A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf
.
Accurate and Interpretable Machine Learning for Transparent Pricing of Health Insurance Plans
[article]
2021
arXiv
pre-print
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
arXiv:2009.10990v2
fatcat:xdrfiu5u4ngxhakw65s6mydn64