Valid Model-Free Prediction of Future Insurance Claims

18 Pages Posted: 22 Oct 2019 Last revised: 26 Feb 2020

See all articles by Liang Hong

Liang Hong

The University of Texas at Dallas

Ryan Martin

North Carolina State University - Department of Statistics

Date Written: October 12, 2019

Abstract

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.

Suggested Citation

Hong, Liang and Martin, Ryan, Valid Model-Free Prediction of Future Insurance Claims (October 12, 2019). Available at SSRN: https://ssrn.com/abstract=3468969 or http://dx.doi.org/10.2139/ssrn.3468969

Liang Hong (Contact Author)

The University of Texas at Dallas ( email )

2601 North Floyd Road
Richardson, TX 75083
United States

Ryan Martin

North Carolina State University - Department of Statistics ( email )

Raleigh, NC 27695-8203
United States

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