Process-based fraud (PBF) is fraud caused by deviation from a business process model. Some studies have proposed methods for PBF detection; however, these are still not able to fully detect the occurrence of fraud. In this context, we propose a new method of PBF detection which carries out the behavior of the originators (users who perform events) to adjust the levels of fraud occured in the ev...

Rapid growth of modern technologies such as internet and mobile computing are bringing dramatically increased e-commerce payments, as well as the explosion in transaction fraud. Meanwhile, fraudsters are continually refining their tricks, making rule-based fraud detection systems difficult to handle the ever-changing fraud patterns. Many data mining and artificial intelligence methods have been...

It is important for search and pay-per-click engines to penetration test their click fraud detection systems, in order to find potential vulnerabilities and correct them before fraudsters can exploit them. In this paper, we describe: (1) some goals and desirable qualities of a click fraud penetration testing system, based on our experience, and (2) our experiences with the challenges of buildin...

Most insurance companies publish few data on the occurrence and detection of insurance fraud. This stands in contrast to the previous literature on costly state verification, which has shown that it is optimal to commit to an auditing strategy, as the credible announcement of thoroughly auditing claim reports might act as a powerful deterrent. We show that uncertainty about fraud detection can ...

We propose a novel approach for credit card fraud detection, which combines evidences from current as well as past behavior. The fraud detection system (FDS) consists of four components, namely, rule-based filter, Dempster–Shafer adder, transaction history database and Bayesian learner. In the rule-based component, we determine the suspicion level of each incoming transaction based on the exten...

In September 1995, the Congressional O ce of Technology Assessment completed a study of the potential for AI technologies to detect money laundering by screening wire transfers. The study, conducted at the request of the Senate Permanent Subcommittee on Investigations, evaluates the technical and public policy implications of widespread use of AI technologies by the Federal government for fraud...

This paper proposes a method for fraud detection in automated selection exams, using knowledge engineering tools for identifying groups of answers with a strong indication of fraud, based on probabilistic evidence. Founded on an analysis of the wrong answers of the various candidates, the proposed method enables identification of suspicions, and evidence of fraud attempts through finding candid...