Fraud-Detection im Gesundheitswesen: Data-Mining zur Aufdeckung von Abrechnungsbetrug

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• 作者：Raoul Könsgen ; Steffen Stock ; Mario Schaarschmidt
• 关键词：SchlüsselwörterGesundheitswesen ; Data ; Mining ; Fraud ; Fraud Detection ; Assoziationsanalyse
• 刊名：HMD Praxis der Wirtschaftsinformatik
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：54
• 期：1
• 页码：146-155
• 全文大小：
• 刊物类别：Computer Science, general; Economics/Management Science, general;
• 刊物主题：Computer Science, general; IT in Business;
• 出版者：Springer Fachmedien Wiesbaden
• ISSN：2198-2775
• 卷排序：54

文摘

The health care fraud costs in Germany totaled 21 bn euro in 2014 (Statistisches Bundesamt 2016) and the numbers continue to grow. Rising health care contributions and a new view with regard to basic medical care are the consequences. From the the German Federal Criminal Office point of view, accounting fraud is a socially harmful conduct. At the same time, health insurances face the challenge to reduce their costs. The accounting data from medical doctors are subjected to continuous change and become increasingly complex. Due to this, it becomes more difficult to extract useful information out of this enormous volume of data. Data-mining is part of the analysis methods of business analytics and is used to pattern recognition. The intention of this paper a new contribution to implementation possibilities of a fraud-detection-system that is based on data-mining-association-analysis.