A Systematic Literature Review of Artificial Intelligence In Detecting Fraud in Health Insurance

Purpose: The purpose of this study is to find out how research on fraud in the world of health insurance is developing and how fraud can be detected. Methodology: The Systematic Literature Review (SLR) method was used by researchers to answer the Research Question (RQ) in this study Results: Research on fraud in health insurance has grown significantly over the past decade, several types of fraud often occur in health insurance such as Upcoding, fragmentation, repeat billing, fake reimbursement and the use of AI can help detect fraud Findings: Artificial Intelligence can help detect fraud so that it can help prevent losses that will be experienced by the company. Novelty: Watase Uake software is used in this study's systematic literature review approach to examine global health insurance fraud from a variety of reference sources. Originality: This study provides an empirical analysis of how the role of Artificial Intelligence in detecting fraud and the development of research related to fraud in the world of health insurance. Conclusion: Research on fraud in health insurance has grown significantly over the past decade, several types of fraud that often occur in health insurance such as upcoding, fake reimbursement, repeat billing and fragmentation. the use of AI can help detect fraud and It is hoped that future research can examine how AI can evolve in detecting fraud. Type of Paper : Empirical research Paper