A Hybrid Machine Learning Framework for Personalized Risk Prediction in Health Insurance Underwriting
Year: 2025
Type: article
Abstract: Traditional health insurance underwriting methods rely heavily on actuarial models based on static demographic and historical cost data, limiting their ability to reflect individual health risks accurately. This study proposes a machine learning-based framework to improve personalized risk stratification by leveraging claims data, electronic health records (EHR), and lifestyle indicators. The framework integrates eXtreme Gradient Boosting (XGBoost) with a feedforward neural network (FNN) comprising three hidden layers and incorporating ReLU activation, dropout regularization, and batch normalization. The hybrid model was trained and evaluated on a real-world dataset containing over anonymized member records from a large U.S. insurer. It achieved an AUC-ROC of 0.79 significantly outperforming traditional baseline methods. Model interpretability was addressed using SHAP to identify key risk drivers. This journal outlines an approach that supports dynamic, data-driven underwriting decisions while maintaining compliance and transparency. These results demonstrate that machine learning can enhance accuracy, efficiency, and fairness in health insurance risk assessment.
Authors: Selvakumar Kalyanasundaram, Sashwath Selvakumar
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414020373