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• W146257493 abstract "This paper describes an e�ective method of outlier based predictors for health insurancefraud detection that identi�es suspicious behavior of health care providers.Fraud and abuse on medical claims became a major concern for health insurancecompanies last decades. Estimates made for the studied U.S. Medicaid health insuranceprogram is that up to 10% of the claims are fraudulent. Unsupervised datamining techniques such as outlier detection are suggested to be an e�ective predictorsfor fraud detection and should be used to support the initiations of audits. Amethod, based on comparative research, fraud cases and literature study has beenproposed. We evaluated the method, by applying the method in a real life case study,were behavioral metrics were designed and 14 analytic experiments were built usingoutlier detection. The analysis ran on dental claim data and showed promisingresults. The proposed methodology enabled successful identi�cation of fraudulentactivity in several cases; however linking these identi�ed incidents with irrefutablede jure fraud proved to be a di�cult process. From 17 top suspicions analyzed, wereported eventually 12 of those to o�cials, a precision rate of approximately 71%. Inthe two interviews conducted with Medicaid Fraud Experts, experiences were gainedon requirements for the design of the analytics and an e�ective implementation of themethod. We found that outlier based predictors are not likely to succeed as fraudclassi�cation technology, though it explored an important role as decision supportivetechnology for resource allocation of fraud audits." @default.
• W146257493 created "2016-06-24" @default.
• W146257493 creator A5008434191 @default.
• W146257493 date "2013-01-01" @default.
• W146257493 modified "2023-09-27" @default.
• W146257493 title "Outlier based predictors for health insurance frauddetection within U.S. medicaid" @default.
• W146257493 cites W110750246 @default.
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• W146257493 hasPublicationYear "2013" @default.
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