GCF-MLD: Integrated Approach for Money Laundering Detection Using Machine Learning and Graph Network Analysis

被引：1

作者：

Irshad, Faizan ^{[1
]}

Alkhalifah, Tamim ^{[2
]}

Alturise, Fahad ^{[3
]}

Khan, Yaser Daanial ^{[1
]}

机构：

[1] Univ Management & Technol, Sch Syst & Technol, Dept Comp Sci, Lahore 54770, Pakistan

[2] Qassim Univ, Coll Comp, Dept Comp Engn, Buraydah 52571, Saudi Arabia

[3] Qassim Univ, Coll Comp, Dept Cybersecur, Buraydah 52571, Saudi Arabia

来源：

IEEE ACCESS | 2024年 / 12卷

关键词：

Machine learning; Terrorism; Machine learning algorithms; Banking; Monitoring; Clustering algorithms; Support vector machines; Drugs; Risk management; Government; governmental factors; machine learning; financial services; money laundering; clustering methods;

D O I：

10.1109/ACCESS.2024.3510115

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Money laundering is a critical issue for financial institutions especially in developing countries and detecting such suspicious activities is a challenging task. The rule-based money laundering detection systems currently being used by the financial institutions rely on pre-defined rules that are often not able to keep up with rapidly changing tactics of money launderers. In this regard, machine learning based approaches have gained attention for detecting suspicious transactions related to money laundering. In this study, we propose a novel approach "Graph-Clustering Fusion for Money Laundering Detection (GCF-MLD)" combining graph network analysis with clustering methodology to identify suspicious accounts in real-world banking dataset. The results exhibit that the proposed model can be effectively used by the financial institutions to replace current rule-based techniques to significantly improve the efficiency and effectiveness of money laundering detection systems.

引用

页码：183961 / 183972

页数：12

共 47 条

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