Payments per claim model of outstanding claims reserve based on fuzzy linear regression
Journal article
Yan, C., Liu, Q., Liu, J., Liu, W., Li, M. and Qi, M. 2019. Payments per claim model of outstanding claims reserve based on fuzzy linear regression. International Journal of Fuzzy Systems. 21, pp. 1950-1960. https://doi.org/10.1007/s40815-019-00617-x
| Authors | Yan, C., Liu, Q., Liu, J., Liu, W., Li, M. and Qi, M. |
|---|---|
| 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. |
| Keywords | Payments per claim model; Fuzzy linear regression |
| Year | 2019 |
| Journal | International Journal of Fuzzy Systems |
| Journal citation | 21, pp. 1950-1960 |
| Publisher | Springer |
| ISSN | 2199-3211 |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/s40815-019-00617-x |
| Official URL | https://link.springer.com/article/10.1007/s40815-019-00617-x |
| Publication dates | |
| 15 Mar 2019 | |
| Publication process dates | |
| Accepted | 16 Feb 2019 |
| Deposited | 20 May 2021 |
| Accepted author manuscript | License |
| Output status | Published |
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