The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Intelligent Anti-Money Laundering Fraud Control Using Graph-Based Machine Learning Model for the Financial Domain
|
|
Author(s): Atif Usman (Department of Computer Science and Information Technology, Virtual University of Pakistan, Pakistan), Nasir Naveed (Department of Computer Science and Information Technology, Virtual University of Pakistan, Pakistan)and Saima Munawar (Department of Computer Science and Information Technology, Virtual University of Pakistan, Pakistan)
Copyright: 2023
Volume: 25
Issue: 1
Pages: 20
Source title:
Journal of Cases on Information Technology (JCIT)
DOI: 10.4018/JCIT.316665
Purchase
|
Abstract
Financial domains are suffering from organized fraudulent activities that are inflicting the world on a larger scale. Basel Anti-Money Laundering (AML) index enlists 146 countries, which are impacted by criminal acts like money laundering, and represents the country's risk level with a notable deteriorating trend over the last five years. Despite AML being a substantially focused area, only a fraction of such activities has been prevented. Because financial data related to this field is concealed, access is limited and protected by regulatory authorities. This paper aims to study a graph-based machine-learning model to identify fraudulent transactions using the financial domain's synthetic dataset (100K nodes, 5.3M edges). Graph-based machine learning with financial datasets resulted in promising 77-79% accuracy with a limited feature set. Even better results can be achieved by enriching the feature vector. This exploration further leads to pattern detection in the graph, which is a step toward AML detection.
Related Content
|
Zhi Chen, Jie Liu, Ying Wang.
© 2024.
19 pages.
|
|
Ping Zhang, Changrong Lv, Qingying Li, Bori Cong, Jian Liu.
© 2024.
19 pages.
|
|
Lai Xin, Liang Chang Sheng, Jiayu Feng, Hengyan Zhang.
© 2024.
17 pages.
|
|
Abida Ellahi, Yasir Javed, Mohammad Farooq Jan, Zaid Sultan.
© 2024.
20 pages.
|
|
Tongyue Feng, Jiexiang Xu, Zehan Zhou, Yilang Luo.
© 2024.
21 pages.
|
|
Toby Chau, Helen Lv Zhang, Yuyue Gui, Man Fai Lau.
© 2024.
13 pages.
|
|
Andrew J. Setterstrom, Jack T. Marchewka.
© 2024.
22 pages.
|
|
|