Variational Autoencoders and Wasserstein Generative Adversarial Networks for Improving the Anti-Money Laundering Process

Variational Autoencoders and Wasserstein Generative Adversarial Networks for Improving the Anti-Money Laundering Process

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 techniques. Studies show that supervised techniques often have the limitation of not adapting well to new irregular fraud patterns wh...

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Bibliographic Details
Main Authors: Zhiyuan Chen, Waleed Mahmoud Soliman, Amril Nazir, Mohammad Shorfuzzaman
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Anti-money laundering (AML)
autoencoders
anomaly detection
deep learning
fraud detection
GANs
Online Access:https://ieeexplore.ieee.org/document/9446893/

Internet

https://ieeexplore.ieee.org/document/9446893/

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