Anomaly and Fraud Detection Techniques

A collection of Jupyter Notebooks to show different ways to implement anomaly and fraud detection. This example workshop will use a dataset from Kaggle that is used for Credit Card Fraud detection.

Dataset:

The dataset used in this series of examples is publicly available at https://www.kaggle.com/mlg-ulb/creditcardfraud

The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on http://mlg.ulb.ac.be/BruFence and http://mlg.ulb.ac.be/ARTML

Methods:

- Supervised Methods (Decision Trees Classification):

This method uses a classification algorithm (XGBoost) given the fact that the dataset has both Fraud and Non-Fraud transactions.

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- Unsupervised Methods:

1- Autoencoders

2- Random Cut Forest

3- Isolation Forest

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