Abstract
The healthcare industry has become a very important pillar in modern society but has witnessed an increase in fraudulent activities. Traditional fraud detection methods have been used to detect potential fraud, but in certain cases, they have been insufficient and time-consuming. Data mining which has emerged as a very important process in knowledge discovery has been successfully applied in the health insurance claims fraud detection. We performed an analysis of studies that used data mining techniques for detecting healthcare fraud and abuse using the supervised and unsupervised data mining methods. Each of these methods has their own strengths and weaknesses. This article attempts to highlight these areas, along with trends and propose recommendations relevant for deployment. We identified the need for the use of more computationally efficient models that can easily adapt and identify the novel fraud patterns generated by the perpetrators of healthcare claims fraud.
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Obodoekwe, N., van der Haar, D.T. (2018). A Critical Analysis of the Application of Data Mining Methods to Detect Healthcare Claim Fraud in the Medical Billing Process. In: Boudriga, N., Alouini, MS., Rekhis, S., Sabir, E., Pollin, S. (eds) Ubiquitous Networking. UNet 2018. Lecture Notes in Computer Science(), vol 11277. Springer, Cham. https://doi.org/10.1007/978-3-030-02849-7_29
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DOI: https://doi.org/10.1007/978-3-030-02849-7_29
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