The increasing digitalization of banking services has led to a surge in financial fraud, necessitating advanced detection ...

Machine learning plays a critical role in fraud detection ... article, "Unsupervised Label Generation for Severely Imbalanced Fraud Data," is an updated version of the researchers' previous work, ...

Fraud detection ... likelihood of detection. Fraudsters are continuously adapting their tactics, but machine learning systems evolve in response, maintaining their effectiveness over time. Future ...

Department of EECS, University of California at Berkeley, 485 Soda Hall, Berkeley CA 94720-1776, USA Previously at: Institute for Adaptive and Neural Computation, University of Edinburgh, UK.

E-commerce fraud has evolved into a sophisticated challenge, with fraudulent activities escalating in complexity and scale. In response, machine learning ... supervised learning, unsupervised learning ...

Particularly, in scenarios with limited labeled data, traditional single-view learning methods often struggle to handle these complex issues. To address this, this article proposes an unsupervised ...

Department of Chemistry, Sharif University of Technology, Tehran 11155-9516, Iran Center for Nanoscience and Nanotechnology, Institute for Convergence Science & Technology, Sharif University of ...

Its behavioral analytics and machine learning feature verifies customer identities, flags high-risk transactions, and automates fraud detection. It’s clear how payment fraud affects consumers ...

Glancy Prongay & Murray LLP (“GPM”) announces that it has filed a class action lawsuit in the United States District Court for the Southern District of New York, captioned Frankin v.