Machine Learning-Based Big Data Analytics for Personalized Health Insurance Services

Sipho Dlamini; Endah Kurniyaningrum; Kustiyono Kustiyono; Untung Rahardja; Qurotul Aini; Sahal Mahfudz

Authors

Sipho Dlamini Mfinitee Incorporation Author
Endah Kurniyaningrum Universitas Trisakti Author https://orcid.org/0009-0006-0094-1208
Kustiyono Kustiyono Ngudi Waluyo University Author
Untung Rahardja University of Raharja Author https://orcid.org/0000-0002-2166-2412
Qurotul Aini University of Raharja Author https://orcid.org/0000-0002-7546-5721
Sahal Mahfudz University of Raharja Author

Keywords:

Big Data Analytics, Personalized Health Insurance, Machine Learning, Predictive Risk Modeling, InsurTech

Abstract

The conventional health insurance sector faces challenges in accurately evaluating risk and pricing policies, as it primarily relies on aggregated demographic data and general medical histories. This often leads to inefficient premium structures and limited preventive care opportunities, highlighting the need for more individualized risk assessment. This study aims to examine how Big Data Analytics (BDA) and machine learning can enhance the design of personalized health insurance products by integrating real-time, individual-level data beyond traditional actuarial methods. The method involves analyzing a large dataset of 50,000 policyholders over three years, including anonymized electronic health records (EHRs), wearable device data (activity and vital signs), social determinants of health (SDOH), and claims history. Predictive modeling was conducted using XGBoost and Random Forest to estimate individual-level claim frequency and severity. The results show that the BDA-driven approach outperforms traditional actuarial models, achieving an AUC of 0.89. Key predictors of high-cost claims include sleep quality and heart rate variability. These insights enable the creation of hyper-segmented insurance products with dynamic premiums and behavior-based incentives. In conclusion, integrating BDA into health insurance underwriting improves pricing accuracy, reduces adverse selection, and enhances profitability. It also supports the development of personalized insurance products that encourage proactive health management, representing a significant advancement in risk management within the InsurTech industry.

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