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<p>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.</p>

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Bagging and regression trees in individual claims reserving

  • 期刊论文

  • 发表日期：

    2025年5月6日

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  • Jan Janoušek,
  • Michal Pešta

摘要

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.