Fraud detection is a set of processes and analyses that allow businesses to identify and prevent unauthorized financial activity. This can include fraudulent credit card transactions, identify theft, cyber hacking, insurance scams, and more.
On the other hand, in data analysis, anomaly detection is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data.
So we can conclude that fraud detection is a type of anomaly detection!
In this project, I explore different methods for detecting credit card fraud transactions; including using the Catboost algorithm with undersampling & oversampling methods, and using an almost new approach, by using deep learning and autoencoder.