=Paper=
{{Paper
|id=Vol-2422/paper24
|storemode=property
|title=Information System for Monitoring Banking Transactions Related to Money Laundering
|pdfUrl=https://ceur-ws.org/Vol-2422/paper24.pdf
|volume=Vol-2422
|authors=Serhiy Leonov,Hanna Yarovenko,Anton Boiko,Tetiana Dotsenko
|dblpUrl=https://dblp.org/rec/conf/m3e2/LeonovYBD19
}}
==Information System for Monitoring Banking Transactions Related to Money Laundering==
https://ceur-ws.org/Vol-2422/paper24.pdf
297
Information System for Monitoring Banking
Transactions Related to Money Laundering
Serhiy Leonov[0000-0001-5639-3008], Hanna Yarovenko[0000-0002-8760-6835],
Anton Boiko and Tetiana Dotsenko
Sumy State University, 2, Rymskoho-Korsakova Str., Sumy, 40000, Ukraine Sumy, Ukraine
slyeonov@gmail.com, a.yarovenko@uabs.sumdu.edu.ua,
antonboyko11@gmail.com, tvdocenko85@gmail.com
Abstract. The article deals with the prototyping of an information system for
intrabank monitoring of transactions related to money laundering. It has been
proven that the automation of financial monitoring system would increase the
bank’s efficiency due to examining all bank transactions without exception,
leveling the human factor, maximizing the speed of identifying suspicious
transactions, which will provide the bank management with the possibility to
reduce reputational risk and minimize losses related to paying penalties imposed
by regulatory agencies. It has been established that the prototype of the
information system for monitoring transactions related to money laundering
through banks should consist of a model of the business process monitoring in an
automated system environment, a DFD model of automated monitoring of
banking transactions, a structural database model, user interface forms and the
logic of validation business rules. The resulting methodological and practical
developments are a universal component of the financial monitoring system of
any bank since they have the opportunity to transform and adapt to new standards
for reporting entities or differentiation of the business processes of a bank.
Keywords: money laundering, intrabank monitoring, information system.
1 Introduction
The problem of countering the shadow economy is relevant for Ukraine since its
independence. According to the Ministry of Economic Development and Trade of
Ukraine, the level of the shadow sector was in the range of 32-43% of GDP in the period
from 2010 to 2018 [1]. This share is confirmed by the FATF studies, which determine
the value of the shadow sector in the range of 20-40% of GDP for transition economies
[2]. It is fair to note that a significant part of the shadow sector in Ukraine is formed as
a result of money laundering.
Given the fact that the financial system of Ukraine is bank-centered, the main
participants in money laundering are banks. Thus, according to the State Financial
Monitoring Service of Ukraine, the number of reports of suspicious financial
transactions recorded in 2017 was 8,013,500 (by 26.8% more than in 2016), and 99%
of these reports were generated by banks. At the same time, we note that more than
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90% of financial transactions of records taken by the State Financial Monitoring
Service belong to compulsory financial monitoring [3]. Thus, the requirements of state
regulators lead to the identification of suspicious transactions, and the system of
internal financial monitoring of banks is ineffective.
Thus, the formation of an autonomous, quick response and multi-functional
intrabank financial monitoring system becomes relevant. The solution of this task is
proposed to be implemented through the prototyping of an information system for
monitoring transactions related to money laundering through banks.
2 Literature Review
The world scientific community pays considerable attention to the study of the
peculiarities of banking transactions related to money laundering. Thus, the place of
banks among other money laundering tools is highlighted in the works by P. He [4],
M. Betron [5], B. Unger [6]. These scientists determine the important role of banking
transactions among all other money laundering methods and emphasize the need for
active counteraction to these illegal actions, both inside the bank and at the level of
state regulation. Moreover, scientists determine the continuing trend of growth in the
funds that were laundered through the financial system.
Other group of scientists J. Simser [7], A. Chong, F. Lopez-De-Silanes [8], D. Sat et
al. [9] and F. Teichmann [10] study the prospects of using different money laundering
tools. Scientists concluded that despite the active use of the latest technologies
(cryptocurrency) for illegal activity, banks in certain regions of the world would remain
a very relevant money laundering tool.
Finance Stability Board [11], Y. Isa et al. [12], and E. Tsingou [13] studied the issue
of financial monitoring in banks and the peculiarities of counteraction to the use of bank
transactions for money laundering. These studies are focused on highlighting the
mechanisms used in various banks worldwide to counteract the use of their transactions
for money laundering, as well as the role of bank staff in this process. In parallel, the
authors emphasize the need for state regulators to intensify the internal banking system
of financial monitoring by developing appropriate coercive regulatory legal acts.
Exploring existing research on the role of information systems in detecting fraud in
the financial sector, we note that E. Karuppiah et al. [14] generalized the basic machine
learning techniques for the preparation, processing and transformation of data related
to money laundering.
In addition, it is necessary to pay attention to some more scientific works. Thus,
V. Pramod, J. Li, P. Gao [15] proposed a new structure for the prevention of money
laundering in banks formed by mapping COBIT (Control for Information and Related
Technology) processes to the COSO (Committee of Sponsoring Organization)
components. In turn, S. Gao, D. Xu, H. Wang, P. Green [16] proposed to use the
intelligent agents technology to increase the flexibility of managerial decisions in the
field of banking monitoring. Thus, the authors have developed a multi-agent framework
in the form of a stand-alone system, which can be integrated into the business processes
of a bank and will detect transactions related to money laundering.
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Scientific paper by E. Divya, P. Umadevi [17], which deals with the Transaction
Flow Analysis (TFA) system, deserves attention. The proposed information model
implies the identification of banking transactions, which are not bound to any file
format, and their subsequent clustering in terms of the probability of being associated
with money laundering.
3 Findings
When studying the features of the prototyping of the information system for intrabank
financial monitoring, we note that the process of identifying transactions related to
money laundering is quite arduous, periodic in nature, significantly dependent on
personnel decisions, but well formalized. Therefore, we analyze the existing system of
intrabank financial monitoring, which was developed using BPMN 2.0 notation [18] and
Bizagi Studio [19] (Fig. 1).
Fig. 1. Diagram of the existing intrabank monitoring business process.
Thus, the identification of the risk related to using bank services for money laundering
consists in assessing the sources of income received by the entity or individual. Thus,
we check:
─ compliance of the funds credited to a bank account with the financial status of the
client;
─ regularity of receipt of funds, and further cash withdrawals;
─ signs of evasion from the mandatory financial monitoring procedure on the part of a
client;
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─ status of a beneficiary in the case of crediting funds from many individuals or legal
entities;
─ payment by the client for remote services;
─ payment of the royalty fee, crediting foreign currency to the card account of the
client;
─ paying off client’s loan for elite goods or real estate;
─ similar IP-addresses of client transactions with other transactions;
─ transactions exceeding 150,000 UAH.
After each verification, the transaction risk record is entered into the database.
Thus, there are the following shortcomings of the existing system for financial
monitoring of risks related to using bank services for money laundering:
─ the absence of a unified system of obligatory transactions, which, depending on the
level of their regulation by a particular regulatory legal act, are mandatory or
recommended;
─ all transactions are carried out manually by a bank employee, requiring the
appropriate competence and a considerable amount of time;
─ the introduction of a transaction into the risk operations base occurs at the discretion
of the banking specialist, which renders impossible a high level of impartiality of the
assessment;
─ risk assessments of money laundering are not conducted by the bank employees
during each transaction. Definition of suspicious transactions is carried out
periodically depending on the risk level of the client, depending on the suspicion of
the specialist in accordance with the client’s transactions or in accordance with the
requests of the back office employees.
Thus, an effective solution to the problems of low efficiency of the intrabank system
for financial monitoring of risks associated with money laundering is the use of
information technologies. Domestic banks do not have such systems due to the specifics
of the subject area. Therefore, we propose to create a prototype of an automated system
for financial monitoring of banking transactions. For this purpose, the team of authors
improved the existing bank monitoring process, taking into account the possibility of
its automation. Figure 2 is a diagram of the improved business process of financial
monitoring, which was developed using BPMN 2.0 notation [18] and Bizagi Studio [19].
Considering the data presented in Figure 2, it can be argued that the automated
system, instead of the employees of the bank front office, should deal with the main
actions related to the verification of suspicious transactions. This will allow unloading
the front office managers regarding verification of potential transactions related to
money laundering. Their automation will assist in improving the efficiency of the bank
staff during the implementation of financial monitoring. Namely, first, it will allow for
constant online verification. Secondly, the situation of the employee’s impact on the
verification process and concealing or distorting its results will no longer be possible.
This will occur because the system provides for the application of business rules logic
that will assist in the automatic selection of those transactions that do not meet the
specified conditions. An administrator is responsible for their settings, and other bank
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employees will not be able to purposefully influence the verification process. Thirdly,
such a system allows verifying a larger volume of transactions concerning their
involvement in money laundering and terrorism financing. Since monitoring is
necessarily applied to transactions, for example, the amount of which exceeds UAH
150,000, transactions with lower amounts, which may also have criminal sources of
origin, remain without attention. The use of an automated system will facilitate the
verification of all transactions, regardless of their amount. Fourthly, the advantage of
the proposed solution is the flexibility of setting up this system in case of changes in
legislation or the provisions of the National Bank of Ukraine and bank instructions for
verifying such transactions. This is possible due to changes in the parameters of
business rules used to verify transactions.
Fig. 2. The monitoring business process model in an automated system environment.
When designing an intrabank financial monitoring system, it is important to build an
information model that provides insight into the interconnections between the system
objects and their structure. For this purpose, based on the proposed business process
(Figure 2), the authors developed an information model based on the Structured
Analysis and Design Technique (SADT) in the DFD (Data Flow Diagrams) notation.
The authors chose this methodology due to its capabilities of the description of data
flows, taking into account their interaction in the process of manual and automated
processing of information. Thus, Figure 3 shows the result of this simulation – the DFD-
model of financial monitoring of banking transactions performed in the software
environment All Fusion Process Modeller [20].
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The proposed model includes the following main entities, such as “Bank Client” and
“Front Office Manager”, 14 main functions related to the verification of banking
transactions concerning their use in money laundering or terrorist financing, and 8 basic
structures for storing information. Input and output streams of information are defined
between the presented objects.
The functions 1-13 from Figure 3 show the main areas of monitoring: the first
verification the criticality of the client’s risk level, the second verification the type of
client, the third verification conformity with financial condition, the forth verification
the regularity of cash flow and cash withdrawal, the fifth verification the signs of
avoidance from obligatory financial monitoring, the sixth verification the cash deposit,
etc. In these areas, there are transactions identified as if there is a risk of money
laundering. The results of verifications are accumulated in the block “Make a decision”
where the decision is made on whether there is a risk on a transaction or there is no risk.
Fig. 3. DFD-model of automated monitoring of banking transactions.
Understanding information about incoming and outcoming streams is very important.
Since the main subject of monitoring is a client transaction, it is verified by comparing
with the criteria. As criteria, a bank can use the client’s financial documentation, loan
payments, information about payments for expensive purchases, transactions that do
not correspond to the client’s type of activity, information about payments of author's
fees, the IP-address of the operation, etc. This information is usually contained in an
automated banking system, where the automated financial monitoring module will be
integrated.
The developed DFD-model formed the basis for the creation of a logical data
scheme, which implementation allowed forming the internal information system of the
system prototype. For this purpose, entities were created, relationships were
established, relations types were selected, and attributes were specified. Thus, a
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complete data structure was created to develop a database of automated monitoring system,
which was developed using Bizagi Studio [19] (Figure 4).
The proposed model (Figure 4) identifies a structured database model running SQL
Server that determines how data is available, stored and used in the system. The value
of the model lies in the fact that it takes into account the main specificity of monitoring
transactions in the bank.
Fig. 4. Database structural model of automated monitoring system.
The next step in developing the system prototype is the development of interfaces and
the definition of basic business rules. Thus, the user interface forms have been
developed that allow seeing how the user will interact with the system. Since the
proposed system carries out the entire verification process without the employee’s
participation, the verification results form were created (Figure 5).
The developed form allows us to get information about the client, the transaction and
the results of the monitoring according to thirteen rules. Only two options were
proposed for each risk position. The system gives the option “YES” if there is a risk of
a transaction. The system issues “NO” in the absence of risk. The information system
also allows us to get a general result of monitoring. The “YES” answer will indicate the
presence of risk at any level of verification and a transaction will be rejected. If there is
no risk at all levels of monitoring, the system will give the answer “NO” and a
transaction will be accepted.
For automatic execution of actions, the system has developed basic business
verification rules. These rules are important for the further development of the
automated system. The development of the rules was carried out according to the
following logic, represented by the formulas 1-3.
To conduct monitoring, there are next business rules (Formulas 1-2):
IF [Condition of Verification_1 ≠ Criteria of Verification_1]
THEN [Risk = 1] ELSE [Risk =0] (1)
…
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IF [Condition of Verification_N ≠ Criteria of Verification_N]
THEN [Risk = 1] ELSE [Risk =0], (2)
where Condition of Verification_1 – a condition for verifying a transaction for a certain
type of risk that corresponds to the first function of Figure 3; Condition of
Verification_N – a condition for verifying a transaction for a certain type of risk that
corresponds to one of the functions of Figure 3 (as an example, it is the condition of
verification the signs of avoidance from obligatory financial monitoring); N – a number
of verifications from 1 to 13; Criteria of Verification_1 – the first criterion that is chosen
to verify the transaction for the risk of money laundering; Criteria of Verification_N -
the criteria 2-13 that is chosen to verify the transaction for the risk of money laundering
(as an example, it is the criterion that corresponds to the information about client's cash
transactions on him account); Risk = 1 – presence of money laundering transaction risk;
Risk =0 – lack of money laundering transaction risk.
Fig. 5. User interface form with results of verification.
To obtain the overall monitoring result, the following business rule is set (Formula 3):
IF [Verification_1 = 1 OR Verification_2 = 1 OR Verification_3 = 1 OR Verification_4
= 1 OR Verification_5 = 1 OR Verification_6 = 1 OR Verification_7 = 1 OR
Verification_8 = 1 OR Verification_9 = 1 OR Verification_10 = 1 OR Verification_11
= 1 OR Verification_12 = 1 OR Verification_13 = 1] THEN [“YES” Risk AND Reject
operation] ELSE [“NO” Risk AND Accept Operation], (3)
where Verification_1,2,…,13 – the result of each verification; “YES” Risk AND Reject
operation – the decision when the risk of money laundering is present and the
transaction is rejected; “NO” Risk AND Accept Operation – the decision when there is
no risk of money laundering and the transaction is accepted.
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The developed rules constitute a group “Define Expressions”, determining the
behavior of the system under certain conditions. Thus, the rules take into account
branching conditions that correspond to a positive verification result when the
transaction is not at risk related to money laundering or negative when the transaction
is entered into the database of risky operations and blocked by the system.
4 Conclusion
It is fair to note that despite the fact that the problem of assessing the risk related to
using banks for money laundering or terrorism financing is not a priority, but its
solution is extremely important both for banks and for the state as a whole. Thus, over
the past five years, the rate of money laundering through banking transactions
significantly exceeds the rate of economic growth in Ukraine. In turn, for banks, the
risks are manifested in the strengthening of supervision on the part of the National Bank
of Ukraine, increased motivation of bank staff to fraud and the future loss of financial
stability.
Banks, as entities of initial financial monitoring, should analyze client's transactions
to identify the features that are typical for the laundering of money obtained illegally.
As part of this activity, they can only detect these operations by post factum. Practical
experience of Ukrainian banks shows that financial monitoring is periodic, non-
systematic, carried out manually, its results can be influenced by the “human factor”,
which is a manifestation of a corrupt component. But the main task of monitoring is to
prevent transactions which there is a risk of money laundering with. Therefore, the
prototyping of an information system for monitoring banking transactions related to
money laundering is a very topical issue.
Thus, a prototype of an automated system for financial monitoring of transactions
was obtained to find their connection with money laundering. The prototype consists
of a monitoring business process model in an automated system environment, a DFD
automated banking monitoring model, a database structural model, user interface forms
and validation business rules logic.
The application of the proposed information system allows us to verify the client's
transactions on the thirteen risk rules. This approach makes it possible to assess the risk
of money laundering for each transaction. If an operation does not correspond at least
one rule, then it is rejected. The system concludes that there is an increased risk of this
transaction. Because of the automatic process, the influence of bank employees on risk
transactions is excluded. Furthermore, the front-office worker can make a decision
based on information obtained from the information system.
The implementation of the proposed system will automate the monitoring process,
reduce its labor intensity, increase the efficiency of verification by processing more
transactions, and shift the focus from the employee to the automated system to reduce
the impact on the verification results.
In the future it is planned to implement the proposed prototype into the practical
activity of banks at the level of subjects of initial financial monitoring. Since this
implementation involves the necessity to optimize the monitoring business process in
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a bank, it requires a considerable amount of time. In today's conditions of intensifying
the struggle with the problem of money laundering, the interest of banks in this decision
is unconditional. Under the influence of regulation of this problem by the National Bank
of Ukraine, the implementation by banks an automated monitoring system will
contribute to the creation of a unified information base of monitoring and information
integration at the level of subjects of state monitoring.
5 Acknowledgements
The article was executed in the framework of state budget scientific research work No.
0118U003574 “Cyber security in the fight against bank fraud: protection of financial
services consumers and growth of financial and economic security of Ukraine” and
scientific research work No. 0117U002251 “Improvement of national anti money
laundering system in terms of increasing financial and economic security of the state”.
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