Graph-based anomaly detection (GBAD) approaches are among the most popular
techniques used to analyze connectivity patterns in communication networks and identify …

The high volume of money involved in e-commerce transactions draws the attention of
fraudsters, which makes fraud prevention and detection techniques of high importance …

Advanced fraud detection systems leverage the digital traces from (credit-card) transactions
to detect fraudulent activity in future transactions. Recent research in fraud detection has …

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 …

The rise of digital payments has caused consequential changes in the financial crime
landscape. As a result, traditional fraud detection approaches such as rule-based systems …

There has been much recent work on fraud and Anti Money Laundering (AML) detection
using machine learning techniques. However, most algorithms are based on supervised …

In an era where financial fraud tactics evolve rapidly, traditional fraud detection systems
struggle to keep up with the sophisticated schemes emerging daily. This article explores …

In recent years, the unprecedented growth in digital payments fueled consequential
changes in fraud and financial crimes. In this new landscape, traditional fraud detection …

Collaboration between financial institutions helps to improve detection of fraud. However,
exchange of relevant data between these institutions is often not possible due to privacy …

The vigorous development of e-commerce breeds cybercrime. Online payment fraud
detection, a challenge faced by online service, plays an important role in rapidly evolving e …