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

no code implementations • 23 Sep 2020 • Rohun Kshirsagar, Li-Yen Hsu, Vatshank Chaturvedi, Charles H. Greenberg, Matthew McClelland, Anushadevi Mohan, Wideet Shende, Nicolas P. Tilmans, Renzo Frigato, Min Guo, Ankit Chheda, Meredith Trotter, Shonket Ray, Arnold Lee, Miguel Alvarado

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").

BIG-bench Machine Learning Interpretable Machine Learning