Accurate and Interpretable Machine Learning for Transparent Pricing of Health Insurance Plans

TL;DR

This paper presents a machine learning approach that improves the accuracy and interpretability of health insurance pricing models, outperforming traditional actuarial methods and identifying opportunities for more competitive premiums.

Contribution

The study introduces a two-stage machine learning model for health insurance pricing that surpasses existing actuarial models in accuracy and maintains interpretability.

Findings

Models performed 20% better than existing pricing models.

Successfully identified 84% of concession opportunities.

Demonstrated the feasibility of explainable ML in health insurance pricing.

Abstract

Health insurance companies cover half of the United States population through commercial employer-sponsored health plans and pay 1.2 trillion US dollars every year to cover medical expenses for their members. The actuary and underwriter roles at a health insurance company serve to assess which risks to take on and how to price those risks to ensure profitability of the organization. While Bayesian hierarchical models are the current standard in the industry to estimate risk, interest in machine learning as a way to improve upon these existing methods is increasing. Lumiata, a healthcare analytics company, ran a study with a large health insurance company in the United States. We evaluated the ability of machine learning models to predict the per member per month cost of employer groups in their next renewal period, especially those groups who will cost less than 95\% of what an actuarial model predicts (groups with "concession opportunities"). We developed a sequence of two models, an individual patient-level and an employer-group-level model, to predict the annual per member per month allowed amount for employer groups, based on a population of 14 million patients. Our models performed 20\% better than the insurance carrier's existing pricing model, and identified 84\% of the concession opportunities. This study demonstrates the application of a machine learning system to compute an accurate and fair price for health insurance products and analyzes how explainable machine learning models can exceed actuarial models' predictive accuracy while maintaining interpretability.