A Review of Machine Learning Techniques for Risk Evaluation in Healthcare and Insurance Systems

Abstract

As chronic diseases surge globally and insurance markets grow increasingly complex, traditional risk assessment frameworks are becoming obsolete. Static evaluations based on historical data and rigid actuarial models can no longer keep pace with the dynamic nature of individual health profiles or the rising volume of real-time behavioral and biomedical data. Machine learning (ML), with its capacity to analyze vast, high-dimensional, and heterogeneous datasets, is revolutionizing risk modeling in both healthcare and insurance sectors. This survey highlights the critical role of ML in accurately and proactively identifying, predicting, and managing risks. In healthcare, ML models enable disease prediction, patient risk stratification, early warning systems, and mortality forecasting by processing data from electronic health records, medical imaging, wearable devices, and genomics. In parallel, the insurance domain benefits from ML-driven claims prediction, fraud detection, customer segmentation, and dynamic underwriting using behavioral, demographic, and historical claims data. Techniques such as Random Forest, Support Vector Machines (SVM), Neural Networks, and Gradient Boosting (GB) are evaluated for their performance, interpretability, and flexibility. Key issues, including data fragmentation, model bias, and interpretability in high-stakes scenarios, are addressed. Grounded in state-of-the-art algorithms and real-world applications, this research demonstrates ML’s transformative potential for individualizing, resilient, and data-driven healthcare and insurance systems.