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Detection of Accounting Anomalies using Deep Autoencoder Neural Networks - A lab we prepared for NVIDIA's GPU Technology Conference 2018 that will walk you through the detection of accounting anomalies using deep autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.
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 J…
A series of interactive lab notebooks we prepared for the ACA course on "Internal Audit Knowledge Elements". The content of the series is based on Python, IPython Notebook, and PyTorch.
This repo has a notebook that I worked on for making a fraud detection model. The dataset was Highly imbalanced, so i used random undersampling to balance the data.
In this notebook we take a look at a relevant project that is frequently encountered by insurers: Fraud Detection. For this purpose we use a car data set from a public source and will show the necessary steps to establish an automated fraud detection.
A notebook about credit card fraud detection treated as anomaly detection via multivariative normal distribution. The dataset is highly imbalanced (0.17 % of positive class labels). Dataset source: https://www.kaggle.com/mlg-ulb/creditcardfraud