A survey of identification and forecasting of healthcare fraud through machine learning

Rasnarayan Chaurasiya, Kirti Jain and Vikas Chaurasia

International Journal of Complexity in Applied Science and Technology, 2026, vol. 2, issue 2, 128-152

Abstract: Healthcare fraud is a widespread problem that costs billions of dollars annually and has significant societal and financial consequences. Patients may face increased premiums and out-of-pocket expenses as a result of this because it compromises the integrity of healthcare systems. This survey analyses the ongoing philosophies for medical services misrepresentation recognition and expectation, the difficulties confronted, and arising patterns in this basic field. Patients and providers alike are harmed by healthcare fraud, which can lead to decreased quality and increased costs. The vast, complex, and ever-evolving nature of healthcare data has proven to be too much for traditional fraud detection methods. Improved fraud detection and prediction in the healthcare industry may be possible with the help of machine learning. This review looks at how various ML techniques are used to find healthcare fraud, talks about the problems and opportunities, and gives ideas for where research and practice should go in the future.

Keywords: healthcare fraud; forecasting; machine learning; ML; fraud detection; premiums and expenses. (search for similar items in EconPapers)
Date: 2026
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