Leveraging Big Data Characteristics for Enhanced Healthcare Fraud Detection

Article

Karami, A. and Jafari, F. 2025. Leveraging Big Data Characteristics for Enhanced Healthcare Fraud Detection. Cluster Computing. p. In press.
AuthorsKarami, A. and Jafari, F.
Abstract

This review explores the transformative potential of Big Data analytics in revolutionizing healthcare fraud detection. By examining the 5Vs (Volume, Variety, Velocity, Veracity, and Value), we illustrate how these dimensions can significantly enhance the accuracy and efficiency of fraud detection systems. This review not only analyzes how the 5Vs can be leveraged to improve fraud detection systems but also identifies existing research gaps and proposes future research directions. Furthermore, we address the integration of Artificial Intelligence (AI) and Big Data applications in healthcare fraud detection, highlighting their role in enhancing operational efficiencies and improving the overall quality of healthcare services. This paper aims to provide valuable insights into modernizing healthcare systems through Big Data technology, ultimately contributing to more efficient, reliable, and trustworthy healthcare services.

KeywordsBig Data, Healthcare Fraud, Healthcare Systems, 5Vs, Artificial Intelligence, Spark, Hadoop
JournalCluster Computing
Journal citationp. In press
ISSN1573-7543
1386-7857
Year2025
PublisherSpringer Nature
Accepted author manuscript
License
File Access Level
Anyone
Publication process dates
Accepted30 Dec 2024
Deposited10 Feb 2025
Copyright holder© 2025, The Authors
Additional information

The version of record of this article, first published in Cluster Computing, is available online at Publisher’s website: http://dx.doi.org/[in press]

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