The utilization of text classification techniques has demonstrated great promise in the field of
detecting consumer fraud based on consumer reviews. However, persistent challenges …

Traditional consumer fraud detection usually relies on the relevant regulatory authorities to
conduct inspections through sampling. This would be labor-intensive and inefficient. To …

Standardized datasets and benchmarks have spurred innovations in computer vision,
natural language processing, multi-modal and tabular settings. We note that, as compared to …

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 …

Online fraud is the well-known dark side of the modern Internet. Unsupervised fraud
detection algorithms are widely used to address this problem. However, selecting features …

Consumer fraud is a significant problem that requires accurate and prompt detection.
However, existing approaches such as periodic government inspections and consumer …

Fraud detection is a critical task in various industries, aiming to identify and prevent
fraudulent activities. In this report, we explore different machine learning models and the …

Transaction fraud detection is an essential topic in financial research, protecting customers
and financial institutions from suffering significant financial losses. The existing ensemble …

With the explosive growth of the e-commerce industry, detecting online transaction fraud in
real-world applications has become increasingly important to the development of e …

Fraud review detection is a hot research topic in recent years. The Cold-start is a particularly
new but significant problem referring to the failure of a detection system to recognize the …