Online auction sites are a target for fraud due to their anonymity, number of potential targets and low likelihood of identification. Researchers have developed methods for identifying fraud. However, these methods must be individually tailored for each type of fraud, since each differs in the characteristics important for their identification. Using supervised learning methods, it is possible ...

Fraud is a significant issue for insurance companies, generating much interest in machine learning solutions. Although supervised fraud detection has long been research focus, unsupervised rarely studied this context, and there remains insufficient evidence to guide the choice between these branches of detection. Accordingly, study evaluates using proprietary claim data. Furthermore, we conduct...

a r t i c l e i n f o Occupational fraud is a $652 billion problem to which disgruntled employees are a major contributor. Much security research addresses reducing fraud opportunity and increasing fraud detection, but detecting motivational factors like employee disgruntlement is less studied. The Sarbanes–Oxley Act requires that companies archive email, creating an untapped resource for deter...

In this article, we derive an audit risk formula for a simple situation. This formula closely resembles the SAS 47 model when we assume that no material misstatement due to fraud exists. A simple case illustrates how the risk of material misstatement due to management fraud impacts audit risk and how performing special audit procedures to detect such irregularities can decrease overall audit ri...