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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 > No. 17: IAAI-21, EAAI-21, AAAI-21 Special Programs and Special Track

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

February 1, 2023

Authors

Rohun Kshirsagar

Lumiata Inc, 489 S. El Camino Real, San Mateo, CA 94402 USA


Li-Yen Hsu

Lumiata Inc, 489 S. El Camino Real, San Mateo, CA 94402 USA


Charles H. Greenberg

Lumiata Inc, 489 S. El Camino Real, San Mateo, CA 94402 USA


Matthew McClelland

Lumiata Inc, 489 S. El Camino Real, San Mateo, CA 94402 USA


Anushadevi Mohan

Lumiata Inc, 489 S. El Camino Real, San Mateo, CA 94402 USA


Wideet Shende

Lumiata Inc, 489 S. El Camino Real, San Mateo, CA 94402 USA


Nicolas P. Tilmans

Lumiata Inc, 489 S. El Camino Real, San Mateo, CA 94402 USA


Min Guo

Lumiata Inc, 489 S. El Camino Real, San Mateo, CA 94402 USA


Ankit Chheda

Lumiata Inc, 489 S. El Camino Real, San Mateo, CA 94402 USA


Meredith Trotter

Lumiata Inc, 489 S. El Camino Real, San Mateo, CA 94402 USA


Shonket Ray

Lumiata Inc, 489 S. El Camino Real, San Mateo, CA 94402 USA


Miguel Alvarado

Lumiata Inc, 489 S. El Camino Real, San Mateo, CA 94402 USA


Proceedings:

No. 17: IAAI-21, EAAI-21, AAAI-21 Special Programs and Special Track

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 35

Track:

IAAI Technical Track on Highly Innovative Applications of AI

Downloads:

Download PDF

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.

DOI:

10.1609/aaai.v35i17.17776


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 35



Topics: AAAI

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