Money laundering is a profound global problem. Nonetheless, there is little scientific
literature on statistical and machine learning methods for anti-money laundering. In this …

Our aim in this paper is to detect bank clients involved in suspicious activities related to
money laundering, using the graph of transactions of the bank. Although we have a labeled …

Anti-money laundering (AML) is a classical data mining problem in finance applications. As
well known, money laundering (ML) is critical to the effective operation of transnational and …

Intelligent assistants often struggle with the complexity of spatiotemporal models used for
understanding objects and environments. The construction and usage of such models …

Blockchain provides the unique and accountable channel for financial forensics by mining
its open and immutable transaction data. A recent surge has been witnessed by training …

As the largest blockchain platform that supports smart contracts, Ethereum has developed
with an incredible speed. Yet due to the anonymity of blockchain, the popularity of Ethereum …

Many real-world networks have been evolving and are finely modeled as temporal graphs
from the viewpoint of the graph theory. A temporal graph is informative and always contains …

In recent years, money laundering crimes on blockchain, especially on Ethereum, have
become increasingly rampant, resulting in substantial losses. The unique features of money …

With the development of blockchain technology, security concerns have become
increasingly prominent in recent years. Money laundering through blockchain has been …

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 …