Fairness of Ratemaking for Catastrophe Insurance: Lessons from Machine Learning

Nan Zhang
Corresponding Author
Nan Zhang
[email protected]
https://orcid.org/0000-0002-0454-7885
Warrington College of Business, University of Florida, Gainesville, Florida 32611;
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,
Heng Xu
Heng Xu
[email protected]
https://orcid.org/0000-0001-5642-6543
Kogod School of Business, American University, Washington, District of Columbia 20016
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Corresponding Author

Nan Zhang

[email protected]

https://orcid.org/0000-0002-0454-7885

Warrington College of Business, University of Florida, Gainesville, Florida 32611;

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,

Heng Xu

[email protected]

https://orcid.org/0000-0001-5642-6543

Kogod School of Business, American University, Washington, District of Columbia 20016

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Published Online:17 Jan 2023https://doi.org/10.1287/isre.2022.1195

Supplemental Material

isre.2022.1195.sm1.pdf

cover image Information Systems Research

Volume 35, Issue 2

June 2024

Pages iii-ix, 441-916, C2

Article Information

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Received:February 15, 2022
Accepted:December 06, 2022
Published Online:January 17, 2023

Copyright © 2023, INFORMS

Cite as

Nan Zhang, Heng Xu (2023) Fairness of Ratemaking for Catastrophe Insurance: Lessons from Machine Learning. Information Systems Research 35(2):469-488.

https://doi.org/10.1287/isre.2022.1195

Keywords

Acknowledgments

The authors express their sincere gratitude to the editors and reviewers for their helpful comments. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the sponsors.