Enhancing Transaction Monitoring Controls to Detect Money Laundering Using Machine Learning

Authors: Oztas, B., Cetinkaya, D., Adedoyin, F. and Budka, M.

Journal: Proceedings - 2022 IEEE International Conference on e-Business Engineering, ICEBE 2022

Pages: 26-28

ISBN: 9781665492447

DOI: 10.1109/ICEBE55470.2022.00014


Money laundering has become a great economic problem with huge consequences on society and financial institutions in the last decade. Current anti-money laundering (AML) procedures within the industry are either inefficient due to criminals' increasingly sophisticated approaches or technological advancements. This paper provides an extended abstract to identify and analyze the machine learning methods to detect money laundering through transaction monitoring in the literature. Moreover, the paper identifies research gaps and based on the observed limitations, suggests future research directions and areas in need of improvements.

Source: Scopus