Valid Model-Free Prediction of Future Insurance Claims
18 Pages Posted: 22 Oct 2019 Last revised: 26 Feb 2020
Date Written: October 12, 2019
Bias resulting from model misspecification is a concern when predicting insurance claims. Indeed, this bias puts the insurer at risk of making invalid or unreliable predictions. A method that could provide provably valid predictions uniformly across a large class of possible distributions would effectively eliminate the risk of model misspecification bias. Conformal prediction is one such method that can meet this need, and here we tailor that approach to the typical insurance application and show that the predictions are not only valid but also efficient across a wide range of settings.
Keywords: Machine learning; model misspecification; predictive modeling; robustness; unsupervised learning.
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