GitiHubi/deepAD
Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks - A lab we prepared for the 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.
This project helps finance professionals identify unusual patterns in accounting data that could signal fraud or errors. It takes in your financial transaction records and highlights suspicious entries that might be worth investigating further. It's designed for forensic accountants, auditors, and financial analysts who need to spot anomalies in large datasets.
No commits in the last 6 months.
Use this if you need to detect subtle, interpretable anomalies within your financial accounting data using advanced machine learning.
Not ideal if you're looking for a simple, off-the-shelf fraud detection software without diving into technical details or if your data isn't structured for financial analysis.
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Jupyter Notebook
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Last pushed
Aug 28, 2019
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