A survey of multiple classifier systems as hybrid systems <s> New fraud trends <s> Fraud detection has been an important topic of research in the data mining community for the past two decades. Supervised, semi-supervised, and unsupervised approaches to fraud detection have been proposed for the telecommunications, credit, insurance and health-care industries. We describe a novel hybrid system for detecting fraud in the highly growing lotteries and online games of chance sector. While the objectives of fraudsters in this sector are not unique, money laundering and insider attack scenarios are much more prevalent in lotteries than in the previously studied sectors. The lack of labeled data for supervised classifier design, user anonymity, and the size of the data-sets are the other key factors differentiating the problem from previous studies, and are the key drivers behind the design and implementation decisions for the system described. The system employs online algorithms that optimally aggregate statistical information from raw data and applies a number of pre-specified checks against known fraud scenarios as well as novel clustering-based algorithms for outlier detection which are then fused together to produce alerts with high detection rates at acceptable false alarm levels. <s> BIB001 </s> A survey of multiple classifier systems as hybrid systems <s> New fraud trends <s> Service providing companies including telecommunication companies often receive substantial damage from customers' fraudulent behaviors. One of the common types of fraud is subscription fraud in which usage type is in contradiction with subscription type. This study aimed at identifying customers' subscription fraud by employing data mining techniques and adopting knowledge discovery process. To this end, a hybrid approach consisting of preprocessing, clustering, and classification phases was applied, and appropriate tools were employed commensurate to each phase. Specifically, in the clustering phase SOM and K-means were combined, and in the classification phase decision tree (C4.5), neural networks, and support vector machines as single classifiers and bagging, boosting, stacking, majority and consensus voting as ensembles were examined. In addition to using clustering to identify outlier cases, it was also possible - by defining new features - to maintain the results of clustering phase for the classification phase. This, in turn, contributed to better classification results. A real dataset provided by Telecommunication Company of Tehran was applied to demonstrate the effectiveness of the proposed method. The efficient use of synergy among these techniques significantly increased prediction accuracy. The performance of all single and ensemble classifiers is evaluated based on various metrics and compared by statistical tests. The results showed that support vector machines among single classifiers and boosted trees among all classifiers have the best performance in terms of various metrics. The research findings show that the proposed model has a high accuracy, and the resulting outcomes are significant both theoretically and practically. <s> BIB002 </s> A survey of multiple classifier systems as hybrid systems <s> New fraud trends <s> Prescription fraud is a main problem that causes substantial monetary loss in health care systems. We aimed to develop a model for detecting cases of prescription fraud and test it on real world data from a large multi-center medical prescription database. Conventionally, prescription fraud detection is conducted on random samples by human experts. However, the samples might be misleading and manual detection is costly. We propose a novel distance based on data-mining approach for assessing the fraudulent risk of prescriptions regarding cross-features. Final tests have been conducted on adult cardiac surgery database. The results obtained from experiments reveal that the proposed model works considerably well with a true positive rate of 77.4% and a false positive rate of 6% for the fraudulent medical prescriptions. The proposed model has the potential advantages including on-line risk prediction for prescription fraud, off-line analysis of high-risk prescriptions by human experts, and self-learning ability by regular updates of the integrative data sets. We conclude that incorporating such a system in health authorities, social security agencies and insurance companies would improve efficiency of internal review to ensure compliance with the law, and radically decrease human-expert auditing costs. <s> BIB003
Prescription fraud has been identified as a cause of substantial monetary loss in health care systems, it consists in the prescription of unnecessary medicaments. The research works need to real life data from a large multi-center medical prescription database BIB003 . The authors use a novel distance based on data-mining approach in a system which is capable of self-learning by regular updates. The system is designed to perform on-line risky prescription detection followed by off-line expert evaluation. A new brand of frauds appear in the online gaming and lotteries, i.e. intended for money laundering, whose detection is dealt with a mixture of supervised and unsupervised classifiers BIB001 . To be adaptive to fraudster evolving strategies, it is required to emphasize online learning, and online cluster detection. Fraud in telecommunication systems involving usage beyond contract specifications is dealt with in BIB002 by a preprocessing, clustering and classification pipeline. Clustering has been found to improve classification performance, and boosted trees are the best performing approach. The analysis of social networks by means of MCS may allow the detection of fraud in automobile insurance, consisting in staging traffic accidents and issuing fake insurance claims to their general or vehicle insurance company  .