Through a cost matrix and a combination of classifiers, this work identifies the most economical model to perform the detection of suspected cases of fraud in a dataset of automobile claims. The experiments performed by this work show that working more deeply in sampled data in the training phase and test phase of each classifier is possible obtain a more economic model than other model present...

T he first time I came across the situation of an employee of a group physician practice being admitted to the hospital owned by that practice for a slip-and-fall accident on a monthly basis amazed me. I was horrified to discover that the employee had 30 to 40 active insurance certificates, including health insurance, accident insurance, workers’ compensation, and automobile personal injury pol...

Data mining has been increasing as one of the chief key features of many security initiatives. Often, used as a means for detection of fraud, assessing risk as well. Data mining involves the use of data analysis tools to discover unknown, valid patterns as well as relationships in large data sets. Decades have seen a massive growth in the use of credit cards as a transactional medium. Data mini...

Some automobile insurance companies use computerized auto-detection systems to expedite claims payment decisions for insured vehicles. Claims suspected of fraud are evaluated using empirical data from previously investigated claims. The main objective this manuscript is demonstrate a novel processing system and its potential in classification. approach was used develop machine learning-based se...

Phishing emails are messages designed to fool the recipient into handing over personal information, such as login names, passwords, credit card numbers, account credentials, social security numbers etc. Fraudulent emails harm their victims through loss of funds and identity theft. They also hurt Internet business, because people lose their trust in Internet transactions for fear that they will ...

This article explores the explicative capabilities of neural network classifiers with automatic relevance determination weight regularization, and reports the findings from applying these networks for personal injury protection automobile insurance claim fraud detection. The automatic relevance determination objective function scheme provides us with a way to determine which inputs are most inf...

BACKGROUND We aimed to identify the indicators of healthcare fraud and abuse in general physicians' drug prescription claims, and to identify a subset of general physicians that were more likely to have committed fraud and abuse. METHODS We applied data mining approach to a major health insurance organization dataset of private sector general physicians' prescription claims. It involved 5 ste...

In today’s scenario medical image forgery has turned out to be unsophisticated because of capable PCs, propelled editing softwares and high resolution capturing gadgets. Insurance fraud is a deliberate deception perpetrated against or by an insurance company agent for financial gain. The only downside that the health industry faced rise in number frauds. An endeavor prepared review current impr...

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...