=Paper=
{{Paper
|id=None
|storemode=property
|title=Ontological Constructs to Create Money Laundering Schemes
|pdfUrl=https://ceur-ws.org/Vol-713/STIDS_R3_MehmetWijesekera.pdf
|volume=Vol-713
|dblpUrl=https://dblp.org/rec/conf/stids/MehmetW10
}}
==Ontological Constructs to Create Money Laundering Schemes==
https://ceur-ws.org/Vol-713/STIDS_R3_MehmetWijesekera.pdf
Ontological Constructs to Create Money Laundering
Schemes
Murad Mehmet and Duminda Wijesekera
George Mason University Fairfax, VA 22030
mmehmet@gmu.edu,dwijesek@gmu.edu
Abstract. There is an increasing tendency in the money laundering sector to
utilize electronic commerce and web services. Misuse of web services and
electronic money transfers occurs at many points in complex trading schemes.
We provide ontological components that can be combined to construct some of
these money laundering schemes. These constructs can be helpful for
investigators, in order to decompose suspected financial schemes and recognize
financial misuses.
Keywords: money laundering, money laundering ontology.
1 Introduction
Money Laundering Schemes (MLS) have evolved in order to take advantage of
internet based financial transactions and web services. To date, regulations alone have
not been able to deter such schemes, as seen in recent examples of long running
money laundering schemes [1], [2], [3], [4]. Digital currencies (E-Money) are
particularly suitable for money laundering schemes because of their global usability,
anonymity, ease of use, and instantaneous transferability. It is becoming increasingly
difficult to differentiate between legitimate and fraudulent transactions because of
their complexity and evolving nature, as described in recent publications [3], [4], [9].
In order to decompose this complexity we provide some basic ontological
constructs that can be used to create known money laundering schemes. These basic
ontological constructs can be integrated with financial transaction specification
languages to provide further forensic analysis, particularly with XBRL, the de-facto
standard for reporting in the financial industry, in order to recognize financial
misuses.
The rest of the paper is organized as follows: Section 2 discusses the well known
money laundering schemes. Section 3 defines the proposed money laundering
ontological constructs. Section 4 presents an example of constructing a money
laundering scheme using the proposed ontological constructs. Section 5 presents a
discussion on related work in the area of money laundering ontologies. Finally,
section 6 presents the conclusion.
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2 Murad Mehmet and Duminda Wijesekera
2 Known Money Laundering Schemes
In order to identify the basic components of existing money laundering schemes, we
list some well known money laundering schemes as follows:
1. Structured Transfer Scheme: This method involves splitting a transfer of funds into
multiple fund transfers involving smaller amounts that are below the threshold of
suspicion.
2. Alternative Remittance Systems Scheme: In this method, all transactions are done
in cash involving parties (two or more) that calculate the difference of their
balances, and make quick payments in their own countries without involving any
electronic wire transfer.
3. Loan Back Scheme: In this method, a shell company (a fictitious company created
merely to transfer money without raising suspicion) transfers funds allocated as
credit from the money launderer in the form of a loan. The loan is then repaid with
laundered money, thereby legitimizing the laundered money.
4. Low Invoicing Scheme: In this method, the seller lowers the invoice to the buyer
as payment for an illegal commodity (such as drugs or weapons). The buyer then
resells the product for a high profit.
5. High Invoicing Scheme: In this method, high prices for goods are paid by
contractors resulting in high profits (laundered money) for the seller. It is
characterized by fabricated deliveries of products, transactions carried out by shell
companies in offshore territories, and use of electronic payments by anonymous
persons.
6. Anonymous Account Holder Services: In this method, accounts are created by E-
Money servers for customers who wish to be anonymous during the use of E-
Money transactions. These are attractive to money launderers due to the ease and
secrecy of fund transfers among the accounts, as well as the accessibility to fund
withdrawals at any regular banking locations.
3 Components of Money Laundering Schemes
The four basic entities that we use to construct a money laundering scheme are
people, organization, portfolio, and messages. The “people” represents the individuals
who participate in a business transaction. This entity can be business related, non-
business related, or a money launderer. The “organization” represents any institution
or firm that engages in financial operation or business trading. The “portfolio”
represents any asset of a person or an organization in a financial institution.
“Messages” represents any form of communication exchanged between people and
organizations.
We also use three auxiliary entities: communication medium, invoice and
identification documents to represent schemes. The “communication medium”
represents any environment that allows the delivery of messages. The “invoice”
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Ontological Constructs to Create Money Laundering Schemes 3
represents the demand for payment issued in trading schemes. “Identification
documents” are used to identify the “people”.
There are many relationships amongst the entities. Therefore, we formally define
these entities and relationships using the Web Ontology Language (OWL).
3.1 The Ontology of Money Laundering Schemes
Fig. 1. The ontology class diagram
The ontology class diagram represents the components used in money laundering
schemes as described in previous section: people, organization, portfolios, messages,
communication medium, invoice and identification documents. We list and describe
the entities in the OWL ontology shown in Figure 1 above as follows:
1. People: Represents individuals who participate in a business transaction. It consists
of the subclasses “business related”, “ML participant”, and “ML not participant”.
This entity is associated with entities: “organization”, “portfolio”, “messages”,
“identification document”, and “money laundering schemes”. For instance, money
launderer “people” need “identification documents”, to send “messages” to
withdraw “portfolio” cash from an “organization” bank, as part of a structuring
“money laundering scheme”.
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4 Murad Mehmet and Duminda Wijesekera
2. Portfolio: Represents financial assets and products, it has many subclasses such as
“cash”, “security”, and “bank account”. This entity is associated with
“organization”, “people” and “messages”. For instance, “people” own accounts
linked to a bank “organization”, they access them via bank transaction “messages”.
3. Organization: Represents any business engaged in trading or financial
transactions, it has many subclasses such as “banking”, “securities trading”, and
“electronic organization”. This entity is associated with entities: “people”,
“portfolio”, “messages”, “invoice”, and “money laundering schemes”. For
instance, a security trading company sends invest “messages”, or issues an
“invoice” from the security account to the account owner.
4. Messages: Represents all messages exchanged in the domain between “people” and
“organization”. All the activities in the domain are performed via messages, such
as “bank messages”, “trade messages”, and “human messages”. This entity is
associated with entities: “people”, “organization”, “portfolio”, “communication
medium” and “money laundering schemes”. For instance, to withdraw funds the
money launderer “people” send the withdraw “message” to the bank
“organization”, and thereby the withdraw “message” accesses the “portfolio” bank
account, as part of the structuring “money laundering scheme” using the phone
“communication medium”.
5. Communication medium: Represents all methods of standard and encrypted
communication. It has many subclasses such as “anonymous proxy server”,
“electronic payment server”, and “mail server”. This entity is associated with
entities “message” and “money laundering schemes”. For instance, the deposit uses
the “electronic payment server” as part of the “money laundering schemes”.
6. Identification document: Represents all documents that can be provided by the
person for identification purposes. It has many subclasses such as “national card
ID” and “passport”. This entity is only associated with the entity “people”. For
instance, money launderer “people” must have an “identification document”
passport.
7. Invoices: Represents trading statements. It consists of the subclasses “invoice to
business” and “invoice to people”. This entity is associated with entities
“organization” and “people”. For instance, an “organization” issues an “invoice” to
“people”.
8. Money laundering schemes: Represents the various money laundering techniques,
it has many subclasses such as “low invoicing scheme” and “structuring scheme”.
The finance industry is very dynamic, as the money laundering techniques continue
to evolve they will be added to our ontology. This entity is associated with entities:
“people”, “organization”, “message”, and “communication medium”. For instance,
money launderer “people” send transfer “message” to bank “organization”, as part
of the high invoicing “money laundering schemes”.
We list and describe the object properties in the OWL ontology as follows:
1. HasProvided: For one entity to provide information to another entity. For instance,
a person provides his or her bank account number to an organization.
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Ontological Constructs to Create Money Laundering Schemes 5
2. HasRequestedBy: An entity makes a request to another entity. For instance, an
EFT is requested by an account holder from a bank.
3. HasRequestedFrom: An entity receives a request from another. For instance, an
EFT requested from a bank by a person.
4. HasUsedAccessed: An entity uses or accesses another entity.
5. Is: To associate an entity within the MLS with their specific entity. For instance,
the entity “EMSS Launderer” is a “MLSParticipant”.
6. IsAssociatedAsClassWith: To associate or link an entity “Value Type” with its
super class.
7. IsFrom: To associate the source entity of messages that is not in the form of a
request. For instance, an electronic fund transfer is from a person.
8. IsOwnedByLinkedTo: An entity that is owned by or linked to another.
9. IsTowards: To associate the target entity of messages that is not in the form of a
request. For instance, an electronic fund transfer is towards a shell company.
10. MustHaveDataOf: An entity has data of another. For instance, a bank must have
data of the account holder person.
4 Example Construction of Money Laundering Scheme
In this section we create the anonymous account holder services scheme, using the
constructs from our OWL ontology. According to our OWL definition, messages are
linked to one or more entities. For instance, opening an account is a relation linked to
the requester entity and the requested entity, the request message is sent by a person
to a bank. Another example can be the relation in electronic fund transfer (EFT),
where there is a receiver entity and a sender entity. Owning an account, however, is
linked to only one entity.
We list the message sequence of the example scheme in Table 1.
Table 1. Choreographies of Anonymous Account Holder Services Scheme
Step Entity Message (Linked Entity)
1st AnonySession HasRequestedFrom(ProxyServer), HasRequestedBy(MLaunderer)
2nd AnonySession IsFrom(ProxyServer), IsTowards(MLaunderer)
3rd EMAccount-1 HasRequestedFrom(EMoneyServer), HasRequestedBy(MLaunderer)
4th EMAccount-1 IsFrom(EMoneyServer), IsTowards(MLaunderer)
5th MLaunderer HasProvided(ShellComp)
6th ShellComp MustHaveDataOf(EMAccount-1)
7th MLaunderer HasUsedAccessed (DepositCash)
8th DepositCash IsFrom(MLaunderer), IsTowards(ShellComp)
9th EMAccount-2 HasRequestedFrom(EMoneyServer), HasRequestedBy(ShellComp)
10th EMAccount-2 IsFrom(EMoneyServer), IsTowards(ShellComp)
11th E-Deposit HasRequestedFrom(EMExchange), HasRequestedBy(ShellComp)
12th EMExchange HasUsedAccessed(EMAccount-2)
13th EFT IsFrom(EMAccount-2), IsTowards(EMAccount-1)
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14th EFT IsFrom(ShellComp), IsTowards(MLaunderer)
15th Withdraw HasRequestedFrom(EMExchange), HasRequestedBy(MLaunderer)
16th Withdraw HasUsedAccessed(EMoneyServer)
17th Withdraw IsFrom (EMAccount-1), IsTowards(MLaunderer)
We briefly describe the choreographies of Table 1 as follows:
Steps 1 and 2 are the request for an anonymous session by the money launderer
and the opening of the session by the proxy server. Steps 3 and 4 are the request for
an electronic currency account by the money launderer and the opening of the account
by the electronic payment server. In steps 5 and 6 the money launderer passes the
account information to the shell company. In steps 7 and 8 the money launderer
transfers cash funds to the shell company. Steps 9 and 10 are the request for an
electronic currency account by the shell company and the opening of the account by
the electronic payment server. Steps 11 and 12 represent the cash deposit of the shell
company to the electronic currency exchange and provision of account information.
Steps 13 and 14 are the transfer of funds from the shell company to the money
launderer using electronic currency accounts. Steps 15, 16, and 17 represent the
withdrawal of funds from the electronic currency account of the money launderer,
using the electronic currency exchange office.
Figure 2 represents the sequence diagram of the choreographies, using the relation
and constructs from the OWL ontology. Figure 3 depicts the objects properties used
in the ontology, linking the entities of the choreographies of anonymous account
holder services scheme.
Fig. 2. The equence diagram of the anonymous account holder services scheme
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Ontological Constructs to Create Money Laundering Schemes 7
Fig. 3. The objects represented in the anonymous account holder services scheme
5 Related Work
International organizations such as The Financial Action Task Force on Money
Laundering and National Drug Intelligence Center publish annual reports and
statistics of money laundering trends, including ongoing investigation of cyber
laundering cases [1], [2], [3], [4]. FF-POIROT [5], [6], [7], [8] is a project which
builds a detailed ontology of European law on the preventive practices of financial
fraud. The project is focused on sales tax fraud and online investment solicitation, and
it does not go into details of money laundering ontologies and schemes. Woda [9]
extensively describes money laundering techniques, but does not include any formal
specification or ontology definition of the MLS. Vanderlinden [12] produced a
comprehensive OWL ontology for financial systems, and covers legitimate
transactions. The emphasis of the work done is to produce the OWL, and no detail is
provided about the formal definition and methodological background.
Several publications study deficiencies of the languages used in the financial
industry, with a particular focus on the taxonomy and the specification of the
reporting languages. None of these studies cover the MLS with the exception of
Viveo [20] and SEPBLAC [21].
As part of their consulting work for large global financial enterprises, Viveo
released their product “QUALIFY-IT- XBRL Reporting” to provide bankers with
uniform message content (e.g. Fraud detection, Risk control, Money laundering)
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8 Murad Mehmet and Duminda Wijesekera
before anyone else can get it. The Viveo [20] product is tailored to the retail banking
industry and heavily depends on XBRL [13], and thus lacks the capability to be used
in web services transaction languages such as IFX [22]. The taxonomy project of
SEPBLAC [21] entitled “Telematic Reporting Project” automates the reporting
process of suspicious transactions, improve efficiency with fewer tasks and errors,
and ensure scalability. Chen et al. [13] assess different taxonomies used for financial
reporting in different countries, based on data samples selected from the Shanghai
Stock Exchange. They explore if the current XBRL can apply to real life scenarios,
and conclude the need to improve XBRL. Nicola et al. [14] developed an application-
oriented, domain-specific benchmark "Transaction Processing over XML”, which
simulates multi-user financial workloads with data based on the FIXML standard.
Carrillo et al. [15], [16] propose creating middleware to reduce the incompatibility
from multiple implementations of XBRL in an enterprise. This is based on their
developing an XBRL taxonomy for public institutions in Colombia.
Several efforts are underway in developing taxonomies for financial and
investment organizations. Progress is being made on preparing taxonomy for the
financial industry and investment organizations. Lara et al. [17] introduce a generic
translation process of XBRL taxonomies of investment funds into OWL ontologies.
They suggest that extensions to OWL are required to fulfill all the requirements of
financial information reporting. An improved XBRL can be achieved by adding
formal semantics. Castells et al. [18] developed an ontology-based platform that
provides the integration of contents and semantics in a knowledge base that provides a
conceptual view of low-level contents and semantic search facilities. Dui et al. [19]
demonstrate that configuration management for XML languages is more complicated
than traditional software engineering artifacts, they propose to evaluate XML by
using different versions of the Financial Products Markup Language (FpML). They
conclude that designers of FpML, and of many other complex XML languages, may
need to make changes to the language while retaining overall compatibility. None of
these works mentioned above analyze the semantics of money laundering, nor
propose a model that can used to detect the schemes within the available financial
reporting languages such as IFX [22], a language the financial industry heavily
depends upon for web-based transaction and business-to-business banking.
We have used Methontology [10] to develop this ontology because Protégé [11]
uses it.
6 Conclusions
In this paper we describe a preliminary OWL ontology to build money laundering
schemes. Our ontology provides components that can be used to construct MLS. Our
work creating money laundering ontologies is aimed at providing formal semantics
for financial transaction data, and facilitating detection of illegal financial schemes.
Currently, we are working on developing algorithms to detect each of the schemes
from a sequence of financial transaction records, where the objective is to capture and
identify the transactions that match constructs from our OWL ontology.
STIDS 2010 Proceedings Page 28 of 135
Ontological Constructs to Create Money Laundering Schemes 9
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