ライブラリ登録: Guest
International Journal for Uncertainty Quantification

年間 6 号発行

ISSN 印刷: 2152-5080

ISSN オンライン: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

Indexed in

A NON-PARAMETRIC METHOD FOR INCURRED BUT NOT REPORTED CLAIM RESERVE ESTIMATION

巻 2, 発行 1, 2012, pp. 39-51
DOI: 10.1615/Int.J.UncertaintyQuantification.v2.i1.40
Get accessDownload

要約

The number and cost of claims that will arise from each policy of an insurance company′s portfolio are unknown. In fact, there is a high degree of uncertainty on how much will ultimately be the cost of claims, not only during the period of inception but also after the contract termination, since there might be future, not yet reported, losses associated with past claims. Therefore, in practice, insurance companies have to protect themselves against the possibility of this ultimate cost by creating an additional reserve known as the incurred but not reported (IBNR) reserve. This work introduces new non-parametric models to IBNR estimation based on kernel methods; namely, support vector regression and Gaussian process regression. These are used to learn certain types of nonlinear structures present in claims data using the residuals produced by a benchmark IBNR estimation model, Mack′s chain ladder. The proposed models are then compared to Mack′s model using real data examples. Our results show that the three new proposed models are competitive when compared to Mack′s benchmark model: they may produce the closest predictions of IBNR and also more accurate estimates, given that the variance for the reserve estimation, obtained through the bootstrap technique, is usually smaller than the one given by Mack′s model.

によって引用された
  1. Cunha Americo, Nasser Rafael, Sampaio Rubens, Lopes Hélio, Breitman Karin, Uncertainty quantification through the Monte Carlo method in a cloud computing setting, Computer Physics Communications, 185, 5, 2014. Crossref

  2. Lally Nathan, Hartman Brian, Estimating loss reserves using hierarchical Bayesian Gaussian process regression with input warping, Insurance: Mathematics and Economics, 82, 2018. Crossref

  3. Zhai Jia, Zheng Haitao, Bai Manying, Jiang Yunyun, An Uncertain Alternating Renewal Insurance Risk Model, Mathematical Problems in Engineering, 2020, 2020. Crossref

  4. Blier-Wong Christopher, Cossette Hélène, Lamontagne Luc, Marceau Etienne, Machine Learning in P&C Insurance: A Review for Pricing and Reserving, Risks, 9, 1, 2020. Crossref

  5. Ulyah S M, Mardianto M F F, Sediono , Comparing the Performance of Seasonal ARIMAX Model and Nonparametric Regression Model in Predicting Claim Reserve of Education Insurance, Journal of Physics: Conference Series, 1397, 1, 2019. Crossref

  6. Taha Ayman, Cosgrave Bernard, Rashwan Wael, McKeever Susan, Insurance Reserve Prediction: Opportunities and Challenges, 2021 International Conference on Computational Science and Computational Intelligence (CSCI), 2021. Crossref

  7. Taha Ayman, Cosgrave Bernard, Mckeever Susan, Using Feature Selection with Machine Learning for Generation of Insurance Insights, Applied Sciences, 12, 6, 2022. Crossref

Begell Digital Portal Begellデジタルライブラリー 電子書籍 ジャーナル 参考文献と会報 リサーチ集 価格及び購読のポリシー Begell House 連絡先 Language English 中文 Русский Português German French Spain