Naof F Saleem Al-Ansary
The findings indicate that FWA imposes substantial direct and indirect economic costs on health insurance systems by increasing financial losses, reducing operational efficiency, weakening accountability, and threatening long-term fund sustainability. Evidence suggests that effective mitigation requires an integrated approach combining preventive monitoring, robust governance, legislative enforcement, whistleblower protections, and advanced analytical technologies. Artificial intelligence, machine learning, blockchain, and automated auditing systems demonstrated promising performance in improving fraud detection under experimental conditions; however, evidence supporting their effectiveness in large-scale operational healthcare settings remains limited, and implementation challenges related to scalability, interoperability, privacy, and governance should be considered. Publication trends indicate increasing international research interest in FWA, although evidence remains concentrated in a limited number of countries.
PURPOSE: This study examines the economic impact of fraud, waste, and abuse (FWA) in health insurance systems and synthesizes evidence on governance, legislative, regulatory, and technology-based interventions aimed at improving financial sustainability. It further evaluates the implications of these interventions for healthcare system resilience and identifies research gaps to inform future policy and practice.
METHOD: This study employed a systematic literature search followed by a narrative synthesis to examine the economic impact of health insurance FWA. A comprehensive search of Scopus, Web of Science, MEDLINE, Embase, PubMed, OpenAlex, and Dimensions was conducted for studies published up to 24 November 2025, complemented by backward and forward citation tracking using the Inciteful.xyz platform. Following screening and eligibility assessment, 30 studies were included in the final narrative synthesis.
RESULTS: The findings indicate that FWA imposes substantial direct and indirect economic costs on health insurance systems by increasing financial losses, reducing operational efficiency, weakening accountability, and threatening long-term fund sustainability. Evidence suggests that effective mitigation requires an integrated approach combining preventive monitoring, robust governance, legislative enforcement, whistleblower protections, and advanced analytical technologies. Artificial intelligence, machine learning, blockchain, and automated auditing systems demonstrated promising performance in improving fraud detection under experimental conditions; however, evidence supporting their effectiveness in large-scale operational healthcare settings remains limited, and implementation challenges related to scalability, interoperability, privacy, and governance should be considered. Publication trends indicate increasing international research interest in FWA, although evidence remains concentrated in a limited number of countries.