DeepAI AI Chat
Log In Sign Up

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

09/23/2020
by   Rohun Kshirsagar, et al.
41

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

07/21/2020

Machine Learning in Population and Public Health

Research in population and public health focuses on the mechanisms betwe...
12/21/2020

An Approach to Gender Pay Equity Analysis Using Bayesian Hierarchical Regression

Diversity and inclusion, or D and I, is a topic that sparks the interest...
11/04/2019

Understanding racial bias in health using the Medical Expenditure Panel Survey data

Over the years, several studies have demonstrated that there exist signi...
11/29/2018

Machine Learning on Electronic Health Records: Models and Features Usages to predict Medication Non-Adherence

Adherence can be defined as "the extent to which patients take their med...
12/06/2018

Automatically Explaining Machine Learning Prediction Results: A Demonstration on Type 2 Diabetes Risk Prediction

Background: Predictive modeling is a key component of solutions to many ...
04/12/2019

Boosting insights in insurance tariff plans with tree-based machine learning

Pricing actuaries typically stay within the framework of generalized lin...