Can the Generative Adversarial Network useful for Outlier detection and Outlier explanation in a high dimentional numerical data?
See this answer by Ian Goodfellow (the creator of GANs) to the same question at Quora:

There are definitely some papers about it, such as [1703.05921] Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery. I don’t work on anomaly detection so I haven’t read these papers and don’t know a lot about how they work.
It’s worth mentioning that there are also papers on anomaly detection using inputs that resemble adversarial examples: https://arxiv.org/pdf/1706.02690.pdf
For GANs, one thing to keep in mind is that the discriminator is not a generalized detector of weird things. It is trying to tell whether a sample came from the real data or one specific non-data distribution: the generator. Because of that, it seems like the discriminator would only be useful for anomaly detection if you think you can make your generator resemble the anomalies you expect to need to detect.

Nevertheless, neural networks in general are black boxes (i.e. not very interpretable), so they might detect outliers but you would not be able to tell what characterized them as so.