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 the customer level.
Addressing this gap, our study introduces a benchmark that contains structured
datasets specifically designed for customer-level fraud detection. The
benchmark not only adheres to strict privacy guidelines to ensure user
confidentiality but also provides a rich source of information by encapsulating
customer-centric features. We have developed the benchmark that allows for the
comprehensive evaluation of various machine learning models, facilitating a
deeper understanding of their strengths and weaknesses in predicting fraudulent
activities. Through this work, we seek to bridge the existing gap in data
availability, offering researchers and practitioners a valuable resource that
empowers the development of next-generation fraud detection techniques.

为了推动机器学习研究并开发有效的反欺诈系统，欺诈检测领域需要全面且符合隐私规范的数据集。传统数据集往往关注交易级别的信息，忽视了对于检测复杂欺诈方案至关重要的更广泛的客户行为模式的背景。在这项研究中，我们引入了一个针对客户级欺诈检测的结构化数据集基准，不仅遵守严格的隐私指南以确保用户机密性，还通过封装以客户为中心的特征提供了丰富的信息源。通过这项工作，我们旨在弥补现有数据可用性方面的差距，为研究人员和实践者提供有价值的资源，推动下一代欺诈检测技术的发展。