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 |
https://repository.canterbury.ac.uk/item/8xw50/payments-per-claim-model-of-outstanding-claims-reserve-based-on-fuzzy-linear-regression
Download files
47
total views36
total downloads2
views this month4
downloads this month
Export as
Related outputs
Repairing process models with non-free-choice constructs based on token replay
Bai, E., Qi, M., Luan, W., Li, P. and Du, Y. 2022. Repairing process models with non-free-choice constructs based on token replay. Computing and Informatics. 41 (4), pp. 1054-1077. https://doi.org/10.31577/cai_2022_4_1054Transformers only look once with nonlinear combination for real-time object detection
Xia, R., Li, G., Huang, Z., Pang, Y. and Qi, M. 2022. Transformers only look once with nonlinear combination for real-time object detection. Neural Computing and Applications. https://doi.org/10.1007/s00521-022-07333-yDeviation detection in clinical pathways based on business alignment
Tian, Y., Li, X., Qi, Man, Han, D. and Du, Yuyue 2022. Deviation detection in clinical pathways based on business alignment. Scientific Programming. 2022, pp. 1-13. https://doi.org/10.1155/2022/6993449Security vulnerabilities of popular smart home appliances
Qi, M., Induruwa, A. and Hussain, F. 2021. Security vulnerabilities of popular smart home appliances. in: Proceeding of The Twentieth International Conference on Networks April 18, 2021 to April 22, 2021 - Porto, PortugalImproved adaptive genetic algorithm for the vehicle insurance fraud identification model based on a BP neural network
Yan, C., Li, M., Liu, W. and Qi, M. 2020. Improved adaptive genetic algorithm for the vehicle insurance fraud identification model based on a BP neural network. Theoretical Computer Science. 817, pp. 12-23. https://doi.org/10.1016/j.tcs.2019.06.025Hybrid Intrusion Detection System for Smart Home Applications
Hussain, Fida, Induruwa, Abhaya and Qi, Man 2020. Hybrid Intrusion Detection System for Smart Home Applications. in: Mahmood, Z. (ed.) Developing and Monitoring Smart Environments for Intelligent Cities IGI Global. pp. 300-322Fuzzy interacting multiple model H∞ particle filter algorithm based on current statistical model
Wang, Q., Chen, X., Zhang, L., Li, J., Zhao, C. and Qi, M. 2019. Fuzzy interacting multiple model H∞ particle filter algorithm based on current statistical model. International Journal of Fuzzy Systems. 21, pp. 1894-1905. https://doi.org/10.1007/s40815-019-00678-yTemporal sparse feature auto-combination deep network for video action recognition
Wang, Q., Gong, D., Qi, M., Shen, Y. and Lei, Y. 2018. Temporal sparse feature auto-combination deep network for video action recognition. Concurrency and Computation: Practice and Experience. https://doi.org/10.1002/cpe.4487Soundness analytics of composed logical workflow nets
Liu, W., Wang, L., Feng, X., Qi, M., Yan, C. and Li, M. 2017. Soundness analytics of composed logical workflow nets. International Journal of Parallel Programming. https://doi.org/10.1007/s10766-017-0536-8A sliding window-based dynamic load balancing for heterogeneous Hadoop clusters
Liu, Y., Jing, W., Liu, Y., Lv, L., Qi, M. and Xiang, Y. 2016. A sliding window-based dynamic load balancing for heterogeneous Hadoop clusters. Concurrency and Computation: Practice and Experience. 29 (3). https://doi.org/10.1002/cpe.3763Gaussian-Gamma collaborative filtering: a hierarchical Bayesian model for recommender systems
Luo, C., Zhang, B., Xiang, Y. and Qi, M. 2017. Gaussian-Gamma collaborative filtering: a hierarchical Bayesian model for recommender systems. Journal of Computer and System Sciences. https://doi.org/10.1016/j.jcss.2017.03.007Facilitating visual surveillance with motion detections
Qi, M. 2017. Facilitating visual surveillance with motion detections. Concurrency and Computation: Practice and Experience. 29 (3). https://doi.org/10.1002/cpe.3770Data security of android applications
Obiri-Yeboah, J. and Qi, M. 2016. Data security of android applications. in: 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery : ICNC-FSKD 2016 : 13-15 August, Changsha, China IEEE Xplore.AL-DDCNN : a distributed crossing semantic gap learning for person re-identification
Cheng, K., Zhan, Y. and Qi, M. 2017. AL-DDCNN : a distributed crossing semantic gap learning for person re-identification. Concurrency and Computation: Practice and Experience. 29 (3). https://doi.org/10.1002/cpe.3766