Relevant bibliographies by topics / Non-life insurance

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Non-life insurance'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 May 2024

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Journal articles on the topic "Non-life insurance"

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Carroll, Patrick, and E. Straub. "Non-Life Insurance Mathematics." Journal of the Royal Statistical Society. Series A (Statistics in Society) 153, no. 2 (1990): 262. http://dx.doi.org/10.2307/2982815.

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Schmidli, Hanspeter. "Optimisation in Non-Life Insurance." Stochastic Models 22, no. 4 (November 22, 2006): 689–722. http://dx.doi.org/10.1080/15326340600878420.

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Lemaire, Jean, and G. C. Taylor. "Claims Reserving in Non-Life Insurance." Journal of Risk and Insurance 55, no. 2 (June 1988): 396. http://dx.doi.org/10.2307/253338.

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Buchwalder, Markus, Hans Bühlmann, Michael Merz, and Mario V. Wüthrich. "Valuation portfolio in non-life insurance." Scandinavian Actuarial Journal 2007, no. 2 (June 2007): 108–25. http://dx.doi.org/10.1080/03461230701251455.

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Meyers, Glenn, and G. C. Taylor. "Claim Reserving in Non-Life Insurance." Journal of the American Statistical Association 82, no. 397 (March 1987): 362. http://dx.doi.org/10.2307/2289204.

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Ruohonen, Matti. "Non-Life Insurance Mathematics (Erwin Straub)." SIAM Review 32, no. 1 (March 1990): 184–85. http://dx.doi.org/10.1137/1032031.

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Carroll, Patrick, and G. C. Taylor. "Claims Reserving in Non-Life Insurance." Journal of the Royal Statistical Society. Series A (General) 150, no. 2 (1987): 175. http://dx.doi.org/10.2307/2981647.

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Diers, Dorothea, Martin Eling, Christian Kraus, and Marc Linde. "Multi‐year non‐life insurance risk." Journal of Risk Finance 14, no. 4 (August 9, 2013): 353–77. http://dx.doi.org/10.1108/jrf-04-2013-0036.

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Pavić Kramarić, Tomislava, Maja Pervan, and Marijana Ćurak. "Determinants of Croatian Non-Life Insurance Companies’ Efficiency." Croatian operational research review 13, no. 2 (December 22, 2022): 149–60. http://dx.doi.org/10.17535/crorr.2022.0011.

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Although a relatively large number of studies have been focused on evaluating the efficiency of insurance companies from different aspects, analysis of factors that determine the achieved level of insurers’ efficiency is still in their inception. While these studies primarily encompass insurance companies operating in developed insurance markets, such research based on the sample of Croatian non-life insurers does not exist. Therefore, this paper is focused on the efficiency drivers of the insurance companies that operate in the Croatian non-life insurance market. The research is based on data for 18 insurance companies in the period from 2009 to 2021. Applying Data envelopment analysis (DEA) and Truncated regression, the research results show that age and ownership influence the efficiency of non-life insurance companies in Croatia, while the companies’ size, leverage, and product diversification are not confirmed as significant determinants of the efficiency.
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Asongu, Simplice A. "Technology, Education, Life and Non-life Insurance in Africa." International Journal of Public Administration 43, no. 11 (September 2, 2019): 915–25. http://dx.doi.org/10.1080/01900692.2019.1660994.

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Dissertations / Theses on the topic "Non-life insurance"

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Yamazato, Makoto. "Non-life Insurance Mathematics." Pontificia Universidad Católica del Perú, 2014. http://repositorio.pucp.edu.pe/index/handle/123456789/96535.

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In this work we describe the basic facts of non-life insurance and then explain risk processes. In particular, we will explain in detail the asymptotic behavior of the probability that an insurance product may end up in ruin during its lifetime. As expected, the behavior of such asymptotic probability will be highly dependent on the tail distribution of each claim.
En este artículo describimos los conceptos básicos relacionados a seguros que no sean de vida y luego explicamos procesos de riesgo. En particular, tratamos al detalle el comportamiento asintótico de la probabilidad de que un producto sea declarado en ruina. Como es suponible, el comportamiento en el horizonte depende de la cola de la distribución de las primas.
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Arvidsson, Hanna, and Sofie Francke. "Dependence in non-life insurance." Thesis, Uppsala University, Department of Mathematics, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-120621.

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Hagsjö, Renberg Oscar, and Oscar Hermansson. "Large claims in non-life insurance." Thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215492.

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It is of outmost importance for an insurance company to apply a fair pricing policy. If the price is too high, valuable customers are lost to other insurance companies while if it’s too low – it nets a negative profit. To achieve a good pricing policy, information regarding claim size history for a given type of customer is required. A problem arises as large extremal events occur and affects the claim size data. These extremal events take shape in individually large claim sizes that by themselves can alter the distribution for what certain groups of individuals are expected to cost. A remedy for this is to apply what is called a large claim limit. Any claim exceeding this limit is thought of as being outside the scope of what is captured by the original distribution of the claim size. These exceeding claims are treated separately and have their cost distributed across all insurance takers, rather than just the group they belong to. So, where exactly do you draw this limit? Do you treat the entire claim size this way (exclusion) or just the bit that is exceeding the threshold (truncation)? These questions are treated and answered in this master’s thesis for Trygg-Hansa. For each product code, a limit was achieved in addition to which method for exceeding data that was best to use.
Det är oerhört viktigt för ett försäkringsbolag att kunna tillämpa en god prissättning. Är priset för högt så förloras kunder till andra försäkringsbolag, och är den underprisad är det en förlustaffär. För att kunna sätta bra priser krävs information om vilka samt hur stora skador som kan tänkas inträffa för en given kundprofil. Ett problem uppstår när stora extremfall påverkar skadedatan. Dessa extremfall yttrar sig genom enskilda storskador som kan komma att påverka prissättningen för en hel grupp då distributionen för vad gruppen förväntas kosta kan ändras. Detta problem kan lösas genom att införa en storskadegräns till skadedatan. Skador över denna gräns räknas som extremfall och utanför ramen av vad den ursprungliga distributionen för skadorna beskriver. De hanteras separat och låter sin kostnad fördelas över samtliga försäkringstagare. Men vart dras denna gräns? Ska man behandla hela den överstigande kostnaden på detta sätt (exkludering) eller bara den biten av skadan som går över storskadegränsen (trunkering)? Dessa frågor behandlas och besvaras i denna masteruppsats i uppdrag åt Trygg-Hansa. För de olika produkttypkoderna beräknades varsin storskadegräns samt metod för överskridande data.
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Barnholdt, Jacob, and Josefin Grafford. "Predicting Large Claims within Non-Life Insurance." Thesis, KTH, Matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-228983.

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This bachelor thesis within the field of mathematical statistics aims to study the possibility of predicting specifically large claims from non-life insurance policies with commercial policyholders. This is done through regression analysis, where we seek to develop and evaluate a generalized linear model, GLM. The project is carried out in collaboration with the insurance company If P&C Insurance and most of the research is conducted at their headquarters in Stockholm. The explanatory variables of interest are characteristics associated with the policyholders. Due to the scarcity of large claims in the data set, the prediction is done in two steps. Firstly, logistic regression is used to model the probability of a large claim occurring. Secondly, the magnitude of the large claims is modelled using a generalized linear model with a gamma distribution. Two full models with all characteristics included are constructed and then reduced with computer intensive algorithms. This results in two reduced models, one with two characteristics excluded and one with one characteristic excluded.
Det här kandidatexamensarbetet inom matematisk statistik avser att studera möjligheten att predicera särskilt stora skador från sakförsäkringspolicys med företag som försäkringstagare. Detta görs med regressionsanalys, där vi ämnar att utveckla och bedöma en generaliserad linjär modell, GLM. Projektet utförs i samarbete med försäkringsbolaget If Skadeförsäkring och merparten av undersökningen sker på deras huvudkontor i Stockholm. Förklaringsvariablerna som är av intresse att undersöka är egenskaper associerade med försäkringstagarna. På grund av sällsynthet av storskador i datamängden görs prediktionen i två steg. Först används logistisk regression för att modellera sannolikheten för en storskada att inträffa. Sedan modelleras storskadornas omfattning genom en generaliserad linjär modell med en gammafördelning. Två grundmodeller med alla förklaringsvariabler konstrueras för att sedan reduceras med datorintensiva algoritmer. Det resulterar i två reducerade modeller, med två respektive en kundegenskap utesluten.
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Chitiyo, Fadzai Chitiyo. "Demand for non-life insurance: Evidence from select insurance markets in Africa." Master's thesis, University of Cape Town, 2017. http://hdl.handle.net/11427/27402.

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The impact of insurance market activity within financial development is gaining more attention in academia, as the sector experiences growth within emerging markets. This paper aims to understand which macro-economic and social variables impact the growth or decline of the non-life insurance sector broadly across Africa, with a view to provide recommendations to drive increased penetration across the region. The study examines the explanatory factors of non-life insurance demand in Africa, using annual data from 1990 to 2013 on 28 countries. Using Fixed Effects Panel Data Regression, the study finds that: levels of income, and unemployment rates have a significant negative impact on non-life insurance demand; whilst population growth rates, and the level of private sector credit (to GDP) positively impacted non-life insurance demand. Urbanisation rates, and levels of merchandise trade had statistically insignificant effects on non-life insurance demand. Since these variables only explained about 11 % of the variation in the dependent variable, the study suggests that further research into the cultural and institutional (ie. Legal / regulatory) dynamics is required to improve our understanding of what conditions would stimulate non-life insurance demand in future.
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Villaume, Erik. "Predicting customer level risk patterns in non-life insurance." Thesis, KTH, Matematisk statistik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-103590.

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Several models for predicting future customer profitability early into customer life-cycles in the property and casualty business are constructed and studied. The objective is to model risk at a customer level with input data available early into a private consumer’s lifespan. Two retained models, one using Generalized Linear Model another using a multilayer perceptron, a special form of Artificial Neural Network are evaluated using actual data. Numerical results show that differentiation on estimated future risk is most effective for customers with highest claim frequencies.
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Khan, Sheza Ally. "Determinants of the non life Insurance performance : the portuguese case." Master's thesis, Instituto Superior de Economia e Gestão, 2015. http://hdl.handle.net/10400.5/9091.

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Mestrado em Finanças
Esta dissertação tem como objetivo estudar os efeitos da crise financeira de 2008 no setor segurador não vida e determinar quais as variáveis que influenciam a rentabilidade das seguradoras não vida em Portugal, para o período compreendido entre 2004 e 2013. Para alcançar este objetivo, após revisão da literatura foi feita uma análise financeira ao setor segurador não vida e, através de duas regressões lineares, estudaram-se as variáveis que explicam o comportamento do desempenho financeiro. Os dados necessários para a realização deste estudo foram recolhidos na ASF - Autoridade de Supervisão de Seguros e Fundos de Pensões. O método escolhido para obter os resultados referentes às regressões múltiplas foi o de efeitos variáveis para o ROA - Return on Assets - e efeitos fixos para o ROE - Return on Equity - através do Gretl. A amostra inclui 150 observações de 15 seguradoras não vida, num horizonte temporal de 10 anos. Os resultados confirmaram que a estrutura financeira das companhias de seguro não-vida em Portugal é bastante sólida e que as variáveis independentes escolhidas apresentaram um comportamento de acordo com o que seria expectável. De acordo com os resultados finais obtidos, os determinantes do desempenho financeiro no mercado de segurador não vida Português são a idade da empresa, o crescimento dos prémios, taxa de sinistralidade, tangibilidade e índice de competência de gestão.
This dissertation aims to study the effects of the financial crisis of 2008 on the non-life insurance industry and to determine which variables influence the profitability of non-life insurers in Portugal, for the period between 2004 and 2013. To accomplish this, we reviewed the literature and made a financial analysis of the non-life insurance industry in order to, through two linear regressions, be able to study the variables that explain the behaviour of the financial performance. The data required for this study were collected in the ASF - Autoridade de Supervisão de Seguros e Fundos de Pensões. The method chosen for the results of the multiple regressions was the random effects on ROA - Return on Assets - and fixed effects for the ROE - Return on Equity - through Gretl. The sample includes 150 observations of 15 non-life insurers, through a time horizon of 10 years. The results confirmed that the financial structure of the non-life insurance companies in Portugal is rock solid, and that the chosen independent variables behaved according to what would be expected. According to our final results, the determinants of the financial performance in the Portuguese non-life insurance market are company age, premium growth, loss ratio, tangibility and management competence index.
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Gschlössl, Susanne. "Hierarchical Bayesian spatial regression models with applications to non-life insurance." [S.l.] : [s.n.], 2006. http://deposit.ddb.de/cgi-bin/dokserv?idn=978924576.

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Björkwall, Susanna. "Stochastic claims reserving in non-life insurance : Bootstrap and smoothing models." Doctoral thesis, Stockholms universitet, Matematiska institutionen, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-55347.

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In practice there is a long tradition of actuaries calculating reserve estimates according to deterministic methods without explicit reference to a stochastic model. For instance, the chain-ladder was originally a deterministic reserving method. Moreover, the actuaries often make ad hoc adjustments of the methods, for example, smoothing of the chain-ladder development factors, in order to fit the data set under analysis. However, stochastic models are needed in order to assess the variability of the claims reserve. The standard statistical approach would be to first specify a model, then find an estimate of the outstanding claims under that model, typically by maximum likelihood, and finally the model could be used to find the precision of the estimate. As a compromise between this approach and the actuary's way of working without reference to a model the object of the research area has often been to first construct a model and a method that produces the actuary's estimate and then use this model in order to assess the uncertainty of the estimate. A drawback of this approach is that the suggested models have been constructed to give a measure of the precision of the reserve estimate without the possibility of changing the estimate itself. The starting point of this thesis is the inconsistency between the deterministic approaches used in practice and the stochastic ones suggested in the literature. On one hand, the purpose of Paper I is to develop a bootstrap technique which easily enables the actuary to use other development factor methods than the pure chain-ladder relying on as few model assumptions as possible. This bootstrap technique is then extended and applied to the separation method in Paper II. On the other hand, the purpose of Paper III is to create a stochastic framework which imitates the ad hoc deterministic smoothing of chain-ladder development factors which is frequently used in practice.
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Hahn, Lukas [Verfasser]. "Quantitative assessment of multi-year non-life insurance risk / Lukas Hahn." Ulm : Universität Ulm, 2019. http://d-nb.info/119313966X/34.

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Books on the topic "Non-life insurance"

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Non-life insurance mathematics. Berlin: Springer-Verlag, 1988.

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Straub, Erwin. Non-life insurance mathematics. 2nd ed. Berlin: Springer, 1997.

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Actuaries, Association of Swiss, ed. Non-life insurance mathematics. Berlin: Springer-Verlag, 1988.

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Mikosch, Thomas. Non-Life Insurance Mathematics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-88233-6.

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Straub, Erwin. Non-Life Insurance Mathematics. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-662-03364-7.

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Tiopianco, Cesario P. New insurance reviewer: Life & non-life. Metro Manila, Philippines: National Book Store, 1986.

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Claims reserving in non-life insurance. Amsterdam: North-Holland, 1986.

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Sundt, Bjørn. An introduction to non-life insurance mathematics. 3rd ed. Karlsruhe: VVW, 1993.

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Japan. Japanese insurance laws and Japanese non-life insurance agency contract forms. Tokyo, Japan: The Non-Life Insurance Institute of Japan, 1986.

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Ohlsson, Esbjörn, and Björn Johansson. Non-Life Insurance Pricing with Generalized Linear Models. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-10791-7.

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Book chapters on the topic "Non-life insurance"

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Ohlsson, Esbjörn, and Björn Johansson. "Non-Life Insurance Pricing." In EAA Lecture Notes, 1–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-10791-7_1.

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Cipra, Tomas. "Classical Non-Life Insurance." In Financial and Insurance Formulas, 219–34. Heidelberg: Physica-Verlag HD, 2010. http://dx.doi.org/10.1007/978-3-7908-2593-0_21.

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Straub, Erwin. "Problems." In Non-Life Insurance Mathematics, 1–6. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-662-03364-7_1.

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Straub, Erwin. "Tools." In Non-Life Insurance Mathematics, 7–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-662-03364-7_2.

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Straub, Erwin. "Premiums." In Non-Life Insurance Mathematics, 52–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-662-03364-7_3.

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Straub, Erwin. "Reinsurance." In Non-Life Insurance Mathematics, 68–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-662-03364-7_4.

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Straub, Erwin. "Retentions." In Non-Life Insurance Mathematics, 76–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-662-03364-7_5.

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Straub, Erwin. "Statistics." In Non-Life Insurance Mathematics, 89–101. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-662-03364-7_6.

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Straub, Erwin. "Reserves." In Non-Life Insurance Mathematics, 102–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-662-03364-7_7.

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Straub, Erwin. "Solutions." In Non-Life Insurance Mathematics, 116–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-662-03364-7_8.

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Conference papers on the topic "Non-life insurance"

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A.Salem Alghusin, Nawaf, and Hamad A.Kasasbeh. "The Drivers of Demand for Non-Life Insurance." In 2nd International Conference on Advanced Research in Business, Management and Economics. GLOBALKS, 2019. http://dx.doi.org/10.33422/2nd.icabme.2019.12.885.

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Stepchenko, Darja, and Irina Voronova. "Investigation Of Insurance Company Financial Stability: Case Of Baltic Non-Life Insurance Market." In The 8th International Scientific Conference "Business and Management 2014". Vilnius, Lithuania: Vilnius Gediminas Technical University Publishing House Technika, 2014. http://dx.doi.org/10.3846/bm.2014.042.

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Yap, Shen, Hui-Shan Lee, and Ping-Xin Liew. "THE INTEGRATION OF LIFE AND NON-LIFE INSURANCE IN FINANCIAL INCLUSION INDEX." In 16th Economics & Finance Conference, Prague. International Institute of Social and Economic Sciences, 2022. http://dx.doi.org/10.20472/efc.2022.016.016.

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Ng, S., D. Lestari, and S. Devila. "Generalized linear model for deductible pricing in non-life insurance." In PROCEEDINGS OF THE 4TH INTERNATIONAL SYMPOSIUM ON CURRENT PROGRESS IN MATHEMATICS AND SCIENCES (ISCPMS2018). AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5132465.

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Samsudin, Humaida Banu. "Failure factors in non-life insurance companies in United Kingdom." In PROCEEDINGS OF THE 20TH NATIONAL SYMPOSIUM ON MATHEMATICAL SCIENCES: Research in Mathematical Sciences: A Catalyst for Creativity and Innovation. AIP, 2013. http://dx.doi.org/10.1063/1.4801258.

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Nastase, Adriana. "Calculation models of net risk premiums for RCA insurance contracts." In International Scientific-Practical Conference "Economic growth in the conditions of globalization". National Institute for Economic Research, 2023. http://dx.doi.org/10.36004/nier.cecg.iii.2023.17.34.

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Starting from the fundamental role of insurance, that of providing protection by the insurer to the person interested in concluding an insurance contract (the insured) in exchange for the insurance premium (insurance price), advanced mathematical models have been developed for the appropriate determination of the insurance price. In Romania, RCA insurance is one of the most regulated insurances due to the importance it occupies from the point of view of the volume of gross premiums subscribed. In the total of gross premiums written in Romania, both general and life insurance, RCA insurance holds on average in the period 2014-2022 approximately 47.5%, hence the motivation for this research topic in order to establish the most appropriate premium for the insured RCA portfolio. In the case of non-life insurance, the estimation of the pure insurance premium is carried out with the help of generalized linear models (GLM). The segmentation of the insured portfolio into homogeneous risk classes is more than necessary for choosing the optimal explanatory variables, which reproduce the behavior of the response variable as accurately as possible.
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Chin-Huang Lin, Ho-Li Yang, and Dian-Yan Liou. "Service innovation efficiency evaluation on non-life insurance industry in Taiwan." In 2007 IEEE International Conference on Industrial Engineering and Engineering Management. IEEE, 2007. http://dx.doi.org/10.1109/ieem.2007.4419533.

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Ji Caihong. "Notice of Retraction: Analysis on operational risk of Non-life Insurance companies." In Business Management and Electronic Information. 2011 International Conference on Business Management and Electronic Information (BMEI 2011). IEEE, 2011. http://dx.doi.org/10.1109/icbmei.2011.5920347.

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Zhang, Jiaping, and Xiaojian Yu. "On Measuring Non-Life Insurance Output in China Using the Expectation Approach." In 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). IEEE, 2008. http://dx.doi.org/10.1109/wicom.2008.2041.

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Peovski, Filip, and Igor Ivanovski. "Is the Claims Ratio Dynamic Predictable? A Study of the Macedonian Non-Life Insurance Sector." In Economic and Business Trends Shaping the Future. Ss Cyril and Methodius University, Faculty of Economics-Skopje, 2023. http://dx.doi.org/10.47063/ebtsf.2023.0009.

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Insurance, simplified to a risk management concept, offers protection against unexpected losses arising from adverse events. As a financial service of structural importance in modern economies, it requires specific attention due to its risk protection component. Consequently, insurance companies devote sufficient resources towards stable, resilient, and profitable operations. However, a scarce amount of research treats the topic of financial stability maintenance and claims management with even fewer studies dealing with its prediction. This study deals with claims ratio (CR) dynamics in the Macedonian non-life insurance sector and its prediction. Utilizing a data set of 138 monthly observations between January 2012 and June 2023, this paper models the CR indicator through multiple approaches i.e., naïve, ARIMA, ETS exponential smoothing, and random forest (RF) thus making suitability and accuracy comparisons. Results suggest that the SARIMA(4,0,2)(2,1,2,12) model is superior in predicting the claims ratio in both the training and test samples. Moreover, the random forest algorithm shows good performances in the test set but is only superior to the ETS(A,N,A) model.
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Reports on the topic "Non-life insurance"

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De Donder, Philippe, Marie-Louise Leroux, and François Salanié. Advantageous selection without moral hazard. CIRANO, May 2022. http://dx.doi.org/10.54932/nqvt3458.

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Advantageous (or propitious) selection occurs when an increase in the premium of an insurance contract induces high-cost agents to quit, thereby reducing the average cost among remaining buyers. Hemenway (1990) and many subsequent contributions motivate its advent by differences in risk-aversion among agents, implying different prevention efforts. We argue that it may also appear in the absence of moral hazard, when agents only differ in riskiness and not in (risk) preferences. We first show that profit-maximization implies that advantageous selection is more likely when markup rates and the elasticity of insurance demand are high. We then move to standard settings satisfying the single-crossing property and show that advantageous selection may occur when several contracts are offered, when agents also face a non-insurable background risk, or when agents face two mutually exclusive risks that are bundled together in a single insurance contract. We exemplify this last case with life care annuities, a product which bundles long-term care insurance and annuities, and we use Canadian survey data to provide an example of a contract facing advantageous selection.
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