Bagging and regression trees in individual claims reserving

Jan Janoušek () and Michal Pešta ()
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Jan Janoušek: Charles University
Michal Pešta: Charles University

Statistical Papers, 2025, vol. 66, issue 4, No 16, 26 pages

Abstract: Abstract This methodological paper presents a novel approach to individual claims reserving in non-life insurance, utilizing machine learning techniques. Claims reserving in insurance amounts to stochastically predict the overall loss reserves to cover possible future claims. The developed concepts leverage regression trees and bootstrap aggregating (bagging) to improve the accuracy of reserve predictions. Unlike current approaches focusing solely on the number of claims so far, our approach models both the frequency and severity of claims. Out-of-bag error is employed as a diagnostic tool to enhance model validation. The effectiveness of the proposed methodology is demonstrated through an exemplary data analysis, showcasing its potential to provide more accurate reserve estimates in claims reserving.

Keywords: Bagging; Bootstrap; Regression tree; Out-of-bag error; Machine learning; Individual claims reserving; Non-life insurance (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:stpapr:v:66:y:2025:i:4:d:10.1007_s00362-025-01715-9

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DOI: 10.1007/s00362-025-01715-9

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