One method for detecting fraud is to check for suspicious changes in user behavior over time. This paper describes the automatic design of user profiling methods for the purpose of fraud detection, using a series of data mining and machine learning techniques. It uses a rule-learning program to uncover indicators of fraudulent behavior from a large database customer transactions. Then the indic...

Phishing attack is used to steal confidential information of user. Fraud websites appear similar to genuine websites with the logo and graphics of trusted website. Fraud Website Detection application aims to detect fraud websites using data mining techniques. This project provides intelligent solution to phishing attack. W3C standard defines characteristics which can be used to distinguish frau...

It is estimated that between $600 and $850 billion annually is lost to fraud, waste, and abuse in the US healthcare system, with $125 to $175 billion of this due to fraudulent activity (Kelley 2009). Medicaid, a state-run, federally-matched government program which accounts for roughly one-quarter of all healthcare expenses in the US, has been particularly susceptible targets for fraud in recen...

The amount of online transactions is growing these days to a large number. A big portion of these transactions contains credit card transactions. The growth of online fraud, on the other hand, is notable, which is generally a result of ease of access to edge technology for everyone. There has been research done on many models and methods for credit card fraud prevention and detection. Artificia...

Fraud detection is an increasingly important and difficult task in today’s technological environment. As consumers are putting more of their personal information online and transacting much more business over computers, the potential for losses from fraud is in the billions of dollars, not to mention the damage done by identity theft. This paper reviews the history of fraud detection at AT&T, o...

Addressing tax fraud has been taken increasingly seriously, but most attempts to uncover it involve the use of human fraud experts to identify and audit suspicious cases. To identify such cases, they come up with patterns which an IT team then implements to extract matching instances. The process, starting from the communication of the patterns to the developers, the debugging of the implemente...

Given the widespread and deep economic-social consequences of accounting fraud in micro and macro levels of society and the effectiveness of whistleblowing in their discovery and the effect of demographic characteristics on moral behavior as whistleblowing, this study aimed to examine the whistleblowing intentions for accounting fraud including account manipulation and embezzlement based on dem...

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 ...

Academic research, government inquiries, and press accounts show extensive mortgage fraud during the housing boom of the mid-2000s. We explore a particular type of mortgage fraud: the overstatement of income on mortgage applications. We define “income overstatement” in a zip code as the growth in income reported on home-purchase mortgage applications minus the average IRS-reported income growth...

Online auction fraud is the most common complaint according to Internet Crime and Complaint Center. Despite that, there are not many published empirical studies about fraud occurrence in online auction sites, and the existing ones mostly target eBay. This lack of research is even worse in Latin American countries. In this paper we present the results of an exploratory research on fraud occurren...