 2.36
CrimML 168 0.93 0.24 0 1 0.00 1.18
Tech 157 12.19 3.18 4.55 18.01 0.04 2.55
GovEdu 107 4.55 1.66 1.1 10 0.21 1.10
Table I.
Descriptive statistics Source:Author’s calculations
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spent on education by the countries under study was 1.1 per cent of GDP and the highest Anti-money
allocation of GDP to education was 10 per cent. After taking the natural log (to correct for laundering
scalar bias due to relatively large figures) of per capita income levels of the countries, the
mean, minimum andmaximum values stood at 24.72, 18.46 and 30.26, respectively. compliance
The descriptive statistics for bank concentration showed that on average, for every
100,000 adults, there are 23 commercial bank branches that exist to serve their financial
needs. Also, for every 100,000 adults, the highest number of commercial bank branches for a
country was 265 and the lowest is 1. According to the descriptive statistics reported for 451
technology, the mean value is 12.19 and the maximum and minimum values stood at 18.01
and 4.55, respectively. Finally, the FIC and criminalization of money laundering variables
were included in the model as dummy variables. They record a value of 1 if yes and 0
otherwise.
Robustness
The results in Model 1 suggest that the independent variables explian 56 per cent (adjusted
R2) of the variation in AML/CFT compliance levels among the countries studied. However,
because regulatory quality, control of corruption and GDP per capita were exhibiting high
levels of correlation with the other independent variables, results were reported for three
different models where these three variables were dropped in turns. While the Shaprio
Wilk’s normality test was used to test for the normality of the data, the variance inflation
factor (VIF) was used to test for the acceptability of each variable in the model. With the
maximum acceptable VIF being 10, all the variables were below the threshold of 10,
indicating that the variables were good to be in the model. Also, the Shaprio Wilk’s test with
a null hypothesis of no normal distribution was rejected for all the variables, indicating that
the variables were all normally distributed around their means.
Along came the issue of multicollinearity; the pair-wise correlation analysis was used as
a detector of existence and degree of multicollinearity among the independent variables. An
absolute value of more than 0.7 suggests a high degree of correlation between the two
variables in question; therefore, one of the highly correlated variables must be dropped from
the model to avoid the problem of multicollinearity. From the initial model (Table II), it is
observed that there is a high-level correlation between regulatory quality and GDP per
capita (0.79), FIC and tech (0.71). Also, regulatory quality and control of corruption were
highly correlated; therefore, control of corruption was dropped from the main model. GDP
per capita was also dropped from the main model due to high correlation with most of the
variables. However, results were reported for two other models (2 and 3), which included
control of corruption and GDP per capita (Table III).
The results from Model 1 show that five of the independent variables have a significant
impact on the level of AML/CFT compliance of countries.
The first determinant of AML/CFT compliance was trade openness. Total exports and
imports as a percentage of GDPwas used as a proxy for this variable. The results show that
trade openness was significant in determining the level of AML/CFT compliance at a 1 per
cent significant level. In a similar study conducted, Yepes (2011) found trade openness to be
insignificant in explaining the AML/CFT compliance levels among countries. However, the
results from this study are confirmed by a theory of money laundering known as TBML.
Over the years, as the fight against money laundering/terrorist financing related to the
financial sector intensifies, criminals also find new means of laudering money, just like
Unger and Den Hertog (2012) put it, “Water finds its way” (FATF, 2006). TBML is one of
such tricks that is being used to launder money. The large magnitude and value of
international trade coupled with the relative ease of concealing the true origin of trade has
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JFRC
26,3 Dependent andindependent AML/
variables CFTCI FDI Edu RQ Corr CrimML FIC TradeOp Baco tech gdppercap
AML/CFTCI 1.00
FDI 0.14 1.00
452 Edu 0.02 0.10 1.00RQ 0.59 0.10 0.10 1.00
CO 0.43 0.10 0.27 0.91 1.00
CrimDrugML 0.24 0.02 0.01 0.22 0.26 1.00
FIC 0.64 0.02 0.09 0.49 0.38 0.37 1.00
TradeOp 0.24 0.28 0.06 0.15 0.12 0.09 0.01 1.00
Table II. BC 0.46 0.08 0.05 0.47 0.38 0.20 0.36 0.10 1.00Tech 0.57 0.10 0.15 0.49 0.36 0.33 0.72 0.18 0.31 1.00
Correlation matrix gdppercap 0.63 0.10 0.18 0.79 0.80 0.34 0.62 0.14 0.49 0.60 1.00
for variables in
model 1 Source:Author’s calculations
Variables Dependent variables: AML/CFT compliance index and PCA results
AML/CFT AML/CFT AML/CFT AML/CFT PCA
Independent variables index (model 1) index (model 2) index (model 3) index (model 4)
FDI 0.01 (0.02) 0.01 (0.02) 0.02 (0.02) 0.63 (0.01)
Trade openness 0.06*** (0.02) 0.06*** (0.02) 0.06*** (0.02) 0.00*** (0.00)
Corruption 0.37 (1.36)
Regulatory quality 2.72* (1.68) 0.09** (0.41)
Bank concentration 0.16*** (0.07) 0.02*** (0.07) 0.18** (0.07) 0.01*** (0.02)
GDP Per capita 1.24 (1.37)
FIC 10.57** (4.28) 11.20** (4.34) 10.41** (4.42) 0.02** (1.11)
Criminalization of money
laundering 1.10 (6.66) 1.05 (6.84) 1.69 (6.81) 0.96 (1.63)
Technology 1.60*** (0.67) 1.90*** (0.66) 1.68** (0.70) 0.02*** (0.16)
Government expenditure on
education 0.71 (0.73) 0.88 (0.78) 0.69 (0.78) 0.12 (1.18)
constant 3.98 (10.61) 1.81 (10.97) 6.93 (0.78) 0.00*** (2.58)
Observations 79 79 79 77
Table III. R2 0.61 0.59 0.60 0.63
2
Determinants of Adjusted R 0.56 0.55 0.55 0.58
2
AML/CFT Prob> x 0.00 0.00 0.00 0.00
compliance (Models Notes: *Denotes significance at 10%; **denotes significance at 5%; ***denotes significance at 1%
1, 2, 3 and 4) Source:Authors calculations
made TBML very attractive to criminals (Sullivan & Smith, 2013). Just like the earlier
typologies of money laundering, AML policies are tightened when a sector poses a threat to
a country’s AML regime. Therefore, countries with high volumes of international trade have
more stringent AML/CFT policies than countries with relatively low volumes of world
trade.
The second variable found to explain the AML/CFT compliance levels among countries
is regulatory quality. The regulatory quality variable measures the perception of the ability
of lawmakers of countries to pass sound laws, which create a favorable environment for
businesses to thrive. The variable was found to be significant in explaining compliance
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levels at a 10 per cent level of significance. A study conducted by Yepes (2011) confirms the Anti-money
findings churned out by this research that regulatory quality indeed is a determinant of laundering
AML/CFT compliance. The coefficients from both studies affirm the theory that stronger
domestic governance have a positive and statistically significant impact on AML/CFT compliance
compliance.
The third of the five variables that was found to be statistically significant in explaining
AML/CFT compliance is bank concentration. Bank concentration wasmeasured by the ratio
of commercial bank branches per 100,000 adults. The number of commercial bank branches 453
per 100,000 adults is also used as a measure of bank competition. The assertions in extant
literature on whether bank concetration expains AML/CFT compliance are diverging. While
Verdugo Yepes (2011) asserts that bank concentration is not significant in predicting AML/
CFT compliance, studies conducted by Levine (1996) and Bayraktar et al. (2006) suggest a
positive relationship between bank competition, regulation and supervision, of which AML
policy forms part. Therefore, per the findings in this study, one can glean that AML/CFT
compiance levels can be improved by increasing competition in the banking sector.
Another variable that was found to be a determinant of AML/CFT compliance is
financial intelligence center or unit (FIC/FIU). This is one of two dummy variables included
in the model. One of the requirements of AML policy is that countries should commission
FIUs to serve as collation centers for all money laundering and terrorist financing-related
information (The Egmont Group, 2004). Azevedo Araujo (2008) posits that banks are more
willing to comply with AML regulations if there exist an external body that is responsible
for screening and analyzing suspicious transactions at a faster pace and a larger volume.
Masciandaro (2005) suggest that the more exculsive and financially distinguished an AML
agency is, the more effective it will be. These two papers solidify the findings put forward
by this research that countries with FICs/FIUs tend to have a relatively high AML/CFT
compliance score than countries without FICs/FIUs.
The fifth significant variable from the results produced by the model is technology. The
natural log of the total number of broadband subscriptions is used as a proxy for
technology. The technology variable was found to be statistically significant at 1 per cent.
Technology has been a necessary tool in the fight against money laundering/terrorist
financing. Infact, right from the inception of AML, the concept was based on technology
(Demetis, 2010). Lavorgna (2015) also laid much emphasis on the necessity of technology to
the AML policy framework. The importance of technology to AML policy cannot be
overemphasized because criminal in modern-day technology-driven society are deploying
every means available at their disposal to launder the proceeds from their illicit activities
(Gao and Xu, 2009).
Failing to base AML policy on a strong technology backbone has been the mistake of
many jurisdictions across the globe. All these assertions confirm the findings in this study
that the level of technology of a particular country is a key determinant of the AML/CFT
compliance level of that country. (Table IV)
It was quite interesting to find that some of the variables the were found to be
statistically insignificant in explaining AML/CFT compliance levels were found to
be statistically significant in explaining one or more of the seven components that make up
the FATF Recommendations.
FDI, for instance, was found to be statistically significant in explaining level of
compliance with legal measures, although the same variable was found not to be significant
in explaining AML/CFT compliance levels. This finding is confirmed by a study conducted
by Naheem (2015), who coined the term “investment laundering”. Naheem posited that
investment inflows have an impact on the AML compliance, howbeit a negative impact. The
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JFRC
26,3
454
Table IV.
Determinants of
compliance with the
seven (7) components
of the FATF
recommemdations
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Dependent variables (seven components of FATF recommendation)
Financial
Independent Institutional institutions DNFBPs Informal sector Entity International
variables Legal (%) factors (%) prevention (%) prevention (%) prevention (%) transparency (%) cooperation (%)
FDI 0.02** (0.01) 0.02*** (0.01) 0.001 (0.02) 0.05*** (0.01) 0.02 (0.02) 0.05*** (0.01) 0.03** (0.01)
Trade openness 0.02 (0.02) 0.06*** (0.1) 0.08*** (0.02) 0.01 (0.02) 0.01 (0.04) 0.05*** (0.01) 0.04** (0.02)
Regulatory
quality 4.83** (2.40) 3.98* (2.04) 1.31 (2.08) 3.77 (2.51) 10.18*** (3.24) 3.57** (1.64) 0.91 (2.41)
Bank
concentration 0.12 (0.08) 0.09 (0.08) 0.16** (0.07) 0.13 (0.09) 0.21** (0.12) 0.15 (0.10) 0.28*** (0.08)
FIC 13.12** (5.49) 12.33*** (4.50) 7.84 (6.25) 3.94 (5.80) 2.7 (7.49) 9.55 (5.98) 21.88*** (5.94)
Criminalization
of money
laundering 4.05 (12.73) 1.15 (7.89) 3.75 (11.91) 6.23 (7.39) 13.9 (17.88) 2.45 (4.57) 6.62 (9.10)
Technology 1.59* (0.83) 2.06*** (0.61) 2.37*** (0.76) 0.05 (0.74) 3.01** (1.17) 0.42 (0.10) 0.04 (1.02)
Government
expenditure on
education 1.76 (1.1) 1.1 (0.83) 0.25 (0.99) 0.79 (0.87) 0.96 (1.41) 0.85 (0.76) 1.30 (1.54)
constant 3.63 (17.21) 1.98 (11.54) 2.40 (16.07) 6.73 (10.70) 15.06 (25.97) 20.87 (10.43) 20.02 (18.21)
Observations 79 79 79 79 79 79 79
R2 0.54 0.63 0.46 0.20 0.43 0.34 0.48
Notes: *Denotes significance at 10%; **denotes significance at 5%; ***denotes significance at 1%
findings in this study suggest a positive relationship between FDI and compliance with Anti-money
legal measure. The idea conveyed here is that counries with sound, strong legal systems are laundering
more attractive to foreign investors than those percieved to have weak legal systems.
However, the FIC and technology variables were found to explain both AML/CFT compliance
compliance levels and compliance with the legal measures component. FDI was also found
to explain compliance with institutional measures, DNFBPs preventive measures, entity
transparency and internation cooperation measures.
The next independent variable, trade openness, was found to explain AML/CFT 455
compliance levels and compliance with institutional measures, financial institutions
preventive measures, entity transparency and international cooperation measures.
Regulatory quality was also found to be a determinant of AML/CFT compliance and also
legal, institutional, informal sector prevention and entity transparency measures. Bank
concentration was reported as a determinant of AML/CFT compliance and three of the
components; financial institutions prevention, informal sector prevention and international
cooperation. The existence of a financial intelligence center was also found to determine the
AML/CFT compliance level of a country, as well as the country’s compliance with legal,
institutional measure and international cooperation. These findings confirm the
postulations of Azevedo Araujo (2008) and Masciandaro (2005), who argued that AML
regulations are more effective when the task of analyzing and reporting suspicious
transactions is exclusively conducted by an AML agency such as FIUs.
Interestingly criminalization of money laundering beyond drug money laundering was
not found to be significant in explaining AML/CFT compliance levels and country
compliance with any of the seven components of the FATF Recommendations. This could
be because of the perceived low level of executive commitment to the fight against money
laundering. Thus, although many countries have tonnes of laws to detect and presecute
money laundering activities, these laws are mostly rendered inneffective because of lack of
commitment on the part of the executive arm of government. This assertion was made by
Johnson (2003), citing the post-9/11 situation in the USA as an example.
Unlike criminalization of money laundering, technology was found to be key determinant
of AML/CFT compliance and compliance with legal measure, institutional measures,
financial institutions prevention measures and informal sector prevention. This findings
confirm the arguments advanced by Demetis (2010) and Lavorgna (2015) and many other
authors in extant literature. It also reechoes the suggestion that AML agencies must have a
strong technology system to be well equiped to fight tech-savymoney launderers.
The final variable is government expediture of education. This variable was included in
the models to determine whether the level of education of a particular country has any
bearing on the AML/CFT compliance level of that country. Interestingly, the results from
this study suggest that spending more money on education does not enhace or detract from
a country’s level of AML/CFT compliance and does not impact on the level of compliance
with any of the seven components of the 49 FATF Recommendations.
Conclusion
Over the past two decades, several millions of dollars have been committed to AML and the
counter-financing of terrorism activities mainly because of the dire nature of
the consequences of money laundering and terrorist financing. However, though AML/CFT
compliance costs have been on the rise and show no signs of reducing; global incidence of
money laundering and terrorist financing also keep increasing at an even higher rate. This
paper set out to measure the level of AML/CFT compliance that exists among countries and
also to unravel the key factors that promote or inhibit compliance with the AML/CFT
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JFRC recommendations commissioned by FATF. After composing an AML/CFT compliance
26,3 index to measure compliance levels across countries, a cross-sectional regression model was
used to identify the key determinants of AML/CFT compliance. A good number of
interesting findings were produced by this study; the first from the AML/CFT compliance
index indicates that AML/CFT compliance levels have improved slightly over the past
decade, though compliance levels remain low and leave so much room for improvement. The
456 results from the regression model specified in this research indicates that factors such as
countries’ level of technology and regulatory quality and trade openness have the capacity
to improve a country’s compliance with AML/CFT regulations.
The findings give all stakeholders in the fight against money laundering and terrorist
financing a lot to be concerned and optimistic about. This is because from the findings, there
is no clear indication that countries are putting in a lot of effort to improve their AML/CFT
compliance levels. It seems as though countries are doing just enough to avoid being
blacklisted by FATF giving the reputational repercussions of blacklisting.
Also, agencies such as the police, judiciary and FIU tasked with checking money
laundering activities in most countries must be technologically equipped as many of these
agencies are miles behind money launderers in terms of technology. Finally, FATF must
collaborate with other international stakeholders such as the World Bank, IMF and the UN
to build a database for money laundering and other related financial crimes. Many authors
shy away from conducting research in the area of money laundering due to the lack of data
on that topic. A database for financial crimes will go a long way to encourage many
researchers to study the topic and produce useful and relevant findings that will aid in the
successful combat of the menace.
References
Abel, A. (2002), “Anti-money laundering (AML) requirements for investment companies emerge as
compliance deadlines approach”, Journal of Investment Compliance, Vol. 3 No. 2, pp. 26-30.
Azevedo Araujo, R. (2008), “Assessing the efficiency of the anti-money laundering regulation: an
incentive-based approach”, Journal of Money Laundering Control, Vol. 11 No. 1, pp. 67-75.
Bayraktar, N. and Wang, Y. (2006), “Banking sector openness and economic growth (No. 4019)”, The
World Bank, available at: https://doi.org/10.1596/prwp
Beekarry, N. (2011), “International anti-money laundering and combating the financing of terrorism
regulatory strategy: a critical analysis of compliance determinants in international law”,
Northwestern Journal of International Law and Business, Vol. 31 No. 1, p. 137.
Berg, S.V., Jiang, L. and Lin, C. (2012), “Regulation and corporate corruption: new evidence from the
telecom sector”, Journal of Comparative Economics, Vol. 40 No. 1, pp. 22-43.
Choo, H.C., Amirrudin, M.S., Noruddin, N.A.A. and Othman, R. (2014), “Anti-money laundering and its
effectiveness”,Malaysian Accounting Review, Vol. 13 No. 2, pp. 109-124.
Costanzo, P. (2013), “The risk-based approach to anti-money laundering and counter-terrorist financing
in international and EU standards: what it is, what it entails”, Research Handbook on Money
Laundering, Edward Elgar Publishing, Cheltenham, p. 349.
Demetis, D.S. (2010), Technology and anti-Money Laundering: A Systems Theory and Risk-Based
Approach, Edward Elgar Publishing, Cheltenham.
Diamond, L. (2016), “Democracy in decline: how Washington can reverse the tide”, Foreign Aff. 95,
p. 151.
FATF (2003), “The FATF recommendations”, available at: www.fatf-gafi.org/ (accessed 27 February
2017).
Downloaded by University of Ghana At 03:14 03 July 2019 (PT)
FATF (2006), Financial Action Task Force against Money-Laundering, Annual Report 2005-2006, Anti-money
FATF, Paris. laundering
FATF (2007a), Methodology for Assessing Compliance with the FATF 40 Recommendations and the
FATF 9 Special Recommendations, Financial Action Task Force, Paris, pp. 10-11. compliance
FATF (2007b), Guidance on the Risk-Based Approach to Combating Money Laundering and Terrorist
Financing: high Level Principles and Procedures, FATF/OECD, Paris, pp. 22-28.
FATF (2010), Global Money Laundering and Terrorist Financing Threat Assessment, FATF, 457
Paris.
FATF (2012), International Standards on Combating Money Laundering and the Financing of
Terrorism& Proliferation: The FATF Recommendations, FATF/OECD, Paris, pp. 90-93.
FATF (2013a), Global Money Laundering & Terrorist Financing Threat Assessment Annual Report,
FATF, Paris.
FATF (2013b), Methodology: For Assessing Technical Compliance with the FATF Recommendations
and the Effectiveness of AML/CFT Systems, FATF, Paris.
FATF (2013c),Terrorist Financing inWest Africa, FATF, Paris.
Ferwerda, J., Kattenberg, M., Chang, H.H., Unger, B., Groot, L. and Bikker, J.A. (2013), “Gravity models
of trade-based money laundering”,Applied Economics, Vol. 45 No. 22, pp. 3170-3182.
Gao, S. and Xu, D. (2009), “Conceptual modeling and development of an intelligent agent-assisted
decision support system for anti-money laundering”, Expert Systems with Applications, Vol. 36
No. 2, pp. 1493-1504.
Geiger, H. and Wuensch, O. (2007), “The fight against money laundering: an economic analysis of a
cost-benefit paradoxon”, Journal of Money Laundering Control, Vol. 10 No. 1, pp. 91-105.
Gill, M. and Taylor, G. (2004), “Preventing money laundering or obstructing business? Financial
companies’ perspectives on ‘know your customer’ procedures”, British Journal of Criminology,
Vol. 44 No. 4, pp. 582-594.
Lavorgna, A. (2015), “Organised crime goes online: realities and challenges”, Journal of Money
Laundering Control, Vol. 18 No. 2, pp. 153-168.
Masciandaro, D. (2005), “Financial supervisory unification and financial intelligence units”, Journal of
Money Laundering Control, Vol. 8 No. 4, pp. 354-370.
Naheem, M.A. (2015), “Money laundering using investment companies”, Journal of Money Laundering
Control, Vol. 18 No. 4, pp. 438-446.
Putman, R.D. (1988), “Diplomacy and domestic politics: the logic of two-level games”, International
Organization, Vol. 42 No. 3, pp. 427-460.
Rawlings, G. and Unger, B. (2005), “Competing for criminal money”, Discussion Paper Series/Tjalling C.
Koopmans Research Institute, Vol. 5 No. 26.
Schroeder, W.R. (2001), Money Laundering: A Global Threat and the International Communitys
Response, FBI LawEnforcement Bulletin, Washington, DC, pp. 1-7.
Sharman, J.C. (2008), “Power and discourse in policy diffusion: anti-money laundering in developing
states”, International Studies Quarterly, Vol. 52 No. 3, pp. 635-656.
Savona, E.U. and ISPAC (1997), Responding to Money Laundering: International Perspectives, Harwood
Academic Publishers, Amsterdam.
Sullivan, C. and Smith, E. (2013), “Trade based money laundering: risks and regulatory responses”,
Australian Institute of Criminology. Australian Government Research and Public Policy Series
AIC Reports: 115, available at: www.aic.gov.au/documents
Terry, L.S. (2010), “An introduction to the financial action task force and its 2008 lawyer guidance”, J.
Prof. Law., Vol. 3.
The Egmont Group (2004), Information Paper of Financial Intelligence Units, Mimeo, New York, NY.
Downloaded by University of Ghana At 03:14 03 July 2019 (PT)
JFRC Thomas, M.A. (2010), “What do the worldwide governance indicators measure?”,The European Journal
26,3 of Development Research, Vol. 22 No. 1, pp. 31-54.
Tupman, W.A. (2005), “The business of terrorism”, in Goddard, S., Nikitin, A.T. and Fituni, L.I. (Eds),
Financial Monitoring of Cash Flows Aiming to Prevent the Financing of Terrorism, IIUEPS
Publishing, Moscow, pp. 112-140.
Unger, B. (2014), “Money laundering”, Encyclopedia of Criminology and Criminal Justice, Springer,
458 NewYork, NY, pp. 3137-3144.
Unger, B. and Den Hertog, J. (2012), “Water always finds its way: identifying new forms of money
laundering”, Crime, Law and Social Change, Vol. 57 No. 3, pp. 287-304.
Unger, B., Ferwerda, J., van den Broek, M. and Deleanu, I. (2014), The Economic and Legal Effectiveness
of the EuropeanUnion s anti-Money Laundering Policy, Edward Elgar Publishing, Cheltenham.
UNODC (2014), “Topics: definition of money laundering”, available at: www.undoc.org (accessed 2 June
2017).
Verhage, A. (2011), The anti Money Laundering Complex and the Compliance Industry, Taylor &
Francis, Milton Park.
Viritha, B., Mariappan, V. and Venkatachalapathy, V. (2015), “Combating money laundering by the
banks in India: compliance and challenges”, Journal of Investment Compliance, Vol. 16 No. 4,
pp. 78-95.
Wall, D.S. and Williams, M.L. (2013), Policing Cybercrime: Networked and Social Media Technologies
and the Challenges for Policing, Routledge, Abingdon.
Wessel, J. (2006), “The financial action task force: a study in balancing sovereignty with equality in
global administrative law”,Widener Law Review, Vol. 13, p. 169.
Yepes, V.C. (2011), Compliance with the AML/CFT International Standard: Lessons from a Cross-
Country Analysis, International Monetary Fund,Washington, DC.
Young (1979), Compliance and Public Authority, John Hopkins University Press, Baltimore.
Zdanowicz, J.S. (2004), “Money laundering and terrorist financing”, Communications of the Acm,
Vol. 47 No. 5, p. 53.
Further reading
Baker, R.W. (2005), Capitalism’s Achilles Heel: Dirty Money and How to Renew the Free-Market System,
JohnWiley& Sons, Hoboken, NJ.
Beck, T., Demirgüç-Kunt, A. and Levine, R. (2006), “Bank concentration, competition, and crises: first
results”, Journal of Banking & Finance, Vol. 30 No. 5, pp. 1581-1603.
Berger, A.N., Demirguc-Kunt, A., Levine, R. and Haubrich, J.G. (2004), “Bank concentration and
competition: an evolution in the making”, Journal of Money, Credit, and Banking, Vol. 36 No. 3b,
pp. 433-451.
De Meulemeester, J.L. and Rochat, D. (1995), “A causality analysis of the link between higher
education and economic development”, Economics of Education Review, Vol. 14 No. 4,
pp. 351-361.
Heineman, B.W., Jr and Heimann, F. (2006), “The long war against corruption”, Foreign Affairs, Vol. 85
No. 3, pp. 75-86.
IMF (2004), “The IMF And the fight against money laundering and the financing of terrorism”, A Fact
Sheet, pp. 307-331.
Kruss, G., McGrath, S., Petersen, I.H. and Gastrow, M. (2015), “Higher education and economic
development: the importance of building technological capabilities”, International Journal of
Educational Development, Vol. 43, pp. 22-31.
Ksenia, G. (2008), “Can corruption and economic crime be controlled in developing countries and if so, is
it cost-effective?”, Journal of Financial Crime, Vol. 15 No. 2, pp. 223-233.
Downloaded by University of Ghana At 03:14 03 July 2019 (PT)
Masciandaro, D. (1999), “Money laundering: the economics of regulation”, European Journal of Law and Anti-money
Economics, Vol. 7 No. 3, pp. 225-240. laundering
Merritt, C. (2011), “Mobile money transfer services: the next phase in the evolution of person- to-person
payments”, Journal of Payments Strategy & Systems, Vol. 5 No. 2, pp. 143-160. compliance
Osuji, E. (2015), “Foreign direct investment and economic growth in Nigeria: evidence from bounds
testing and ARDL models”, Journal of Economics and Sustainable Development, Vol. 6 No. 13,
pp. 205-211.
Picard, P.M. and Pieretti, P. (2011), “Bank secrecy, illicit money and offshore financial centers”, Journal 459
of Public Economics, Vol. 95 Nos 7/8, pp. 942-955.
PWC (2012), “Anti-money laundering: understanding global KYC differences”, available at: www.pwc.
co.uk (accessed 27 February 2017).
Rowe, M., Frantz, J. and Bozalek, V. (2012), “The role of blended learning in the clinical education of
healthcare students: a systematic review”,Medical Teacher, Vol. 34 No. 4, pp. e216-e221.
Schneider, F. (2008), “Money laundering and financial means of organised crime: some preliminary
empirical findings”,Global Business and Economics Review, Vol. 10 No. 3, pp. 309-330.
Schneider, M.F. and Enste, D. (2000), Shadow Economies around the World: Size, Causes, and
Consequences, International Monetary Fund,Washington, DC, p. 26.
Schneider, F. and Windischbauer, U. (2008), “Money laundering: some facts”, European Journal of Law
and Economics, Vol. 26 No. 3, pp. 387-404.
Sharman, J.C. and Chaikin, D. (2009), “Corruption and anti-money-laundering systems: putting a luxury
good to work”, Governance, Vol. 22 No. 1, pp. 27-45.
Sullivan, K. (2015), “What is money laundering?”, Anti–Money Laundering in a Nutshell, Apress,
New York, NY, pp. 1-13.
Unger, B. and Busuioc, E.M. (2007), The Scale and Impacts of Money Laundering, Edward Elgar
Publishing, Cheltenham.
Unger, B. and Van der Linde, D. (Eds) (2013), Research Handbook on Money Laundering, Edward Elgar
Publishing, Cheltenham.
Unger, B. and Van Waarden, F. (2009), “How to dodge drowning in data? Rule-and risk- based anti-
money laundering”, Review of Law and Economics, Vol. 5 No. 2.
Van Bergeijk, P.A. and Brakman, S. (Eds) (2010), The Gravity Model in International Trade: Advances
and Applications, Cambridge University Press, Cambridge.
Walker, J. (1995), “Estimates of the extent of money laundering in and through Australia”, Paper
Prepared for AUSTRAC, JohnWalker Consulting Services,Queanbeyan.
Walker, J. and Unger, B. (2009), “Measuring global money laundering: the walker gravity model”,
Review of Law and Economics, Vol. 5 No. 2, pp. 821-853.
Zdanowicz, J. (2009), “Trade-based money laundering and terrorist financing”, Review of Law and
Economics, Vol. 5 No. 2, pp. 854-878.
Corresponding author
Emmanuel Senanu Mekpor can be contacted at: emekpor@yahoo.com
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