Financial fraud, considered as deceptive tactics for gaining financial benefits, has recently
become a widespread menace in companies and organizations. Conventional techniques …

Machine Learning and Deep Learning methods are widely adopted across financial
domains to support trading activities, mobile banking, payments, and making customer credit …

Due to the popularity and user friendliness of the Internet, numbers of users of online social
networks (OSNs) and social media have grown significantly. However, globally utilised …

Purpose This paper aims to understand and document the state of the art in the anti-money
laundering (AML) systems literature. Design/methodology/approach A systematic literature …

Money laundering (ML) poses a severe threat to financial stability and social security.
Various money laundering detection methods have emerged in the past two decades …

The emergence of online social network invokes social actors to share their personal
information digitally. Moreover, it provides the facility to maintain their links with people of …

Illicit activity in cryptocurrency has increased dramatically over the years. Bitcoin mechanics
allow for users to mask their identity through obfuscation techniques. Much research has …

Money laundering identification (MLI) is a challenging task for financial AI research and
application due to its massive transaction volume, label sparseness, and label bias. Most of …

Identifying illicit behavior in the Bitcoin network is a well explored topic. The methods
proposed over time have generated great insights into the deanonymization of the Bitcoin …

Money laundering presents a pervasive challenge, burdening society by financing illegal
activities. To more effectively combat and detect money laundering, the use of network …