Notice and Invitation Oral Defense of Doctoral Dissertation The Volgenau School of Engineering, George Mason University
Murad Mehmet Bachelor of Science, Bogazici University, 1998 Master of Science, George Mason University, 2004
Money Laundering Evolution Detection, Transaction Scoring, and Prevention Framework
Wednesday,
July, 03, 2013, 10:00
AM
Nguyen
Engineering Building, RM 4801
All are
invited to attend.
Committee
Dr. Duminda Wijesekera, Chair
Dr. Edgar H. Sibley
Dr. David A. Schum
Dr. Jeremy
E. Allnutt
Abstract
Money
laundering is a
major and ongoing global issue that has not been addressed with
a dynamic
approach by authorities using multiple systems. Made powerful by
modern tools
and resources available to them, money launderers are adopting
more
sophisticated schemes, spanning across many countries, to avoid
being detected
by anti-money laundering systems. Consequently, money laundering
detection and
prevention techniques must be multi-layered, multi-method, and
multi-component
to be ahead of the evolving laundering schemes. Handling such a
multifaceted
problem involves a large amount of unstructured, semi-structured
and
transactional data that stream at speeds requiring a high level
of analytical
processing to discover unraveling business-complexities, and
discover
deliberately concealed relationships.
Therefore,
I developed
the money laundering evolution detection framework (MLEDF) to
capture the trail
of the dynamic and evolving schemes. My framework uses sequence
matching,
case-based analysis, social network analysis, and more
importantly, complex
event processing to link the fraud trails. My system capture a
single scheme as
an event in a trail in “real-time”, and then using detection
algorithms,
associate the captured event with other ongoing events.
A
comprehensive Anti
Money Laundering system must incorporate a risk modeling that
calculates the
dynamic attributes of transactional relationships and the
potential social
relationships among seemingly unrelated entities from a
financial perspective. Therefore,
I developed an industry-wide system to assign a risk score for
any transaction
being a part of a larger money laundering scheme. This score
should be valid
across every financial domain, continuously updated, and it is
not specific to
the evaluating financial institution.
Additionally,
I developed
a transaction scoring exchange and money laundering prevention
framework that
uses a transaction messaging system and assigns scores to the
transactions,
where the score is derived from the dynamics risk of the
transaction and the
statically computed risk score. The transaction score is
correlated to the
static and dynamic risk scores, in order to identify
transactions score
pertaining to money laundering, and to prevent transaction
sequences from being
executed. The transaction score uses, dynamic risk score
obtained from the
analytics of results of the real-time detection algorithms, to
produce valid
results.
The
recommended money
laundering prevention system relies upon the finding of an
accurate detection
system, supported by dynamic risk modeling systems for
transaction scoring. My
prevention framework includes a protocol
to exchange the information among the framework participants,
and it
incorporates two levels of cooperation and information sharing.
The
developed three
level systems in this study consist of multi-levels and
multi-components, and
they can be easily incorporated within existing structure
financial
institutions. My system allows financial investigators to
overcome the long
processes and time-consuming characteristics of their
investigations, to
prevent money laundering schemes, or at least be aware of such
schemes in their
early stages.
I
validated the accuracy
of calculating the money laundering evolution detection
framework, dynamic risk
scoring, and transactions scoring framework using a multi-phase
test methodology.
My test used data generated from real-life cases, and
extrapolated to generate
more varying scenarios of money laundering evolution and risk
data from
real-life schemes and patterns generator that I implemented.
A
copy of this doctoral
dissertation will be on reserve at the Johnson Center Library.