June 2013


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Lisa Nolder <[log in to unmask]>
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Lisa Nolder <[log in to unmask]>
Fri, 21 Jun 2013 10:10:51 -0400
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*_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.

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 
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.