Survival Analysis Methods for Assessing the Anti-Money Laundering
System Effectiveness
OLHA KUZMENKO1, OLENA KRUKHMAL2, VITALIIA KOIBICHUK1,
KOSTIANTYN HRYTSENKO1, OLEKSANDR KUSHNERYOV1, VITA HORDIIENKO3,
GALYNA PASEMKO4, OKSANA TARAN4, OLENA SMIHUNOVA4
1Department of Economic Cybernetics, Sumy State University,
2, Rymskogo-Korsakova st., Sumy,
UKRAINE
2Department of Financial Technologies and Entrepreneurship, Sumy State University,
2, Rymskogo-Korsakova st., Sumy,
UKRAINE
3Department of Management, Sumy State University,
2, Rymskogo-Korsakova st., Sumy,
UKRAINE
4Department of Management, Business and Administration, State Biotechnological University,
44, Alchevskikh str., Kharkiv,
UKRAINE
Abstract: - The article collects and systematizes statistical information to assess the anti-money laundering
system effectiveness for 25 banks from 12 countries. The anti-money laundering system effectiveness was
evaluated based on applying the survival analysis method by constructing tables of survival for banks subject to
sanctions, determining the probability of deciding on the need to impose sanctions on banks, multiple
assessments of Kaplan-Meyer, formalization of the Hazard rate instantaneous risk function. The anti-money
laundering system effectiveness is compared based on the survival analysis in groups of banks around the
world. Relevant factors influenced the assessment of the anti-money laundering system effectiveness based on
the application of the principal components method by creating a scree plot and determining the factor loads of
the statistical input base indicators in the study. A Cox proportional intensity regression model of dependence
of the anti-money laundering system effectiveness on independent factors is constructed.
Key-Words: - Anti-money laundering, anti-terrorism financing, survival analysis methods, anti-money
laundering system, effectiveness, anti-money laundering system effectiveness assessment
Received: December 9, 2022. Revised: May 7, 2023. Accepted: May 22, 2023. Published: June 2, 2023.
1 Introduction
Money laundering is an attempt to conceal the
proceeds of illegal activities by disguising them as
legal earnings. The money laundering process
involves the movement of illicit proceeds through
official bank accounts, through the banking systems
of several countries, to reach an unknown final
beneficiary or mix them with legal money. Growing
concerns about money laundering, terrorist
financing, and the proliferation of mass destruction
weapons contributed to the creation of the Financial
Action Task Force (FATF) in 1989. It must analyse
and monitor money laundering activities and
evaluate the anti-money laundering measures in
member countries. The FATF’s recommendations
are being updated and improved in light of new
research and new challenges since international law
considers money laundering a separate crime.
Today, much attention is paid to the shortcomings
of the financial system in the fight against money
laundering, as the sector continues to grow and
constantly faces new schemes and financial
scandals. An important factor in further improving
measures to combat money laundering is to assess
the existing system's effectiveness for preventing
such crimes.
2 Problem Formulation
Given the literature, one should note that the general
theoretical and practical issues of combating money
laundering are revealed in the works of a wide range
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2023.20.106
Olha Kuzmenko, Olena Krukhmal,
Vitaliia Koibichuk, Kostiantyn Hrytsenko,
Oleksandr Kushneryov, Vita Hordiienko,
Galyna Pasemko, Oksana Taran, Olena Smihunova
E-ISSN: 2224-2899
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Volume 20, 2023
of scientists. Research in this area has different
directions and reveals various aspects of the topic.
For example, Noura Al-Suwaidi and Haitham
Nobanee (2020), [1], analyse scientific theoretical
study and practices, gaps in the safeguards adopted
by countries regarding anti-money laundering
(AML) and anti-terrorism financing (ATF). Marco
Arnone and Leonardo S. Borlini (2011), [2], provide
an empirical assessment and identify regulatory
issues related to international anti-money laundering
(AML) programs. Leonov S., Yarovenko H.,
Boiko A. & Dotsenko T. (2019), [3], investigate the
information system for monitoring bank transactions
related to money laundering.
The state of the anti-money laundering system
affects the macroeconomic stability of the system,
[4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18;
19; 20; 21]. The relationship between
macroeconomic stability and the effectiveness of
state regulation has been studied in Lyeonov S.,
Vasylieva T. & Lyulyov O. (2018), [22], and Bilan
Y., Tiutiunyk I., Lyeonov S. & Vasylieva T. (2020),
[23]. Bouchetara M., Nassour A., Eyih S. (2020),
[24], analyse the role and tools of macroprudential
policy. The study of the shadow economy as a factor
of macroeconomic instability conducted by
Zolkover A., Georgiev M. (2020), [25], Zolkover
A., Terziev V (2020), [26], Shpak et al., [27], Bilan
et al., [28], Yoshimori M. (2019), [29], determine
the impact of the shadow economy on public
administration and financial and economic security.
Vasylieva T., Jurgilewicz O., Poliakh S.,
Tvaronavičienė M., & Hydzik P. (2020), [30],
studied the problems of measuring financial
protection and its effect proceeds from crime.
Buriak A., Lyeonov S., & Vasylieva T. (2015), [31],
describe the fight against money laundering on the
example of the banking system in Ukraine through
the legal framework improvement. The activities of
financial intermediaries and their impact on the anti-
money laundering system are identified in the works
of Brychko M., Savchenko T., Vasylieva T., &
Piotrowski P. (2021), [32].
An important area of research is to assess the risk
of money laundering through financial institutions,
[33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43]. Dmytrov
S., Medvid T. (2017), [44], suggest to assess the
money laundering risks using indices-based
analysis; Kuzmenko O., Šuleř P., Lyeonov S.,
Judrupa I., & Boiko A. (2020), [45], offer Data
mining and bifurcation analysis of the money
laundering risk with the involvement of financial
institutions. The FATF evaluates the country’s anti-
money laundering system, but the FATF’s
effectiveness methodology does not reflect the focus
on anti-money laundering outcomes. Pol R.F., [46],
examines the FATF approach and notes the
misapplication of outcome labels (output labels to
outputs), making it impossible to assess the impact
of anti-money laundering policies.
It is worth emphasizing that modelling the
existing and projected systems and processes is
widely used in today’s conditions to analyse various
issues of all sectors of the world and national
economy, [47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57;
58; 59; 60; 61; 62]. For example, Subeh M. A.,
Boiko A. (2017), [63], conduct modelling of the
public financial monitoring service effectiveness in
terms of anti-money laundering and anti-terrorist
financing; Kozmenko O. & Kuzmenko O. (2013),
[64], carry out modelling of dynamics of banking
system stability; Kuzmenko O. & Koibichuk V.
(2018), [65], use econometric modelling of the
influence of relevant gender policy indicators on the
banking system efficiency. Among such models, a
specific method of economic-mathematical
modelling is modelling based on methods of
survival analysis, which is covered in the works of
the following specialists: Shoaee S. & Khorram E.
(2020), [66] – general theoretical features are
studied; Platero C. & Tobar M. (2020), [67], and
Stevens N., Lydon M., Marshall A.H. & Taylor S.
(2020), [68], describes the practical application in
medicine and health care.
Although many scientists worldwide are working
to study the effectiveness of the anti-money
laundering system, anti-terrorist financing and the
financing of the proliferation of mass destruction
weapons, this question remains open and needs
further development (Mavlutova et al. (2021), [69];
Malyarets et al. (2021), [70]; Perevozova et al.
(2019), [71]; Vovk et al. (2020), [72]). We believe
that special attention should be paid to modelling
based on survival analysis methods, which allow us
to assess and analyse the probability of appearance
or occurrence of inevitable consequences over time.
This article aims to assess the anti-money
laundering system effectiveness, terrorist financing
and the proliferation of mass destruction weapons
based on statistical modelling and comparison of the
countermeasures effectiveness based on survival
analysis in groups of banks around the world.
Relevant factors influence the assessment of the
anti-money laundering system effectiveness,
terrorist financing and proliferation of mass
destruction weapons based on the application of the
main components method by scree plot creation and
determining the factor loads of indicators.
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2023.20.106
Olha Kuzmenko, Olena Krukhmal,
Vitaliia Koibichuk, Kostiantyn Hrytsenko,
Oleksandr Kushneryov, Vita Hordiienko,
Galyna Pasemko, Oksana Taran, Olena Smihunova
E-ISSN: 2224-2899
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Volume 20, 2023
3 Methodology and Data
To implement this stage, 25 banks from 12 countries
were selected (Latvia, Denmark, Malta,
Netherlands, Luxembourg, Portugal, Switzerland,
Sweden, Korea, USA, China, Germany) for 2016-
2019, for which decisions were made on the
application of sanctions for violation by banks of the
requirements of the legislation in the field of money
laundering, terrorism financing. Data on violations
and sanctions were obtained from published court
decisions. Relevant dates in the format: month, date,
year are given in columns 1 - 6 of table 1.
Eleven indicators of Effectiveness in a Country
of Parents (using the 2013 FATF Methodology)
were selected in terms of 25 banks in the world
(columns 7-17 of Table 1) to characterize the anti-
money laundering system: IO1- IO11. It uses an
approach focused on determining the achievement
of specific results in implementing measures to
combat money laundering and terrorist financing to
assess the Effectiveness of the FATF. Each of them
is one of the critical goals of effective Anti-Money
Laundering and Countering Terrorism Financing
(AML / CFT):
- “ІО1 - Money laundering and terrorist
financing risks are understood and, where
appropriate, actions co-ordinated domestically to
combat money laundering and the financing of
terrorism and proliferation;
- IO2 International co-operation delivers
appropriate information, financial intelligence, and
evidence, and facilitates action against criminals and
their assets;
- IO3 Supervisors appropriately supervise,
monitor and regulate financial institutions and
DNFBPs for compliance with AML/CFT
requirements commensurate with their risks;
- IO4 Financial institutions and DNFBPs
adequately apply AML/CFT preventive measures
commensurate with their risks, and report suspicious
transactions;
- IO5 Legal persons and arrangements are
prevented from misuse for money laundering or
terrorist financing, and information on their
beneficial ownership is available to competent
authorities without impediments;
- IO6 Financial intelligence and all other
relevant information are appropriately used by
competent authorities for money laundering and
terrorist financing investigations;
- IO7 Money laundering offences and
activities are investigated and offenders are
prosecuted and subject to effective, proportionate
and dissuasive sanctions;
- IO8 Proceeds and instrumentalities of
crime are confiscated;
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2023.20.106
Olha Kuzmenko, Olena Krukhmal,
Vitaliia Koibichuk, Kostiantyn Hrytsenko,
Oleksandr Kushneryov, Vita Hordiienko,
Galyna Pasemko, Oksana Taran, Olena Smihunova
E-ISSN: 2224-2899
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Volume 20, 2023
Table 1. Input statistical base of the study
№
Bank
Country of
Parent
The date of the last
event that preceded
the sanctions
Date of sanctions
application
А
В
С
1
2
3
4
5
6
1
ABLV Bank
Latvia
13
2
2018
12
6
2018
2
Danske bank Estonia
Denmark
8
5
2018
1
10
2019
3
Pilatus Bank
Malta
19
3
2018
4
11
2018
4
ING
Netherlands
3
4
2016
4
9
2018
5
CA Indosuez Wealth (Europe)
France
3
4
2016
15
12
2017
6
DNB Luxembourg S.A.
Luxembourg
3
4
2016
15
12
2017
7
Nordea Bank S.A.
Luxembourg
15
12
2017
14
11
2019
8
Novo Banco S.A.
Portugal
3
4
2016
15
12
2017
9
LPB Bank
Latvia
25
7
2016
16
10
2018
10
Bank Julius Baer & Co. Ltd.
Switzerland
1
11
2015
27
5
2021
11
Bank Hapoalim B.M.
Switzerland
12
11
2015
30
4
2020
12
Rietumu Banka
Latvia
17
7
2017
15
6
2021
13
PNB banka (NORVIK BANKA)
Latvia
19
7
2017
15
8
2019
14
Swedbank
Sweden
1
2
2019
5
5
2021
15
Industrial Bank Of Korea
Korea
14
12
2016
20
4
2020
16
Apple Bank for Savings
USA
1
9
2018
21
12
2020
17
Mega International Commercial Bank
Co., Ltd.
USA
31
12
2016
17
1
2018
18
Citibank N.A.
USA
5
4
2012
4
1
2018
19
Capital One Bank
USA
7
10
2015
23
10
2018
20
Industrial and Commercial Bank of
China Financial Services LLC
China
1
6
2014
16
5
2018
21
Deutsche Bank AG
Germany
31
12
2016
30
5
2017
22
Lone Star National Bank
USA
30
11
2014
1
11
2017
23
Habib Bank
USA
15
12
2015
7
9
2017
24
Gibraltar Private Bank & Trust Co.
USA
16
10
2014
23
2
2016
25
Agricultural Bank of China
China
31
7
2015
4
11
2016
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2023.20.106
Olha Kuzmenko, Olena Krukhmal,
Vitaliia Koibichuk, Kostiantyn Hrytsenko,
Oleksandr Kushneryov, Vita Hordiienko,
Galyna Pasemko, Oksana Taran, Olena Smihunova
E-ISSN: 2224-2899
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Volume 20, 2023
Continuation of Table 1
№
Bank
IO1
IO2
IO3
IO4
IO5
IO6
IO7
IO8
IO9
IO10
IO11
Censored
А
В
7
8
9
10
11
12
13
14
15
16
17
18
1
ABLV Bank
ME
SE
ME
ME
LE
ME
ME
ME
ME
ME
LE
completed
2
Danske bank Estonia
ME
SE
LE
LE
ME
ME
ME
ME
SE
ME
SE
completed
3
Pilatus Bank
ME
SE
LE
ME
ME
ME
LE
LE
ME
ME
SE
completed
4
ING
-
-
-
-
-
-
-
-
-
-
-
censored
5
CA Indosuez Wealth
(Europe)
-
-
-
-
-
-
-
-
-
-
-
censored
6
DNB Luxembourg S.A.
-
-
-
-
-
-
-
-
-
-
-
censored
7
Nordea Bank S.A.
-
-
-
-
-
-
-
-
-
-
-
censored
8
Novo Banco S.A.
SE
SE
ME
ME
ME
ME
SE
ME
SE
SE
SE
completed
9
LPB Bank
ME
SE
ME
ME
LE
ME
ME
ME
ME
ME
LE
completed
10
Bank Julius Baer & Co.
Ltd.
SE
ME
ME
ME
ME
SE
SE
SE
SE
SE
SE
completed
11
Bank Hapoalim B.M.
SE
ME
ME
ME
ME
SE
SE
SE
SE
SE
SE
completed
12
Rietumu Banka
ME
SE
ME
ME
LE
ME
ME
ME
ME
ME
LE
completed
13
PNB banka (NORVIK
BANKA)
ME
SE
ME
ME
LE
ME
ME
ME
ME
ME
LE
completed
14
Swedbank
ME
HE
ME
ME
ME
ME
SE
SE
SE
ME
SE
completed
15
Industrial Bank Of
Korea
SE
SE
ME
ME
ME
SE
ME
SE
SE
ME
ME
completed
16
Apple Bank for Savings
SE
SE
ME
ME
LE
SE
SE
HE
HE
HE
HE
completed
17
Mega International
Commercial Bank Co.,
Ltd.
SE
SE
ME
ME
LE
SE
SE
HE
HE
HE
HE
completed
18
Citibank N.A.
SE
SE
ME
ME
LE
SE
SE
HE
HE
HE
HE
completed
19
Capital One Bank
SE
SE
ME
ME
LE
SE
SE
HE
HE
HE
HE
completed
20
Industrial and
Commercial Bank of
China Financial
Services LLC
SE
ME
ME
LE
LE
ME
ME
SE
SE
LE
LE
completed
21
Deutsche Bank AG
-
-
-
-
-
-
-
-
-
-
-
censored
22
Lone Star National
Bank
SE
SE
ME
ME
LE
SE
SE
HE
HE
HE
HE
completed
23
Habib Bank
SE
SE
ME
ME
LE
SE
SE
HE
HE
HE
HE
completed
24
Gibraltar Private Bank
& Trust Co.
SE
SE
ME
ME
LE
SE
SE
HE
HE
HE
HE
completed
25
Agricultural Bank of
China
SE
ME
ME
LE
LE
ME
ME
SE
SE
LE
LE
completed
Notes:
ІО1- ІО11 - immediate outcomes, which represent key goals that an effective AML/CFT system should;
HE - High level of effectiveness;
SE - Substantial level of effectiveness;
ME - Moderate level of effectiveness;
LE - Low level of effectiveness.
- IO9 Terrorist financing offences and
activities are investigated and persons who finance
terrorism are prosecuted and subject to effective,
proportionate and dissuasive sanctions;
- IO10 Terrorists, terrorist organisations
and terrorist financiers are prevented from raising,
moving and using funds, and from abusing the NPO
sector;
- IO11 Persons and entities involved in the
proliferation of weapons of mass destruction are
prevented from raising, moving and using funds,
consistent with the relevant UNSCRs” [73].
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2023.20.106
Olha Kuzmenko, Olena Krukhmal,
Vitaliia Koibichuk, Kostiantyn Hrytsenko,
Oleksandr Kushneryov, Vita Hordiienko,
Galyna Pasemko, Oksana Taran, Olena Smihunova
E-ISSN: 2224-2899
1189
Volume 20, 2023
In addition, the availability of complete
information about the considered set of banks is
essential to assess the effectiveness of the national
anti-money laundering system and anti-terrorist
financing based on survival analysis methods.
Thus, the column “Censored” defines two
possible values: “completed” in the presence of
complete information and “censored” in the absence
of data on the occurrence of the event of interest. In
particular, according to the methodology, for some
countries (Netherlands, France, Luxembourg,
Germany) evaluation adopted by the FATF since
2013 has not been conducted.
4 Results
4.1 The Study of the Anti-Money Laundering
System Effectiveness based on Survival
Tables
The technique of constructing survival tables is one
of the data analysis methods of survival, based on
the table of frequencies of the possible occurrence
of critical events following a certain number of
intervals. In terms of the anti-money laundering
system effectiveness, the approach based on
survival tables enables to build frequency tables of
possible decisions on the application of sanctions
(fines, revocation of licenses) for violations by
banks of anti-money laundering and terrorist
financing legislation.
We use the software package Statistica to
implement this stage. Then we execute the
following command: Statistics / Linear / Nonlinear
Models / Survival Analysis / Life labels and
Distributions, i.e. by selecting the command table of
lifetime and distribution (Figure 1).
Based on the data in Figure 1, it is possible to
conclude that for the selected set of banks, their
activities are constantly monitored in terms of anti-
money laundering and on average, in cases of
violations within 2100 days (i.e. 5.75 years), the
relevant banks are allowed to eliminate them, or an
appropriate managerial decision is made to declare
banks insolvent or liquidate banks, which is a
quantitative feature of the national system
efficiency.
Thus, during the first 191 days after the revealed
factors regarding the use of banks for money
laundering (after the last event preceding the
sanctions) among the 25 banks considered in the
sample (Count Entering column), the number of
banks subject to inspection and not liquidated is
100%, i.e. 25 (column Number Exposed). During
this period, the share of banks that intensified their
activities in anti-money laundering was 95.943%
(Proportion Surviving column).
In contrast, the percentage of liquidated banks
(or banks subject to fines) within 191 days after
deciding to declare the bank insolvent is 4.1%.
Moving to the next time interval – the next 191 days
after discovering the facts of legalization of criminal
proceeds (after the last event preceding the
sanctions), the share of liquidated banks (or banks
subject to fines) increases to 4.3%.
Life Table (Spreadsheet2.sta)
Log-Likelihood for data: -47,5847
Interval
Interval
Start
Mid
Point
Interval
Width
Num
ber
Enter
ing
Num
ber
With
drwn
Num
ber
Expo
sed
Num
ber
Dyin
g
Proportn
Dead
Propo
rtn
Surviv
ng
Cum.
Prop
Survi
vng
Problty
Density
Hazard
Rate
Std.Err.
Cum.S
urv
Std.Err.
Prob.Den
Std.Err.
Haz.Rate
Median
Life
Exp
Std.
Err.
Life
Exp
Intno.1
Intno.2
Intno.3
Intno.4
Intno.5
Intno.6
Intno.7
Intno.8
Intno.9
Intno.10
Intno.11
Intno.12
0
95
191
25
1
25
1
0,041
0,959
1,000
0,00021
0,00022
0,000
0,00021
0,00022
900
120
191
286
191
23
0
23
1
0,043
0,957
0,959
0,00022
0,00023
0,040
0,00021
0,00023
733
119
382
477
191
22
0
22
4
0,182
0,818
0,917
0,00087
0,00105
0,056
0,00040
0,00052
567
117
573
668
191
18
3
17
3
0,182
0,818
0,751
0,00071
0,00105
0,088
0,00038
0,00060
514
155
764
859
191
12
1
12
3
0,261
0,739
0,614
0,00084
0,00157
0,101
0,00044
0,00090
494
305
955
1050
191
8
0
8
2
0,250
0,750
0,454
0,00059
0,00150
0,109
0,00039
0,00105
477
135
1145
1241
191
6
0
6
1
0,167
0,833
0,340
0,00030
0,00095
0,107
0,00029
0,00095
382
234
1336
1432
191
5
0
5
2
0,400
0,600
0,284
0,00059
0,00262
0,103
0,00039
0,00179
286
213
1527
1623
191
3
0
3
1
0,333
0,667
0,170
0,00030
0,00210
0,088
0,00029
0,00205
382
220
1718
1814
191
2
0
2
0
0,250
0,750
0,113
0,00015
0,00150
0,075
0,00021
0,00209
318
180
1909
2005
191
2
0
2
1
0,500
0,500
0,085
0,00022
0,00349
0,066
0,00023
0,00329
95
135
2100
1
0
1
1
0,500
0,500
0,043
0,045
Fig. 1: Table of decision-making frequencies on the application of sanctions (fines, revocation of licenses) for
violation by banks of the legislation requirements in the field of anti-money laundering and terrorist financing
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2023.20.106
Olha Kuzmenko, Olena Krukhmal,
Vitaliia Koibichuk, Kostiantyn Hrytsenko,
Oleksandr Kushneryov, Vita Hordiienko,
Galyna Pasemko, Oksana Taran, Olena Smihunova
E-ISSN: 2224-2899
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Volume 20, 2023
The percentage of banks that took measures to
counter the existing facts of violations is reduced to
95.7%. It is the indicator of the survival share
(column Proportion Surviving) indicates the
effectiveness of the anti-money laundering system,
which constantly decreases during the first six-time
intervals (764 days - 2 years) after the detection of
violations or insolvency of banks from 0.959 shares
to 0.739 units.
During the following two periods (from 765 to
1145 days, i.e. up to 3.1 years), the effectiveness of
the anti-money laundering system increases to the
level of 0.833 units and in the following 3-time
intervals on day 191, we observe a decrease with a
gradual increase to level 0, 75 as of 1527 (4.2 years)
the day after the last event preceding the sanctions.
Further, in the previous two-time intervals, the anti-
money laundering system effectiveness decreases to
the level of 0.5 shares of the unit and remains at this
level. Thus, in terms of imposing sanctions, the
periods of 1145 and 1527 days (3.1 and 4.2 years,
respectively) after the last event preceding the
sanctions are important from the point of view of
the anti-money laundering system effectiveness.
The probability density indicator (column
Probability Density) is interesting from the point of
view of the analysis, i.e., the assessment of the
decision probability to liquidate the bank (the need
to apply sanctions to the bank) in the appropriate
time interval:
(1)
where – assessment of the bank’s liquidation
density (application of sanctions) in terms of the i-th
interval;
, – cumulative shares (survival
functions) of banks that were not liquidated (to
which sanctions were not applied) before the
beginning of the i-th and i + 1 intervals;
– the width of the i-th interval.
Based on the data obtained in Figure 1 in terms
of probability density, one can conclude that the
probability of banks’ liquidation declared insolvent
(sanctions) during the first 387 days (about one
year) is the highest and is 0.00087 units. In the
following period, this figure decreased sharply to
0.00022 units.
The analysis of the indicator of the failure rate
function or instantaneous risk function (graph
Hazard rate of Figure 1) is critical in the context of
this stage, which for the normal type of distribution
takes the form:
(2)
where - failure rate functions or
instantaneous risk functions for the normal type of
distribution;
t – time indicator;
– standard deviation.
The hazard rate function is defined as an estimate
of the probability that a surviving bank (not
liquidated, sanctioned) before the relevant time
interval will be liquidated (sanctions will be
applied) during this interval. (Next 191 days for this
case). The analysis of this indicator shows the
instantaneous risk function at the level of 0.00022
units for the first time interval. It means that the
instantaneous risk of the decision on their
elimination (application of sanctions) is 0.00022
units among 25 banks that were not liquidated after
the decision on their insolvency (to which no
sanctions were applied). This risk increases
gradually to 0.00157 units over 2, 3, 4 and 5
intervals (i.e., from 192 to 764 days) and then
decreases to 0.00095 units at the end of the next
interval. During the following intervals until the end
of the study period, the immediate risk of deciding
on their elimination (application of sanctions)
increases to 0.00349 units, i.e., is the largest during
the two periods: from 1336 to 1432 days (0.00262
units) and from 1909 to 2005 days (0.00349 units).
4.2. A Study of the Anti-Money Laundering
National System Effectiveness
A study of the anti-money laundering national
system effectiveness is based on the Kaplan-Meier
method, which involves assessing the survival and
risk functions. The advantage of using the Kaplan-
Meier method compared to the method of life tables
described in the second stage is that in this
approach, performance evaluations do not depend
on the grouping of the observation interval into
intervals.
The Kaplan-Meier method involves estimating
the survival function as follows:
(3)
where – the assessment of survival function;
– the total number of observation objects
(banks) in the studied sample;
– product (geometric sum) in the
context of all observation objects (banks), the study
of which is completed by the time t;
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Olha Kuzmenko, Olena Krukhmal,
Vitaliia Koibichuk, Kostiantyn Hrytsenko,
Oleksandr Kushneryov, Vita Hordiienko,
Galyna Pasemko, Oksana Taran, Olena Smihunova
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Volume 20, 2023
– takes a single value if the observation in
terms of the studied bank is not censored
(completed), and zero value if the observation in
terms of the studied bank is censored (incomplete,
lost communication);
і – the observation number is not in terms of
the studied bank in the source file, and the
observation number in the new file, ordered by the
number of days of banks’ life.
We use the software package Statistica for this
stage. The following commands were executed:
Statistics / Linear / Nonlinear Models / Survival
Analysis / Kaplan and Meier product-limit method,
i.e., selecting the command Method of product
restriction Kaplan and Meyer (Fig. 2).
Kaplan-Meier (Product-limit) analysis (Spreadsheet2.sta)
Note: Censored cases are marked with +
Case
Number
Time
Cumulatv
Survival
Standard
Error
1
21+
3
17
25
24
2
8
6+
5+
23
7+
13
9
14
16
4+
22
19
15
12
20
11
10
18
119,000
0,960000
0,039192
150,000
230,000
0,918261
0,055423
382,000
0,876522
0,066797
462,000
0,834783
0,075539
495,000
0,793044
0,082492
511,000
0,751304
0,088079
621,000
0,709565
0,092548
621,000
621,000
632,000
0,662261
0,097723
699,000
757,000
0,611318
0,102629
813,000
0,560375
0,105968
824,000
0,509431
0,107887
842,000
0,458488
0,108461
884,000
1067,000
0,401177
0,108999
1112,000
0,343866
0,107443
1223,000
0,286555
0,103701
1429,000
0,229244
0,097520
1445,000
0,171933
0,088390
1631,000
0,114622
0,075247
2034,000
0,057311
0,055297
2100,000
0,000000
0,000000
Fig. 2: The results of the effectiveness analysis of anti-money laundering system based on the Kaplan-Meier
method
Analysing the anti-money laundering system
effectiveness based on the Kaplan-Meier method
using the data in Figure 2, we note that the censored
banks are marked with a + sign. This table groups
all surveyed banks by the number of days (column
Time), when the bank will intensify activities in
combating money laundering after the revealed
violations, and take insolvency decisions (the need
for sanctions). The column Cumulative Survival in
Figure 2 shows the probability that the bank in
question will "live" (will not be liquidated for
violations in terms of money laundering) and take
countermeasures.
Thus, the national anti-money laundering
effectiveness is the highest at the level of at least
95% in the first 119 days after violation detection.
This figure is reduced to 90% in the interval up to
230 days (about eight months); up to 80% in the
range of up to 462 days (1.3 years), up to 50% in the
range of up to 824 days (2.3 years). It means that the
effectiveness of anti-money laundering measures
and anti-terrorist financing will decline rapidly with
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Olha Kuzmenko, Olena Krukhmal,
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Oleksandr Kushneryov, Vita Hordiienko,
Galyna Pasemko, Oksana Taran, Olena Smihunova
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increasing time passed since the detection of the
offence (decision-making on the need for sanctions).
We represent Figure 3 to visualize the identified
dependence.
ABLV Bank of Latvia, which decided to be
liquidated based on a FinCEN report on suspicion of
the bank in money laundering, currency control
avoidance, belongs to the group of banks for which
the anti-money laundering system effectiveness is
the highest at 95% in the first 119 days after the
facts of violations detection. High counteraction
efficiency (at least 75%) in the first 514 days after
detecting the offence is also peculiar for the USA,
China, Malta, Danish systems. A specific feature is
that this response interval includes banks that have
violated the law in anti-money laundering, anti-
terrorist financing, and proliferation of mass
destruction weapons. In particular, there are cases of
liquidated Danske bank Estonia, ABLV Bank. Most
cases of banks, using which it is decided to apply
sanctions (fine or re-fine) due to imperfect
monitoring and prevention of violations in the
money laundering combating, terrorist financing and
financing the proliferation of mass destruction
weapons, belong to the quartile with an efficiency of
at least 50% and a response interval of up to 827
days. A quartile with an efficiency of at least 25%
and a response interval of up to 1354 days includes
cases of banks that have been sanctioned (fined) for
their inability to create an effective system for
combating money laundering, terrorist financing and
proliferation of mass destruction weapons.
Survival Function
Complete Censored
0500 1000 1500 2000 2500
Survival Time
-0,1
0,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1,0
1,1
1,2
Cumulative Proportion Surviving
Fig. 3: Dependence of the anti-money laundering system effectiveness (based on the Kaplan-Meier method)
on the time interval after the violation detection
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Olha Kuzmenko, Olena Krukhmal,
Vitaliia Koibichuk, Kostiantyn Hrytsenko,
Oleksandr Kushneryov, Vita Hordiienko,
Galyna Pasemko, Oksana Taran, Olena Smihunova
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We construct Figure 4 to reflect the distribution
of the anti-money laundering system effectiveness,
presented in Figure 3. We give the percentage there:
25% (lower quartile) - banks that make decisions on
liquidation (sanctions), i.e., take appropriate
measures in terms of countering the offence, during
the first 514 days (1.4 years) after the detection of
these facts; 50% (median) - reflect the banks that
activate countermeasures within at least 827 days
(2.3 years). The decision on the largest number of
banks (75%) on anti-money laundering measures is
made within 1354 days after the detection of
violation, i.e., within 3.7 years.
Percentiles of (Spreadsheet2.sta)
the Survival Function
Percentiles
Survival
Time
25'th percentile (lower quartile)
50'th percentile (median)
75'th percentile (upper quartile)
514,438
827,332
1354,395
Fig. 4: Percentile of the survival function
4.3 Comparison of the Effective Anti-Money
Laundering System: Modelling based on the
Analysis of Survival in Groups of Banks
around the World
We use the software package Statistica to
implement this stage. Therefore, we perform the
following command: Statistics / Linear / Nonlinear
Models /
Survival Analysis / Comparing multiple samples,
i.e., by choosing the command to compare survival
in more than two groups (Fig. 5). We analyse the
results obtained under the cumulative share of banks
that "survived" (were not liquidated or to which
sanctions were not applied) by groups. This
procedure helps to plot the cumulative survival
function for banks in each group of countries
separately.
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Olha Kuzmenko, Olena Krukhmal,
Vitaliia Koibichuk, Kostiantyn Hrytsenko,
Oleksandr Kushneryov, Vita Hordiienko,
Galyna Pasemko, Oksana Taran, Olena Smihunova
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Cumulative Proportion Surviving (Kaplan-Meier)
Complete Censored
Latvia
Denmark
Netherlands
Luxembourg
Portugal
Switzerland
Sweden
Germany
0
200
400
600
800
1000
1200
1400
1600
1800
2000
2200
2400
Time
-0,1
0,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
Cumulative Proportion Surviving
Cumulative Proportion Surviving (Kaplan-Meier)
Complete Censored
Malta
Korea
USA
China
0200 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400
Time
-0,1
0,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1,0
Cumulative Proportion Surviving
Fig. 5: Comparison of the observed banks in anti-money laundering (based on the Kaplan-Meier method) from
the time interval after the detection of violations for the sample of countries
Table 2. Life Table for Group: Latvia, Denmark, Netherlands, Luxembourg, Portugal, Switzerland,
Sweden, Germany
Lower
Limit
Latvia
Denmark
Netherlands
Luxembourg
Portugal
Switzerland
Sweden
Germany
% Srvvng
Cum.%.Sr
% Srvvng
Cum.%.Sr
% Srvvng
Cum.%.Sr
% Srvvng
Cum.%.Sr
% Srvvng
Cum.%.Sr
% Srvvng
Cum.%.Sr
% Srvvng
Cum.%.Sr
% Srvvng
Cum.%.Sr
119
75
100
100
100
100
100
100
100
100
100
100
100
100
100
0
100
332
100
75
0
100
100
100
100
100
100
100
100
100
100
100
0
0
545
67
75
0
0
100
100
0
100
0
100
100
100
100
100
0
0
757
50
50
0
0
0
100
0
0
0
0
100
100
0
100
0
0
970
100
25
0
0
0
0
0
0
0
0
100
100
0
0
0
0
1183
100
25
0
0
0
0
0
0
0
0
100
100
0
0
0
0
1396
0
25
0
0
0
0
0
0
0
0
100
100
0
0
0
0
1608
0
0
0
0
0
0
0
0
0
0
50
100
0
0
0
0
1821
0
0
0
0
0
0
0
0
0
0
0
50
0
0
0
0
2034
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Table 3. Life Table for Group: Malta, Korea, USA, China
Lower
Limit
Malta
Korea
USA
China
% Srvvng
Cum.%.Sr
% Srvvng
Cum.%.Sr
% Srvvng
Cum.%.Sr
% Srvvng
Cum.%.Sr
230,0000
0
100
100
100
86
100
100
100
437,7778
0
0
100
100
67
86
50
100
645,5555
0
0
100
100
75
57
100
50
853,3333
0
0
100
100
100
43
100
50
1061,111
0
0
0
100
33
43
100
50
1268,889
0
0
0
0
100
14
0
50
1476,667
0
0
0
0
100
14
0
0
1684,445
0
0
0
0
100
14
0
0
1892,222
0
0
0
0
0
14
0
0
2100,000
0
0
0
0
0
0
0
0
A similar step system is typical for Latvia.
There are tough and more aggressive systems in
Germany, Denmark, Portugal, Sweden, Malta.
However, the distinctive feature for these
countries is the presence of only one bank in the
sample.
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Oleksandr Kushneryov, Vita Hordiienko,
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Proportion Surviving
Latvia
Denmark
Netherlands
Luxembourg
Portugal
Switzerland
Sweden
Germany
119,00 544,56 970,11 1395,7 1821,2 2246,8 2672,3
Interval Start
0,0
0,2
0,4
0,6
0,8
1,0
Proportion Surviving
Proportion Surviving
Malta
Korea
USA
China
230,00
437,78
645,56
853,33
1061,1
1268,9
1476,7
1684,4
1892,2
2100,0
2307,8
2515,6
2723,3
Interval Start
0,0
0,2
0,4
0,6
0,8
1,0
Proportion Surviving
Fig. 6: Comparison of the number of observed banks that have been inspected and not liquidated (they have not
been sanctioned) depending on the time interval after the violation detection for the sample of countries
Analysis of Figure 5 suggests that the United States
has the most flexible anti-money laundering system,
because during the first three years after the
detection of violations, one can observe the most
intensive system of gradual control weakening
(column Cum.%. Sr table 3, Fig. 5), when the
number of banks gradually decreases from the level
of 86% during the first 438 days from the revealing
the violations (application of sanctions) to 57% -
646 days (1.8 years), to 43% - 1061 days (2.9
years), 14% - 1892 days (5.2 years), and level 0 by
the end of the study period. At the same time, the
number of banks that successfully passed the
inspection and were not subject to sanctions
(column% Srvvng table 3, Fig. 6) has a variable
nature: the period of greatest control over the
application of sanctions which is critical for banks,
from 33% of banks in 1061 days ( 2.9 years) after
monitoring until the easing periods, when no
sanctions were applied to any of the observed banks
- ie 100% in the periods of 853 days (2.3 years) and
from 1269 to 1684 days (3.5 - 4.6 year) after the
inspection.
4.4. Determination of Relevant Factors
Influencing the Assessment of the Anti-
Money Laundering System Effectiveness
based on the Application of the Principal
Components Method
Several intermediate calculations were performed to
implement this stage.
The quantitative evaluation by the quality
indicators of Effectiveness in a Country of Parent
(using the 2013 FATF Methodology) introducing
the following scale:
(4)
where – quality evaluation of j-index
Effectiveness in a Country of Parent for i-bank;
– quantitative evaluation of j-index
Effectiveness in a Country of Parent for i-bank;
- High level of effectiveness;
– Substantial level of effectiveness;
– Moderate level of effectiveness;
- Low level of effectiveness.
According to formula (4), the calculations are
presented in tabular form (Table 4).
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Oleksandr Kushneryov, Vita Hordiienko,
Galyna Pasemko, Oksana Taran, Olena Smihunova
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Table 4. Quantitative assessment of indicators of Effectiveness in a Country of Parent
№
Банк
IO1
IO2
IO3
IO4
IO5
IO6
IO7
IO8
IO9
IO10
IO11
1
ABLV Bank
0.5
0.75
0.5
0.5
0.25
0.5
0.5
0.5
0.5
0.5
0.25
2
Danske bank Estonia
0.5
0.75
0.25
0.25
0.5
0.5
0.5
0.5
0.75
0.5
0.75
3
Pilatus Bank
0.5
0.75
0.25
0.5
0.5
0.5
0.25
0.25
0.5
0.5
0.75
4
ING
0
0
0
0
0
0
0
0
0
0
0
5
CA Indosuez Wealth (Europe)
0
0
0
0
0
0
0
0
0
0
0
6
DNB Luxembourg S.A.
0
0
0
0
0
0
0
0
0
0
0
7
Nordea Bank S.A.
0
0
0
0
0
0
0
0
0
0
0
8
Novo Banco S.A.
0.75
0.75
0.5
0.5
0.5
0.5
0.75
0.5
0.75
0.75
0.75
9
LPB Bank
0.5
0.75
0.5
0.5
0.25
0.5
0.5
0.5
0.5
0.5
0.25
10
Bank Julius Baer & Co. Ltd.
0.75
0.5
0.5
0.5
0.5
0.75
0.75
0.75
0.75
0.75
0.75
11
Bank Hapoalim B.M.
0.75
0.5
0.5
0.5
0.5
0.75
0.75
0.75
0.75
0.75
0.75
12
Rietumu Banka
0.5
0.75
0.5
0.5
0.25
0.5
0.5
0.5
0.5
0.5
0.25
13
PNB banka (NORVIK BANKA)
0.5
0.75
0.5
0.5
0.25
0.5
0.5
0.5
0.5
0.5
0.25
14
Swedbank
0.5
1
0.5
0.5
0.5
0.5
0.75
0.75
0.75
0.5
0.75
15
Industrial Bank Of Korea
0.75
0.75
0.5
0.5
0.5
0.75
0.5
0.75
0.75
0.5
0.5
16
Apple Bank for Savings
0.75
0.75
0.5
0.5
0.25
0.75
0.75
1
1
1
1
17
Mega International Commercial Bank Co.,
Ltd.
0.75
0.75
0.5
0.5
0.25
0.75
0.75
1
1
1
1
18
Citibank N.A.
0.75
0.75
0.5
0.5
0.25
0.75
0.75
1
1
1
1
19
Capital One Bank
0.75
0.75
0.5
0.5
0.25
0.75
0.75
1
1
1
1
20
Industrial and Commercial Bank of China
Financial Services LLC
0.75
0.5
0.5
0.25
0.25
0.5
0.5
0.75
0.75
0.25
0.25
21
Deutsche Bank AG
0
0
0
0
0
0
0
0
0
0
0
22
Lone Star National Bank
0.75
0.75
0.5
0.5
0.25
0.75
0.75
1
1
1
1
23
Habib Bank
0.75
0.75
0.5
0.5
0.25
0.75
0.75
1
1
1
1
24
Gibraltar Private Bank & Trust Co.
0.75
0.75
0.5
0.5
0.25
0.75
0.75
1
1
1
1
25
Agricultural Bank of China
0.75
0.5
0.5
0.25
0.25
0.5
0.5
0.75
0.75
0.25
0.25
5.2. Identification of relevant indicators of
Effectiveness in a Country of Parent by constructing
a scree plot and determining the factor loads of the
input statistical base indicators in the study. We will
use the Statistica software package with the
Statistics / Multivariate Expljratory Techniques /
Principal Components and Classification Analysis
toolkit to implement this step. The scree plot (Fig.
7) enables us to determine that the variation of the
effective Kaplan-Meier feature by 84.67% is due to
the first main component variation (i.e., the first
factor), proposed to consider when determining the
priority indicators Effectiveness in a Country of
Parent.
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Oleksandr Kushneryov, Vita Hordiienko,
Galyna Pasemko, Oksana Taran, Olena Smihunova
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Eigenvalues of correlation matrix
Active variables only
84,67%
6,71%
3,88%
2,53%
1,14%
,68%
,29%
,10%
,00% 9
-2 0 2 4 6 8 10 12 14
Eigenvalue number
-1
0
1
2
3
4
5
6
7
8
9
10
11
Eigenvalue
84,67%
6,71%
3,88%
2,53%
1,14%
,68%
,29%
,10%
,00% 9
Fig. 7: Scree plot of evaluation factors relevance regarding Effectiveness in a Country of Parent
The obtained results of using reasonability to
determine the priority of indicators regarding
Effectiveness in a Country of Parent regarding the
first main component are confirmed by the data of
the eigenvalues of the correlation matrix presented
in Figure 8, column% Total variance.
We consider the variable contribution
(indicators) of Effectiveness in a Country of Parent
in terms of the first main component (first factor),
presented in Figure 9. Thus, the relevant indicators
are defined as IO1, IO3, IO4, IO6, IO7, IO8, IO9,
IO10.
Eigenvalues of correlation matrix, and related statistics (Spreadsheet2.sta)
Active variables only
Value number
Eigenvalue
% Total
variance
Cumulative
Eigenvalue
Cumulative
%
1
2
3
4
5
6
7
8
9
9,313583
84,66894
9,31358
84,6689
0,738283
6,71166
10,05187
91,3806
0,426318
3,87562
10,47818
95,2562
0,278123
2,52839
10,75631
97,7846
0,125196
1,13815
10,88150
98,9228
0,074764
0,67967
10,95627
99,6024
0,032158
0,29235
10,98842
99,8948
0,011073
0,10066
10,99950
99,9954
0,000502
0,00456
11,00000
100,0000
Fig. 8: Eigen values of the correlation matrix of indicators regarding Effectiveness in a Country of Parent
Therefore, Immediate Outcomes related to each
of the three Intermediate Outcomes of the
International Standards of Anti-Money Laundering,
Anti-Terrorist Financing and Proliferation of Mass
Destruction Weapons were considered relevant:
- “policy, coordination and cooperation
reduce the risks of money laundering and terrorist
financing” IO1 is relevant, characterizing the
awareness of the money laundering and terrorist
financing risks and the need for appropriate action
to combat them;
- “criminal money and funds for terrorism
support in the financial and other sectors have been
prevented or detected and reported in these sectors”,
are relevant. At the same time, which
characterizes the possibility of legal entities and
institutions for money laundering or terrorism
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financing, with low or average importance for the
sampled countries, is irrelevant;
- “threats to terrorist financing have been
identified and eliminated, terrorists have been
deprived of resources, and those who financed
terrorists have been punished, thus contributing to
the prevention of terrorist acts" -
are relevant. Indicator
is irrelevant, characterizing the
disqualification of individuals and legal entities
involved in the proliferation of mass destruction
weapons, to collect, move and use funds.
Variable contributions, based on correlations (Spreadsheet2.sta)
Variable
Factor 1
Factor 2
Factor 3
Factor 4
Factor 5
Factor 6
Factor 7
Factor 8
Factor 9
IO1
IO2
IO3
IO4
IO5
IO6
IO7
IO8
IO9
IO10
IO11
0,098749
0,000190
0,031736
0,167663
0,014648
0,075106
0,385768
0,011862
0,007375
0,086736
0,094789
0,004299
0,223178
0,450235
0,017083
0,004629
0,044776
0,000171
0,092162
0,023863
0,280214
0,000265
0,007811
0,039972
0,006957
0,025731
0,027770
0,092253
0,058532
0,009230
0,251503
0,160906
0,017104
0,004289
0,192569
0,005085
0,055574
0,481498
0,207464
0,131416
0,004506
0,008694
0,019651
0,004291
0,064620
0,103322
0,001805
0,000973
0,010626
0,080062
0,200056
0,188439
0,166499
0,233214
0,101659
0,004021
0,004325
0,004679
0,001437
0,621540
0,003970
0,024609
0,055431
0,094428
0,101038
0,037894
0,046789
0,047622
0,002295
0,302949
0,068615
0,272481
0,100870
0,040025
0,000338
0,050494
0,117294
0,013110
0,030871
0,012505
0,005503
0,091473
0,108131
0,048453
0,108069
0,104641
0,004835
0,048107
0,216672
0,209596
0,082773
0,086107
0,375075
0,005317
0,010838
0,000205
0,004370
0,231870
0,118755
Fig. 9: The main component contribution (factors) in terms of indicators of Effectiveness in a Country of Parent
4.5 Construction of the Cox Proportional
Intensity Regression Model Regarding the
Dependence of Anti-Money Laundering
System on Independent Factors
Then we use the Statistica software package with
the command Statistics / Advanced Linear /
Nonlinear Models / Survival Analysis / Regression
Models. The Cox proportional intensity model is
based on the idea that the survival intensity function
has a certain level, which acts as a function of
independent variables (covariant):
(5)
where – Kaplan-Meier assessment of the
anti-money laundering system effectiveness for the
i-bank;
- і-parameter of influence
- quantitative assessment of the j-
indicator of Effectiveness in a Country of Parent for
the i-bank.
A necessary condition for building a Cox
proportional intensity regression model of the
dependence of the anti-money laundering
effectiveness on independent factors is the
standardization of the statistical input data, proposed
using the tools of Data / Standardize (table 5).
Thus, choosing columns 1-6 in Table 1 as the
input array to describe the effective feature
(effectiveness of the anti-money laundering system),
and the data in Table 5 - as factor features, we
obtain estimates of Cox model parameters and
standard deviations of parameter estimates (Fig. 10).
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Table 5. Standardized values of indicators of Effectiveness in a Country of Parent
№
Bank
IO1
IO3
IO4
IO6
IO8
IO9
IO10
1
ABLV Bank
-0.10
0.58
0.63
0.00
-0.25
-0.33
-0.14
2
Danske bank Estonia
-0.10
-0.63
-0.58
0.00
-0.25
0.36
-0.14
3
Pilatus Bank
-0.10
-0.63
0.63
0.00
-0.93
-0.33
-0.14
4
ING
-1.82
-1.85
-1.80
-1.79
-1.61
-1.71
-1.49
5
CA Indosuez Wealth (Europe)
-1.82
-1.85
-1.80
-1.79
-1.61
-1.71
-1.49
6
DNB Luxembourg S.A.
-1.82
-1.85
-1.80
-1.79
-1.61
-1.71
-1.49
7
Nordea Bank S.A.
-1.82
-1.85
-1.80
-1.79
-1.61
-1.71
-1.49
8
Novo Banco S.A.
0.75
0.58
0.63
0.00
-0.25
0.36
0.54
9
LPB Bank
-0.10
0.58
0.63
0.00
-0.25
-0.33
-0.14
10
Bank Julius Baer & Co. Ltd.
0.75
0.58
0.63
0.89
0.44
0.36
0.54
11
Bank Hapoalim B.M.
0.75
0.58
0.63
0.89
0.44
0.36
0.54
12
Rietumu Banka
-0.10
0.58
0.63
0.00
-0.25
-0.33
-0.14
13
PNB banka (NORVIK BANKA)
-0.10
0.58
0.63
0.00
-0.25
-0.33
-0.14
14
Swedbank
-0.10
0.58
0.63
0.00
0.44
0.36
-0.14
15
Industrial Bank Of Korea
0.75
0.58
0.63
0.89
0.44
0.36
-0.14
16
Apple Bank for Savings
0.75
0.58
0.63
0.89
1.12
1.05
1.22
17
Mega International Commercial Bank Co., Ltd.
0.75
0.58
0.63
0.89
1.12
1.05
1.22
18
Citibank N.A.
0.75
0.58
0.63
0.89
1.12
1.05
1.22
19
Capital One Bank
0.75
0.58
0.63
0.89
1.12
1.05
1.22
20
Industrial and Commercial Bank of China Financial
Services LLC
0.75
0.58
-0.58
0.00
0.44
0.36
-0.82
21
Deutsche Bank AG
-1.82
-1.85
-1.80
-1.79
-1.61
-1.71
-1.49
22
Lone Star National Bank
0.75
0.58
0.63
0.89
1.12
1.05
1.22
23
Habib Bank
0.75
0.58
0.63
0.89
1.12
1.05
1.22
24
Gibraltar Private Bank & Trust Co.
0.75
0.58
0.63
0.89
1.12
1.05
1.22
25
Agricultural Bank of China
0.75
0.58
-0.58
0.00
0.44
0.36
-0.82
Based on the data of the column “Beta” of Figure
10, we construct a Cox proportional intensity
regression model regarding the dependence of the
anti-money laundering system effectiveness on
independent factors (5) for the selected data set in
terms of 25 banks from 12 countries:
(6)
The most priority indicator of the impact on the
effectiveness of the money laundering combating
system is IO9. For this indicator, the p level
(probability of rejecting the statistical insignificance
hypothesis of this parameter) does not exceed 0.05,
the Student's criterion of statistical significance at
2.10. exceeding the critical value. Indicator IO9 is a
stimulator of the anti-money laundering system
effectiveness, namely when the level of IO9
increases by 1%, the value of the effective feature
will increase by 4.44%.
Analysing the next statistically significant
indicator IO8 (according to the t-statistics criteria,
Wald's criterion, p-level), we note that it is a
disincentive in terms of the anti-money laundering
system effectiveness, namely when the level of IO8
by 1% decrease by 3.03%.
The other six indices of Effectiveness in a
Country of Parent have worse indicators and
significance, which indicates their indirect impact
on the anti-money laundering system effectiveness:
when indicators IO3 and IO4 increase by 1%, the
value of will increase by 0.20% and
1.16%, respectively, due to the focus of these
indicators on assessing the ability to apply
preventive measures for anti-money laundering and
anti-terrorist financing. IO3 and IO4 are correlated
with the prevention of criminal money and funds for
terrorism support in the financial sector. In the
context of the studied issues, indicators IO1, IO6,
IO8, IO10 are disincentives and their increase will
lead to a decrease in the effective feature by 0.88%,
0.39%, 3.03% and 0.94%, respectively. All
indicators identified as disincentives focus on
assessing the ability to recognize risks, record and
block threats of money laundering and terrorist
financing, using relevant information from the
competent authorities.
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Dependent Variable: Survival times in days (Spreadsheet2 stand.sta)
Censoring var.: Censored
Chi? = 9,30920 df = 7 p = ,23125
N=25
Beta
Standard
Error
t-value
exponent
beta
Wald
Statist.
p
IO1
IO3
IO4
IO6
IO8
IO9
IO10
-0,88106
1,924774
-0,45775
0,41434
0,209535
0,647135
0,19780
2,093269
0,09449
1,21871
0,008929
0,924720
1,15858
1,651315
0,70161
3,18541
0,492258
0,482927
-0,38522
1,847574
-0,20850
0,68030
0,043473
0,834838
-3,03438
2,201123
-1,37856
0,04810
1,900427
0,168040
4,44208
2,107500
2,10775
84,95119
4,442598
0,035061
-0,93992
0,864072
-1,08778
0,39066
1,183265
0,276701
Fig. 10: Parameter estimates and standard deviations of parameter estimates
Thus, the system’s effectiveness is determined
by its ability to act in a biased manner and to
prevent violations in the field of money laundering
and terrorist financing.
5 Conclusion
Evaluation of the effectiveness of the anti-money
laundering system, based on the survival analysis
method by constructing tables of "survival" for
banks to which sanctions have been applied,
allowed to determine the response intervals of the
system. It is possible to conclude that, on average, if
there are violations within 2100 days (i.e., 5.75
years), the relevant banks are allowed to liquidate
them or make a managerial decision to declare
banks insolvent or liquidate banks, which is a
quantitative feature of the national system. In this
case, the probability density (column Probability
Density) enables us to assess the probability of a
decision to liquidate the bank (the need to apply
sanctions to the bank) in the appropriate time
interval. Analysis of this indicator shows the value
of the instantaneous risk function at 0.00022 units
per unit for the first time interval (up to 191 days),
then a gradual increase in risk to 0.00157 units per
unit for 2, 3, 4 and 5 intervals (i.e., from 192 to 764
days), and then reduce it to 0.00095 parts per unit at
the end of the next time interval.
Comparing the anti-money laundering system
effectiveness through modelling based on survival
analysis in groups of banks around the world
suggests that the United States has the most flexible
anti-money laundering system.
Based on the application of the principal
components method, the relevant factors influencing
the assessment of the anti-money laundering system
effectiveness are defined. Moreover, a Cox intensity
proportional regression model regarding the
dependence of the anti-money laundering system
effectiveness on independent factors is formed. It is
found that the system effectiveness is determined by
its ability to pro-act and prevent violations in money
laundering and terrorist financing.
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Olha Kuzmenko, Olena Krukhmal,
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Oleksandr Kushneryov, Vita Hordiienko,
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Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
- Olha Kuzmenko carried out development and
design of methodology, creation of models.
- Olena Krukhmal was responsible for
determination of relevant factors influencing the
assessment of the anti-money laundering system
effectiveness based on the application of the
principal components method and preparation of
the published work, specifically critical review.
- Vitaliia Koibichuk was responsible for the
Statistics.
- Vita Hordiienko has implemented the modelling
based on the analysis of survival in groups of
banks around the world.
- Galyna Pasemko was responsible for Construction
of the Cox proportional intensity regression model
regarding the dependence of anti-money
laundering system on independent factors.
- Kostiantyn Hrytsenko applied computational
analysis using of the Statistica software complex.
- Oleksandr Kushneryov was responsible for
application of statistical, mathematical,
computational to analyse and synthesize study
data.
- Oksana Taran carried out preparation, creation of
a published work.
- Olena Smihunova carried out preparation of a
published work, including meaningful translation.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
The article was prepared based on the results of a
research funded by the National Research Fund of
Ukraine "Optimization and automation of financial
monitoring processes to increase information
security in Ukraine." (Registration number:
0120U104810).
Conflict of Interest
The authors have no conflict of interest to declare.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.en
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DOI: 10.37394/23207.2023.20.106
Olha Kuzmenko, Olena Krukhmal,
Vitaliia Koibichuk, Kostiantyn Hrytsenko,
Oleksandr Kushneryov, Vita Hordiienko,
Galyna Pasemko, Oksana Taran, Olena Smihunova
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