An Overview in Adverse Selection: A Case Study in Indonesia Healthcare Insurance

This study analyzes adverse selection in health insurance poses a significant challenge to the sustainability of insurance schemes, particularly in developing countries where enrolment is voluntary and insurance literacy is low. Information asymmetry between participants and insurers often results in higher-risk individuals dominating the pool, threatening the program’s financial stability. This study aims to synthesize findings from various studies on adverse selection in health insurance, identifying common patterns, causal factors, and mitigation strategies. A systematic literature review approach was used to examine about 40 selected articles published between 2012 and 2025 related to adverse selection in different types of health insurance schemes (public, private, and community-based). Literature was sourced from academic databases such as Google Scholar, PubMed, and others, using specific boolean keyword combinations. Adverse selection was found to be most prevalent in voluntary schemes in developing countries. In contrast, evidence from some developed nations indicated advantageous selection. Mitigation strategies such as premium subsidies, risk-based premium setting, risk pooling, and improving insurance literacy proved effective in reducing negative selection. Advanced technologies like big data and machine learning also showed promise in managing risk profiles. Addressing adverse selection requires a multi-dimensional approach involving public policy, financial incentives, and technological innovation. A combination of mandatory enrolment, risk adjustment, targeted subsidies, and improved literacy is essential for building a sustainable and inclusive health insurance system.