Annals of Actuarial Science最新文献_第2页

De-risking in multi-state life and health insurance 多州人寿和健康保险的去风险化

IF 1.7

Annals of Actuarial Science Pub Date : 2024-04-22 DOI: 10.1017/s1748499524000083

Susanna Levantesi, Massimiliano Menzietti, Anna Kamille Nyegaard

引用次数: 0

Smoothness and monotonicity constraints for neural networks using ICEnet 利用 ICEnet 实现神经网络的平滑性和单调性约束

IF 1.7

Annals of Actuarial Science Pub Date : 2024-04-01 DOI: 10.1017/s174849952400006x

Ronald Richman, Mario V. Wüthrich

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Interpretable zero-inflated neural network models for predicting admission counts 用于预测入院人数的可解释零膨胀神经网络模型

IF 1.7

Annals of Actuarial Science Pub Date : 2024-03-26 DOI: 10.1017/s1748499524000058

Alex Jose, Angus S. Macdonald, George Tzougas, George Streftaris

{"title":"Interpretable zero-inflated neural network models for predicting admission counts","authors":"Alex Jose, Angus S. Macdonald, George Tzougas, George Streftaris","doi":"10.1017/s1748499524000058","DOIUrl":"https://doi.org/10.1017/s1748499524000058","url":null,"abstract":"In this paper, we construct interpretable zero-inflated neural network models for modeling hospital admission counts related to respiratory diseases among a health-insured population and their dependants in the United States. In particular, we exemplify our approach by considering the zero-inflated Poisson neural network (ZIPNN), and we follow the combined actuarial neural network (CANN) approach for developing zero-inflated combined actuarial neural network (ZIPCANN) models for modeling admission rates, which can accommodate the excess zero nature of admission counts data. Furthermore, we adopt the LocalGLMnet approach (Richman & Wüthrich (2023). Scandinavian Actuarial Journal, 2023(1), 71–95.) for interpreting the ZIPNN model results. This facilitates the analysis of the impact of a number of socio-demographic factors on the admission rates related to respiratory disease while benefiting from an improved predictive performance. The real-life utility of the methodologies developed as part of this work lies in the fact that they facilitate accurate rate setting, in addition to offering the potential to inform health interventions.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140301621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Genetic testing and actuarial science 基因检测与精算学

IF 1.7

Annals of Actuarial Science Pub Date : 2024-03-01 DOI: 10.1017/s1748499524000034

A. Macdonald

引用次数: 0

GEMAct: a Python package for non-life (re)insurance modeling GEMAct：用于非寿险（再）保险建模的 Python 软件包

IF 1.7

Annals of Actuarial Science Pub Date : 2024-02-14 DOI: 10.1017/s1748499524000022

Gabriele Pittarello, Edoardo Luini, Manfred Marvin Marchione

引用次数: 0

The discrete-time arbitrage-free Nelson-Siegel model: a closed-form solution and applications to mixed funds representation 离散时间无套利的 Nelson-Siegel 模型：封闭式解法及混合基金表示法的应用

IF 1.7

Annals of Actuarial Science Pub Date : 2024-02-12 DOI: 10.1017/s1748499524000010

Ramin Eghbalzadeh, Frédéric Godin, Patrice Gaillardetz

引用次数: 0

On clustering levels of a hierarchical categorical risk factor 关于分层分类风险因素的聚类水平

IF 1.7

Annals of Actuarial Science Pub Date : 2024-02-01 DOI: 10.1017/s1748499523000283

Bavo D.C. Campo, Katrien Antonio

{"title":"On clustering levels of a hierarchical categorical risk factor","authors":"Bavo D.C. Campo, Katrien Antonio","doi":"10.1017/s1748499523000283","DOIUrl":"https://doi.org/10.1017/s1748499523000283","url":null,"abstract":"Handling nominal covariates with a large number of categories is challenging for both statistical and machine learning techniques. This problem is further exacerbated when the nominal variable has a hierarchical structure. We commonly rely on methods such as the random effects approach to incorporate these covariates in a predictive model. Nonetheless, in certain situations, even the random effects approach may encounter estimation problems. We propose the data-driven Partitioning Hierarchical Risk-factors Adaptive Top-down algorithm to reduce the hierarchically structured risk factor to its essence, by grouping similar categories at each level of the hierarchy. We work top-down and engineer several features to characterize the profile of the categories at a specific level in the hierarchy. In our workers’ compensation case study, we characterize the risk profile of an industry via its observed damage rates and claim frequencies. In addition, we use embeddings to encode the textual description of the economic activity of the insured company. These features are then used as input in a clustering algorithm to group similar categories. Our method substantially reduces the number of categories and results in a grouping that is generalizable to out-of-sample data. Moreover, we obtain a better differentiation between high-risk and low-risk companies.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139657706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Boosted Poisson regression trees: a guide to the BT package in R 提升泊松回归树：R 中 BT 软件包的使用指南

IF 1.7

Annals of Actuarial Science Pub Date : 2024-01-15 DOI: 10.1017/s174849952300026x

Gireg Willame, Julien Trufin, Michel Denuit

{"title":"Boosted Poisson regression trees: a guide to the BT package in R","authors":"Gireg Willame, Julien Trufin, Michel Denuit","doi":"10.1017/s174849952300026x","DOIUrl":"https://doi.org/10.1017/s174849952300026x","url":null,"abstract":"Thanks to its outstanding performances, boosting has rapidly gained wide acceptance among actuaries. Wüthrich and Buser (Data Analytics for Non-Life Insurance Pricing. Lecture notes available at SSRN. http://dx.doi.org/10.2139/ssrn.2870308, 2019) established that boosting can be conducted directly on the response under Poisson deviance loss function and log-link, by adapting the weights at each step. This is particularly useful to analyze low counts (typically, numbers of reported claims at policy level in personal lines). Huyghe et al. (Boosting cost-complexity pruned trees on Tweedie responses: The ABT machine for insurance ratemaking. Scandinavian Actuarial Journal. https://doi.org/10.1080/03461238.2023.2258135, 2022) adopted this approach to propose a new boosting machine with cost-complexity pruned trees. In this approach, trees included in the score progressively reduce to the root-node one, in an adaptive way. This paper reviews these results and presents the new BT package in R contributed by Willame (Boosting Trees Algorithm. https://cran.r-project.org/package=BT; https://github.com/GiregWillame/BT, 2022), which is designed to implement this approach for insurance studies. A numerical illustration demonstrates the relevance of the new tool for insurance pricing.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139469134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Epidemic modelling and actuarial applications for pandemic insurance: a case study of Victoria, Australia 大流行病保险的流行病建模和精算应用：澳大利亚维多利亚州的案例研究

IF 1.7

Annals of Actuarial Science Pub Date : 2024-01-09 DOI: 10.1017/s1748499523000246

Chang Zhai, Ping Chen, Zhuo Jin, Tak Kuen Siu

引用次数: 0

Nonparametric intercept regularization for insurance claim frequency regression models 保险索赔频率回归模型的非参数截距正则化

IF 1.7

Annals of Actuarial Science Pub Date : 2024-01-05 DOI: 10.1017/s1748499523000271

Gee Y. Lee, Himchan Jeong

{"title":"Nonparametric intercept regularization for insurance claim frequency regression models","authors":"Gee Y. Lee, Himchan Jeong","doi":"10.1017/s1748499523000271","DOIUrl":"https://doi.org/10.1017/s1748499523000271","url":null,"abstract":"In a subgroup analysis for an actuarial problem, the goal is for the investigator to classify the policyholders into unique groups, where the claims experience within each group are made as homogenous as possible. In this paper, we illustrate how the alternating direction method of multipliers (ADMM) approach for subgroup analysis can be modified so that it can be more easily incorporated into an insurance claims analysis. We present an approach to penalize adjacent coefficients only and show how the algorithm can be implemented for fast estimation of the parameters. We present three different cases of the model, depending on the level of dependence among the different coverage groups within the data. In addition, we provide an interpretation of the credibility problem using both random effects and fixed effects, where the fixed effects approach corresponds to the ADMM approach to subgroup analysis, while the random effects approach represents the classic Bayesian approach. In an empirical study, we demonstrate how these approaches can be applied to real data using the Wisconsin Local Government Property Insurance Fund data. Our results show that the presented approach to subgroup analysis could provide a classification of the policyholders that improves the prediction accuracy of the claim frequencies in case other classifying variables are unavailable in the data.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139373310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0