DeepAD

Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks

Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks - A lab we prepared for the 2nd KDD'19 Workshop on Anomaly Detection in Finance that will walk you through the detection of interpretable accounting anomalies using adversarial autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.

The workshop details are available via the KDD'19 workshop webpage under the following external link.

Running the Notebook

Reference

The lab is inspired by our work "Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks" by Marco Schreyer, Timur Sattarov, Christian Schulze, Bernd Reimer and Damian Borth.

The publication is available via arXiv under the following link: https://arxiv.org/abs/1908.00734 .

Questions?

Please feel free to get in touch by opening an issue report, submitting a pull request, or sending us an email.

Disclaimer

Opinions expressed in this work are solely those of the authors, and do not necessarily reflect the view of Deutsche Bundesbank or PricewaterhouseCoopers (PwC) International Ltd. and its network firms.