Innovative Approaches to Prevent and Detect Medical Insurance Fraud: A Systematic Literature Review
Year: 2025
Type: review
Study design: Systematic Review
Abstract: Health insurance fraud is a significant issue that profoundly impacts the global healthcare sector, resulting in substantial financial losses. To address this problem, previous researchers have developed various methods and techniques to detect and prevent fraud in the realm of health insurance. This study presents a systematic literature review that analyzes 21 research articles focusing on innovative methods for preventing health insurance fraud. The primary objective of this research is to gain a comprehensive overview of the methods and approaches employed to detect and prevent fraudulent claims and activities in the field of health insurance. The study utilized a systematic search and screening method to analyze relevant articles. The selected research articles demonstrated various prevention efforts, including data analytics, machine learning, empowerment initiatives, and more. Each study was analyzed for its objectives, methodology, data, innovation success, and research findings. The results of the systematic review reveal numerous approaches and techniques used to prevent and detect health insurance fraud. In this context, machine learning and data analytics-based innovations have shown promising results in detecting fraudulent activities. Conversely, for prevention, blockchain technology can be implemented to enhance security systems. The research also highlights weaknesses and challenges in previous studies. The findings from this research can aid in the development of more effective fraud detection and prevention systems, contributing to a reduction in fraud rates and losses in the health insurance sector.
Authors: Chandra Zuli Tanjung, Anis Chariri
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4408919235