Big data analytics in healthcare insurance: Applications, challenges, and strategic implications for data‑driven decision-making

Big data analytics is revolutionizing the healthcare insurance sector by enabling data-driven decision-making, enhancing risk management, and improving patient outcomes. The exponential growth of healthcare data, including electronic health records, insurance claims, and wearable device data, has necessitated the adoption of advanced analytical tools such as predictive analytics, machine learning, and artificial intelligence. This study explores the classifications and sources of big data in healthcare insurance and examines its key applications, including underwriting, fraud detection, claims processing, and population health management. A qualitative literature-based approach is employed to synthesize current knowledge and identify emerging trends. Despite its benefits, the implementation of big data analytics presents challenges such as data privacy concerns, integration complexities, and infrastructure limitations. This paper highlights these challenges and proposes strategic solutions, emphasizing the importance of data governance, regulatory compliance, and investment in analytics capabilities. The findings suggest that effective utilization of big data analytics can significantly improve operational efficiency and service delivery within healthcare insurance systems.