FDB: Fraud Dataset Benchmark

The Fraud Dataset Benchmark (FDB) is a compilation of publicly available datasets relevant to fraud detection (arXiv link). The FDB aims to cover a wide variety of fraud detection tasks, ranging from card not present transaction fraud, bot attacks, malicious traffic, loan risk and content moderation. The Python based data loaders from FDB provide dataset loading, standardized train-test splits and performance evaluation metrics. The goal of our work is to provide researchers working in the field of fraud and abuse detection a standardized set of benchmarking datasets and evaluation tools for their experiments. Using FDB tools we demonstrate several applications of FDB that are of broad interest for fraud detection, including feature engineering, comparison of supervised learning algorithms, label noise removal, class-imbalance treatment and semi-supervised learning.