A Hybrid Machine Learning Framework for Personalized Risk Prediction in Health Insurance Underwriting

Authors: Selvakumar Kalyanasundaram, Sashwath Selvakumar

DOI: https://doi.org/10.37082/IJIRMPS.v13.i4.232717

Short DOI: https://doi.org/

Country: United States

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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.

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Paper Id: 232717

Published On: 2025-08-11

Published In: Volume 13, Issue 4, July-August 2025

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