researchers and practitioners doing work in these three related areas. Risk management, fraud detection, and intrusion detection all involve monitoring the behavior of populations of users (or their accounts) to estimate, plan for, avoid, or detect risk. In his paper, Til Schuermann (Oliver, Wyman, and Company) categorizes risk into market risk, credit risk, and operating risk (or fraud). Simil...

Insurance fraud, which adds an estimated $85 billion per year to the total insurance bill in the United States, is an extremely serious problem for consumers, regulators, and insurance companies. This paper analyzes the effects of state legislation and market conditions on automobile insurance fraud from 1988 to 1999, a period exhibiting a substantial increase in the enactment of antifraud legi...

Health smart card is like ATM card which provide cash benefits to patient through insurance company for hospital and medical benefits without expending money from the patient at the time of need. But now-a-days, fraud is done using the health smart card as few patients does not know the real cost of the treatment, so doctor take more payment and benefits through health smart card and generate f...

Given the current global economic turmoil and contracting economies, financial crime is on the rise. The use of analytical techniques to protect financial institutions against fraudulent activity has seen varying degrees of success in the past. Recent advances include the use of rule-based fraud detection flags, exception reporting, third-party data searching, profiling, and fraud scorecards ba...

The paper presents application of data mining techniques to fraud analysis. We present some classification and prediction data mining techniques which we consider important to handle fraud detection. There exist a number of data mining algorithms and we present statistics-based algorithm, decision tree-based algorithm and rule-based algorithm. We present Bayesian classification model to detect ...

The paper presents application of data mining techniques to fraud analysis. We present some classification and prediction data mining techniques which we consider important to handle fraud detection. There exist a number of data mining algorithms and we present statistics-based algorithm, decision treebased algorithm and rule-based algorithm. We present Bayesian classification model to detect f...

Frauds in insurance industry are one of the major sources of operational risk of insurance companies and constitute a significant portion of their losses. Every reasonable company on the market aims for improving their processes of uncovering frauds and invests their resources to reduce them. This article is addressing fraud management area from the view of extension of existing Business Intell...

Claims fraud is an increasingly vexing problem confronting the insurance industry. In this empirical study, we apply Kohonen's Self-Organizing Feature Map to classify automobile bodily injury (BI) claims by the degree of fraud suspicion. Feed forward neural networks and a back propagation algorithm are used to investigate the validity of the Feature Map approach. Comparative experiments illustr...

Inappropriate payments by insurance organizations or third party payers occur because of errors, abuse and fraud. The scale of this problem is large enough to make it a priority issue for health systems. Traditional methods of detecting health care fraud and abuse are time-consuming and inefficient. Combining automated methods and statistical knowledge lead to the emergence of a new interdiscip...