Financial fraud negatively impacts organizational administrative processes, particularly
affecting owners and/or investors seeking to maximize their profits. Addressing this issue …

The rapid increase of fraud attacks on banking systems, financial institutions, and even
credit card holders demonstrate the high demand for enhanced fraud detection (FD) systems …

Fraud detection in banking systems is crucial for financial stability, customer protection,
reputation management, and regulatory compliance. Machine Learning (ML) is vital in …

This study addresses the critical challenge of data imbalance in credit card fraud detection
(CCFD), a significant impediment to accurate and reliable fraud prediction models. Fraud …

The ease with which mobile money is used to facilitate cross‐border payments presents a
global threat to law enforcement in the fight against money laundering and terrorist …

The proliferation of consumer electronic products has engendered a substantial surge in
data generation and information exchange, concurrently escalating the potential for security …

Real-time fraud prevention in digital banking involves employing advanced technologies
such as machine learning and cloud infrastructure to detect and mitigate fraudulent …

Imbalanced class distribution is a frequent and problematic issue in the domains of data
engineering and machine learning. Traditional classification algorithms or machine learning …

Background Breast cancer is a highly predominant destructive disease among women
characterised with varied tumour biology, molecular subgroups and diverse …

In recent years, with the rapid development of technology and the economy, online
transaction fraud has become more and more frequent. In the face of massive records of …