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Payments Per Claim Model of Outstanding Claims Reserve Based on Fuzzy Linear Regression

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Abstract

There are uncertainties in factors such as inflation. Historical data and variable values are ambiguous. They lead to ambiguity in the assessment of outstanding claims reserves. The payments per claim model can only perform point estimation. But the fuzzy linear regression is based on fuzzy theory and can directly deal with uncertainty in data. Therefore, this paper proposes a payments per claim model based on fuzzy linear regression. The linear regression method and fuzzy least square method are used to estimate the parameters of the fuzzy regression equation. And the estimated results are introduced into the payments per claim model. Then, the predicted value of each accident reserve is obtained. This result is compared with that of the traditional payments per claim model. And we find that the payments per claim model of estimating the fuzzy linear regression parameters based on the linear programming method is more effective. The model gives the width of the compensation amount for each accident year. In addition, this model solves the problem that the traditional payments per claim model cannot measure the dynamic changes in reserves.

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Acknowledgements

This work was financially supported by the Project of National Natural Science Foundation of China(61502280,61472228).

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Authors and Affiliations

  1. College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, China

    Chun Yan, Qian Liu, Jiahui Liu & Meixuan Li

  2. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China

    Wei Liu

  3. Department of Computing, Canterbury Christ Church University, Canterbury, CT1 1QU, UK

    Man Qi

Authors
  1. Chun Yan
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  2. Qian Liu
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  3. Jiahui Liu
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  4. Wei Liu
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  5. Meixuan Li
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  6. Man Qi
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Correspondence to Wei Liu.

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Yan, C., Liu, Q., Liu, J. et al. Payments Per Claim Model of Outstanding Claims Reserve Based on Fuzzy Linear Regression. Int. J. Fuzzy Syst. 21, 1950–1960 (2019). https://doi.org/10.1007/s40815-019-00617-x

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  • DOI: https://doi.org/10.1007/s40815-019-00617-x

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