A Survey of Intelligent Data Mining Methods to Combat Money Laundering and Establish Data Governance in the Banking Network
Subject Areas : Multimedia Processing, Communications Systems, Intelligent Systems
Hedayat Alimoradi dokoohi
1
,
Erfaneh Noroozi
2
*
1 - Department of Computer Engineering, Qeshm branch, Islamic Azad university, Qeshm
2 - Department of Computer Engineering, Qeshm branch, Islamic Azad university, Qeshm
Keywords: Anti-Money Laundering, Data Mining, Data Governance, Banking Transactions, Machine Learning,
Abstract :
Abstract
Introduction: Money laundering refers to the process of concealing the illicit origins of dirty money and presenting it as legitimate funds. Dirty money signifies sums obtained from criminal or illegal activities such as drug trafficking, human trafficking, bribery, and tax evasion. Money laundering can be defined as the process of cleansing dirty money. Detecting financial crimes has become a crucial priority for governments, and with the advancement of modern technologies and global communications, fraudulent methods are also significantly growing and evolving, leading to substantial damages to legitimate businesses. With the growth of electronic services in the financial sector and the increasing prevalence of non-face-to-face payment gateways worldwide, methods to combat money laundering have also experienced significant advancement. To the extent that nowadays, through intelligent software, tracking cryptocurrency transactions has become feasible.
Method: This study is a literature review that focuses on keywords such as Anti-Money Laundering, Money Laundering, suspicious transaction, machine learning, and Data Mining. The research involves searching for relevant articles related to these keywords, and suitable articles will be selected for further analysis.
Results: This study will present a classification based on approaches to combat money laundering in the electronic banking industry. Furthermore, it highlights the issues and challenges of these approaches in tackling money laundering within the electronic banking sector.
Discussion: This article presents a comprehensive analysis of the existing literature on combating money laundering, focusing on the use of machine learning, deep learning, data mining, and big data techniques. The reviews indicate that the lack of comprehensive data governance prevents the effective utilization of data from various institutions and centers for anti-money laundering purposes. Additionally, multiple graph algorithms, including centrality algorithms and embedded graph algorithms, have been employed to identify money laundering networks and organizations. Not utilizing these algorithms leaves a significant gap. Researchers can enhance the detection of illicit activities in anti-money laundering systems by employing community detection algorithms. Furthermore, the analyses reveal that unsupervised learning techniques can be more efficient in identifying money laundering due to the absence of labeled datasets and imbalanced data. Consequently, unsupervised methods can serve as a suitable tool for anti-money laundering systems.
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