*_Notice and Invitation_*
Oral Defense of Doctoral Dissertation
The Volgenau School of Engineering, George Mason University
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.
Dr. Duminda Wijesekera, Chair
Dr. Edgar H. Sibley
Dr. David A. Schum
Dr. Jeremy E. Allnutt
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
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
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