HIGHLIGHTS
- who: Variational Autoencoders et al. from the School of Computer Science, University of Nottingham Malaysia, Semenyih, Malaysia have published the research work: Variational Autoencoders and Wasserstein Generative Adversarial Networks for Improving the Anti-Money Laundering Process, in the Journal: (JOURNAL)
- what: The authors propose a suite of unsupervised deep learning techniques to implement an anti-money laundering fraud detection system to resolve this limitation. The authors design and implement deep learning models with promising results in terms of the FPR, RFT, and AUC for fraud detection. For the first time, the authors demonstrate the applicability . . .
If you want to have access to all the content you need to log in!
Thanks :)
If you don't have an account, you can create one here.