New Anonymized Health Insurance Dataset Enhances Market and Risk Analysis

Access to real and comprehensive datasets remains a significant challenge for research and professional analysis within the U.S. health insurance industry, primarily due to confidentiality and competition constraints. Addressing this gap, a new extensive dataset originating from a Spanish health insurance portfolio, spanning 2017 to 2019, has been developed, providing valuable insights into health insurance market dynamics. The dataset comprises over 70,000 unique insured individuals with more than 225,000 data records, featuring 42 variables that blend insurer-sourced data with derived area-based contextual information from public resources, ensuring both depth and breadth in analysis potential. Privacy considerations are meticulously managed through data anonymization, preserving confidentiality while enabling rigorous analytical applications, including product design, risk assessment, and market behavior studies. The availability of this dataset offers an evidence-based tool supporting actuarial research, regulatory compliance studies, and the development of machine learning models tailored to health insurance scenarios. Educational applications are also bolstered, providing real-world data for student training in data preprocessing and statistical examination, enhancing academic-industry linkage. The dataset and associated R code for spatial data matching are openly accessible at Mendeley Data, endorsing transparency and reproducibility in analytical methods. This work aligns with ongoing industry efforts to integrate multiple data sources and contextual variables to refine underwriting and pricing strategies under regulatory frameworks. The data structure facilitates explorations of socioeconomic impacts, product innovation, and geographic influences on insurance risk and performance. Notably, the dataset exemplifies compliance with stringent European data protection regulations (GDPR), underscoring the importance of anonymization and controlled data usage in insurance analytics. This integration of individual and contextual data marks a significant advancement for insurance market analysis, enabling professionals to navigate complex datasets for enhanced decision-making. The dataset's methodological framework, including spatial transfer techniques and climate area classifications, offers a blueprint potentially adaptable to U.S. insurance analytics environments. Ultimately, this resource enriches the repertoire of data-driven tools available to insurers, actuaries, and policymakers to address challenges inherent in asymmetric information and competitive dynamics in health insurance markets.