Anti-Money Laundering system based on customer behavior

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Journal Title
Journal ISSN
Volume Title
Perustieteiden korkeakoulu | Master's thesis
Date
2019-08-19
Department
Major/Subject
Data Science
Mcode
SCI3095
Degree programme
Master's Programme in ICT Innovation
Language
en
Pages
68
Series
Abstract
Money Laundering is a big problem that concerns many governments and institutions. Vast amounts of money from illicit activities are laundered and go through the financial system. The criminals behind these crimes are still unpunished. In addition to this, the money launderers are improving their techniques to counteract the efforts of the legal institutions that are fighting against these crimes. Those who are working to uncover these crimes should also improve the way to detect money laundering and therefore, to identify suspicious transactions. That is the reason behind the interest of companies to use innovative techniques such as Artificial Intelligence (AI) to improve the accuracy of the current methods used in the industry. The objective of this thesis is to research and develop a system capable of flag changes in the behaviour of the contacts. There are many techniques in the field of AI, concretely in the area of machine learning that can be used to classify the activities in asset management companies. The algorithm used to analyse the behaviour was K-Means. This algorithm can group the data points for a given set of parameters. These features were carefully selected to characterise the behaviour of the investors, flagging the clients who move from one cluster to another. The change might be seen as suspicious behaviour of money laundering. An expert should review this flagged client, and decide whether it is necessary to report the authorities or not. The main reason to use AI is to reduce the number of false positives that are reported to the experts. The purpose is to reduce the manual work needed to identify suspicious transactions, allowing the experts to be free to focus on the main things of their jobs.
Description
Supervisor
Jung, Alexander
Thesis advisor
Jahkola, Olli
Keywords
machine learning, data science, money laundering, anti-money laundering, unsupervised machine learning
Other note
Citation