The Bank Fraud (BAF) dataset suite, introduced at NeurIPS 2022, comprises 6 synthetic datasets for bank fraud detection. It's designed to be a comprehensive, realistic test bed with over 32 attributes. Our goal is to leverage this data to visualize trends and develop a machine learning model to predict bank fraud transactions.
We wanted to select a dataset with enough relevant detail to allow us to work on this project. After selecting a dataset that turned out to be a sample and given the feedback we received, we wanted to be more careful in our choice of dataset. We chose this dataset because it is straightforward , contains enough data and attributes, and is reliable.
Read more here
Read more here
Read more here