A Customer Level Fraudulent Activity Detection Benchmark for Enhancing Machine Learning Model Research and Evaluation

arXiv:2404.14746v1 Announce Type: cross
Abstract: In the field of fraud detection, the availability of comprehensive and privacy-compliant datasets is crucial for advancing machine learning research and developing effective anti-fraud systems. Traditional datasets often focus on transaction-level information, which, while useful, overlooks the broader context of customer behavior patterns that are essential for detecting sophisticated fraud schemes. The scarcity of such data, primarily due to privacy concerns, significantly hampers the development and testing of predictive models that can operate effectively at …

anti-fraud arxiv availability benchmark context cs.ai cs.cr cs.lg customer datasets detection evaluation focus fraud fraud detection fraudulent information machine machine learning privacy research systems transaction