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Title:
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COUNTER TERRORISM FINANCE BY DETECTING
MONEY LAUNDERING HIDDEN NETWORKS USING
UNSUPERVISED MACHINE LEARNING ALGORITHM |
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Author(s):
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Amr Ehab Muhammed Shokry, Mohammed Abo Rizka, and Nevine Makram Labib |
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ISBN:
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978-989-8704-19-1 |
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Editors:
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Piet Kommers and Guo Chao Peng |
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Year:
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2020 |
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Edition:
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Single |
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Keywords:
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Terrorism Financing, Money Laundering, Anti-Money Laundering, Machine Learning |
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Type:
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Full |
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First Page:
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89 |
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Last Page:
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97 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Today's most immediate threat to address is terrorism. Terror organizations use illegal methods to raise their fund, such
as scamming banks, fraud, donation, ransom and oil. This illicit money needs be laundered to be used within legal
economy through financial institutions (FI). This paper is a complementary to our previous research. And it's proposes an
unsupervised machine learning technique for detecting Money Laundering hidden patterns, groups and transactions in a
timely manner to counter terrorism finance. Two different algorithms were implemented and performance was measured,
compared and summarized. The preliminary experimental results show the effectiveness of the proposed technique.
Domain experts confirm that the proposed method has produced efficient accurate results by identifying and detecting
similarities, hidden patterns, grouping across all transactions and all the suspicious accounts involved. |
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