Counter Terrorism Finance by Detecting Money Laundering Hidden Networks Using Unsupervised Machine Learning Algorithm

Abstract

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.

Developing Anti Money Laundering Identification using Machine Learning Techniques

“…[9] conducted a study on money laundering detection using machine learningtechniques. The study used a variety of libraries and tools like Numpy, Matplotlib, SKLearn and Pandas packages to carry out the investigations.…”