Fraud is a serious problem that costs the worldwide economy trillions of dollars annually. However, fraud detection is di cult as perpetrators actively attempt to masquerade their actions, among typically overwhelming large volumes of, legitimate activity. In this paper, we investigate the fraud detection problem and examine how learning classi er systems can be applied to it. We describe the c...

Clustering analysis and Datamining methodologies were applied to the problem of identifying illegal and fraud transactions. The researchers independently developed model and software using data provided by a bank and using Rapidminer modeling tool. The research objectives are to propose dynamic model and mechanism to cover fraud detection system limitations. KDA model as proposed model can dete...

In this paper we consider the application of a naïve Bayes model for the evaluation of fraud risk connected with government agencies. This model applies probabilistic classifiers to support a generic risk assessment model, allowing for more efficient and effective use of resources for fraud detection in government transactions, and assisting audit agencies in transitioning from reactive to proa...

Health care has become a major expenditure in the US since 1980. Both the size of the health care sector and the enormous volume of money involved make it an attractive fraud target. Therefore, effective fraud detection is important for reducing the cost of health care services. In order to achieve more effective fraud detection, many researchers have attempted to develop sophisticated antifrau...

Fraud and abuse have led to significant additional expense in the health care system of the United States. This paper aims to provide a comprehensive survey of the statistical methods applied to health care fraud detection, with focuses on classifying fraudulent behaviors, identifying the major sources and characteristics of the data based on which fraud detection has been conducted, discussing...

As many countries in the world start to experience population aging, there are an increasing number of people relying on medical insurance to access healthcare resources. Medical insurance frauds are causing billions of dollars in losses for public healthcare funds. The detection of medical insurance frauds is an important and difficult challenge for the artificial intelligence (AI) research co...

Some models of provider behavior predict that physicians, like other experts, may respond dysfunctionally to financial incentives by recommending unnecessary treatment. We empirically test this relationship using data from inpatient hospitalizations surrounding a 2003 Colorado auto insurance reform. The reform shifted a large fraction of auto injury patients from coverage through auto insurers ...

The purpose of this study was to analyze elements in the fraud triangle clarify possibility financial statement consumer goods industry subsector. population is all manufacturing companies sub-sector measured by M-score model. total sample used 36, consisting 9 companies. method research quantitative with primary data type. analytical tool logistic regression analysis using SPSS v20. results in...