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.

应用机器学习计算健康保险产品的精准合理价格的研究，分析可解释机器学习模型如何超越精算模型的预测准确性，同时保持可解释性。