Healthcare Fraud Detection Techniques and Applications in Data Analytics
Keywords:
Healthcare fraud, data analytics, fraud detection, machine learning, anomaly detection, healthcare claims, predictive analytics, supervised learning, data miningAbstract
Healthcare fraud is a pervasive issue that impacts the financial sustainability of healthcare systems and reduces the quality of patient care. With the increasing digitization of healthcare data and the evolution of data analytics, novel techniques have emerged to detect and mitigate fraudulent activities in the healthcare sector. This paper reviews various fraud detection techniques, including data mining, machine learning, and statistical approaches, which leverage vast healthcare datasets to identify abnormal patterns in billing, service usage, and patient care. Furthermore, it discusses the application of predictive analytics, anomaly detection, and supervised learning algorithms in the context of healthcare fraud. A comprehensive understanding of these techniques offers significant potential to enhance fraud detection capabilities, reduce losses, and improve the overall efficiency of healthcare systems. This study highlights key techniques, common challenges, and future directions for advancing fraud detection in healthcare analytics.
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