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THE IMPACTS OF INDIVIDUAL INFORMATION ON LOSS RESERVING

Published online by Cambridge University Press:  14 December 2020

Zhigao Wang Open the ORCID record for Zhigao Wang [Opens in a new window] ,
Xianyi Wu  and
Chunjuan Qiu
Zhigao Wang
Affiliation:
Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal UniversityShanghai, China E-Mail: [email protected]
Xianyi Wu
Affiliation:
Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal UniversityShanghaiChina E-Mail: [email protected]
Chunjuan Qiu*
Affiliation:
Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal UniversityShanghai, China E-Mail: [email protected]
*
E-Mail: [email protected]
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Abstract

The projection of outstanding liabilities caused by incurred losses or claims has played a fundamental role in general insurance operations. Loss reserving methods based on individual losses generally perform better than those based on aggregate losses. This study uses a parametric individual information model taking not only individual losses but also individual information such as age, gender, and so on from policies themselves into account. Based on this model, this study proposes a computation procedure for the projection of the outstanding liabilities, discusses the estimation and statistical properties of the unknown parameters, and explores the asymptotic behaviors of the resulting loss reserving as the portfolio size approaching infinity. Most importantly, this study demonstrates the benefits of individual information on loss reserving. Remarkably, the accuracy gained from individual information is much greater than that from considering individual losses. Therefore, it is highly recommended to use individual information in loss reserving in general insurance.

Keywords

General insuranceindividual dataloss reservingIBNR and RBNS claimsasymptotic analysis
Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 51 , Issue 1 , January 2021 , pp. 303 - 347
Copyright
© 2020 by Astin Bulletin. All rights reserved

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