Researchers at University Gadjah Mada Report Research in Machine Learning (Landscape of Machine Learning for Health Insurance Fraud Detection: Global Evidence and Lessons for Indonesia): Machine Learning

2026 JUL 14 (NewsRx) -- By a News Reporter-Staff News Editor at Health Policy and Law Daily -- Researchers detail new data in artificial intelligence. According to news originating from Yogyakarta, Indonesia, by NewsRx correspondents, research stated, “Fraud in health insurance claims threatens the sustainability of health financing systems and the quality of healthcare services.”

Our news journalists obtained a quote from the research from University Gadjah Mada: “Conventional detection methods often fail to capture complex, evolving fraud patterns, whereas machine learning (ML) offers adaptive, data-driven solutions capable of analyzing large-scale claims data. This study reviews the development of ML-based approaches for health insurance fraud detection, mapping research trends, data source distribution, and commonly used algorithms over the past decade. A narrative literature review with bibliometric analysis following the PRISMA framework was conducted across five databases: PubMed, IEEE Xplore, Scopus, Web of Science (WoS), and Google Scholar, using the advanced search queries based on the main keywords, including fraud detection, machine learning, health insurance, and claims. In total, 79 studies published between 2015 and 2025 were included for analysis. The findings show a sharp rise in research on machine learning for health insurance fraud detection, with supervised and ensemble learning methods dominating. Most studies originate from the United States, India, China, and Indonesia, with Random Forest, XGBoost, and Neural Networks emerging as the most widely applied algorithms.”

According to the news reporters, the research concluded: “Recent works also highlight a growing interest in explainable and federated learning. This study provides an overview of the global landscape of machine learning in health insurance fraud detection and offers insights to strengthen Indonesia’s National Health Insurance (JKN) system through data-driven, transparent, and scalable fraud-prevention strategies.”

For more information on this research see: Landscape of Machine Learning for Health Insurance Fraud Detection: Global Evidence and Lessons for Indonesia. Jurnal Jaminan Kesehatan Nasional, 2026,6(1):30-41. The publisher for Jurnal Jaminan Kesehatan Nasional is BPJS Kesehatan.

A free version of this journal article is available at https://doi.org/10.53756/jjkn.v6i1.444.

Our news editors report that additional information may be obtained by contacting Luluk Fadhoh Sakinah, University Gadjah Mada, Yogyakarta, Indonesia. Additional authors for this research include Putu Gede Purwa Darmawan, Renata Gita Cahyani Sese, Annisa Ristya Rahmanti, Anis Fuad.

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