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

TL;DR

This paper introduces a privacy-compliant customer-level fraud detection benchmark dataset to improve machine learning research and evaluation in detecting sophisticated fraud schemes.

Contribution

It provides a structured, privacy-aware dataset for customer-level fraud detection, enabling more comprehensive model evaluation and development.

Findings

Benchmark facilitates evaluation of diverse machine learning models.

Customer-level features improve fraud detection accuracy.

Dataset supports privacy-preserving research in fraud detection.

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 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.