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Implementation of data mining techniques in upcoding fraud detection in the monetary domains

机译:数据挖掘技术在货币领域上的欺诈检测升级编码中的实现

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Fraud detection is a scenario applicable to many industries such as banking and financial sectors, insurance, healthcare, government agencies and law enforcement and more. There has been a drastic increase in recent years, pushing fraud detection more important than ever. Hundreds of millions of dollars are lost to fraud every year. Upcoding fraud is one such fraud in which a service provider acquires additional financial gain by coding a service by upgrading it even though the lesser service has been performed. Incorporating artificial intelligence with data mining and statistics help to anticipate and detect these frauds and minimize costs. Using sophisticated data mining tools, millions of transcations can be searched to spot patterns and detect fraudulent transactions. This paper gives an insight into the various datamining tools which are efficient in detecting upcoding frauds especially in the healthcare insurance sector in India.
机译:欺诈检测是一种适用于许多行业的方案,例如银行和金融部门,保险,医疗保健,政府机构和执法机构等等。近年来,这种情况急剧增加,这使得欺诈检测比以往任何时候都更加重要。每年因欺诈而损失的亿万美元。上编欺诈是一种这样的欺诈,其中即使已经执行了较少的服务,服务提供商也可以通过升级服务来对服务进行编码,从而获得额外的财务收益。将人工智能与数据挖掘和统计数据相结合有助于预测和检测这些欺诈行为,并最大程度地降低成本。使用复杂的数据挖掘工具,可以搜索数百万笔交易以发现模式并检测欺诈性交易。本文深入分析了各种数据挖掘工具,这些工具可以有效地检测上编码欺诈,尤其是在印度的医疗保险领域。

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