Relevant bibliographies by topics / Claims reserving

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

Academic literature on the topic 'Claims reserving'

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Published: 4 June 2021
Last updated: 30 January 2023

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Journal articles on the topic "Claims reserving"

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Hesselager, Ole. "A Markov Model for Loss Reserving." ASTIN Bulletin 24, no. 2 (November 1994): 183–93. http://dx.doi.org/10.2143/ast.24.2.2005064.

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AbstractThe claims generating process for a non-life insurance portfolio is modelled as a marked Poisson process, where the mark associated with an incurred claim describes the development of that claim until final settlement. An unsettled claim is at any point in time assigned to a state in some state-space, and the transitions between different states are assumed to be governed by a Markovian law. All claims payments are assumed to occur at the time of transition between states. We develop separate expressions for the IBNR and RBNS reserves, and the corresponding prediction errors.
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Jessen, Anders Hedegaard, and Niels Rietdorf. "Diagonal effects in claims reserving." Scandinavian Actuarial Journal 2011, no. 1 (March 2011): 21–37. http://dx.doi.org/10.1080/03461230903301876.

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Renshaw, A. "Claims reserving by joint modelling." Insurance: Mathematics and Economics 17, no. 3 (April 1996): 239–40. http://dx.doi.org/10.1016/0167-6687(96)82389-4.

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Dupin, Gilles, Emmanuel Koenig, Pierre Le Moine, Alain Monfort, and Eric Ratiarison. "COHERENT INCURRED PAID (CIP) MODELS FOR CLAIMS RESERVING." ASTIN Bulletin 48, no. 02 (December 18, 2017): 749–77. http://dx.doi.org/10.1017/asb.2017.36.

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AbstractIn this paper, we first propose a statistical model, called the Coherent Incurred Paid model, to predict future claims, using simultaneously the information contained in incurred and paid claims. This model does not assume log-normality of the levels (or normality of the growth rates) and is semi-parametric since it only specifies the first and the second moments; however, in order to evaluate the impact of the normality assumption, we also propose a benchmark Gaussian version of our model. Correlations between growth rates of incurred and paid claims are allowed and the tail development period is estimated. We also provide methods for computing the Claim Development Results and their Values at Risk in the semi-parametric framework. Moreover, we show how to take into account the updating of the estimation in the computation of the Claim Development Results. An application highlights the practical importance of relaxing the normality assumption and of updating the estimation of the parameters.
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Gabrielli, Andrea, and Mario V. Wüthrich. "Back-testing the chain-ladder method." Annals of Actuarial Science 13, no. 2 (November 13, 2018): 334–59. http://dx.doi.org/10.1017/s1748499518000325.

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AbstractThe chain-ladder method is one of the most popular claims reserving techniques. The aim of this study is to back-test the chain-ladder method. For this purpose, we use a stochastic scenario generator that allows us to simulate arbitrarily many upper claims reserving triangles of similar characteristics for which we also know the corresponding lower triangles. Based on these simulated triangles, we analyse the performance of the chain-ladder claims reserving method.
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Mack, Thomas. "A Simple Parametric Model for Rating Automobile Insurance or Estimating IBNR Claims Reserves." ASTIN Bulletin 21, no. 1 (April 1991): 93–109. http://dx.doi.org/10.2143/ast.21.1.2005403.

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AbstractIt is shown that there is a connection between rating in automobile insurance and the estimation of IBNR claims amounts because automobile insurance tariffs are mostly cross-classified by at least two variables (e.g. territory and driver class) and IBNR claims run-off triangles are always cross-classified by the two variables accident year and development year. Therefore, by translating the most well-known automobile rating methods into the claims reserving situation, some known and some unknown claims reserving methods are obtained. For instance, the automobile rating method of Bailey and Simon produces a new claims reserving method, whereas the model leading to the rating method called “marginal totals” produces the well-known IBNR claims estimation method called “chain ladder”. A drawback of this model is the fact that it is designed for the number of claims and not for the total claims amount for which it is usually applied.As an alternative for both, rating and claims reserving, we describe a simple but realistic parametric model for the total claims amount which is based on the Gamma distribution and has the advantage of providing the possibility of assessing the goodness-of-fit and calculating the estimation error. This method is not very well known in automobile insurance—although a satisfactory application is reported—and seems to be completely unknown in the field of claims reserving, although its execution is nearly as simple as that of the chain ladder method.
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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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Wüthrich, Mario V. "Machine learning in individual claims reserving." Scandinavian Actuarial Journal 2018, no. 6 (January 24, 2018): 465–80. http://dx.doi.org/10.1080/03461238.2018.1428681.

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England, P. D., and R. J. Verrall. "Stochastic Claims Reserving in General Insurance." British Actuarial Journal 8, no. 3 (August 1, 2002): 443–518. http://dx.doi.org/10.1017/s1357321700003809.

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ABSTRACTThis paper considers a wide range of stochastic reserving models for use in general insurance, beginning with stochastic models which reproduce the traditional chain-ladder reserve estimates. The models are extended to consider parametric curves and smoothing models for the shape of the development run-off, which allow extrapolation for the estimation of tail factors. The Bornhuetter-Ferguson technique is also considered, within a Bayesian framework, which allows expert opinion to be used to provide prior estimates of ultimate claims. The primary advantage of stochastic reserving models is the availability of measures of precision of reserve estimates, and in this respect, attention is focused on the root mean squared error of prediction (prediction error). Of greater interest is a full predictive distribution of possible reserve outcomes, and different methods of obtaining that distribution are described. The techniques are illustrated with examples throughout, and the wider issues discussed, in particular, the concept of a ‘best estimate’; reporting the variability of claims reserves; and use in dynamic financial analysis models.
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Verrall, Richard. "Claims reserving and generalised additive models." Insurance: Mathematics and Economics 19, no. 1 (December 1996): 31–43. http://dx.doi.org/10.1016/s0167-6687(96)00000-5.

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Dissertations / Theses on the topic "Claims reserving"

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Johansson, Annelie. "Claims Reserving on Macro- and Micro-Level." Thesis, KTH, Matematisk statistik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-173113.

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Three methods for claims reserving are compared on two data sets. The first two methods are the commonly used chain ladder method that uses aggregated payments and the relatively new method, double chain ladder, that apart from the payments data also uses the number of reported claims. The third method is more advanced, data on micro-level is needed such as the reporting delay and the number of payment periods for every single claim. The two data sets that are used consist of claims with typically shorter and longer settlement time, respectively. The questions considered are if you can gain anything from using a method that is more advanced than the chain ladder method and if the gain differs from the two data sets. The methods are compared by simulating the reserves distributions as well as comparing the point estimates of the reserve with the real out-of-sample reserve. The results show that there is no gain in using the micro-level method considered. The double chain lad- der method on the other hand performs better than the chain ladder method. The difference between the two data sets is that the reserve in the data set with longer settlement times is harder to estimate, but no difference can be seen when it comes to method choice.
Tre reservsättningsmetoder jämförs på två dataset. De första två metoderna är den välkända chain ladder-metoden som använder sig av aggregerade utbetalningar samt den relativt nya metoden double chain ladder som förutom utbetalningarna använder sig av antalet anmälda skador. Den tredje metoden baseras på mikro-nivå och kräver information om varje enskild skada, såsom anmälningstid och antalet utbetalningsperioder. De två dataseten som används är ett som innehåller skador med typiskt kortare avvecklingstider och ett som innehåller skador med typiskt längre avvecklingstider. Frågorna som behandlas är om man vinner något på att använda en mer avancerad metod än chain ladder och om det skiljer sig åt mellan dataseten. Metoderna jämförs genom simulering av reserven, men också genom att jämföra punktskattningar med den verkliga reserven. Resultaten visar att man I detta fall inte vinner något på att använda mikro-metoden. Double chain ladder å andra sidan presterar bättre än chain ladder. Skillnaden mellan de två dataseten är att det är svårare att estimera reserven när avvecklingstiden är längre, men ingen skillnad ses när det gäller val av metod
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Ahlgren, Marcus. "Claims Reserving using Gradient Boosting and Generalized Linear Models." Thesis, KTH, Matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229406.

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One fundamental function of an insurance company revolves around calculating the expected claims costs for which the insurer has to compensate its policyholders for. This is the process of claims reserving which is practised by actuaries using statistical methods. Over the last few decades statistical learning methods have become increasingly popular due to their ability to find complex patterns in any type of data. However, they have not been widely adapted within the insurance sector. In this thesis we evaluate the capability of claims reserving with the method of gradient boosting, a non-parametric statistical learning method that has proven to be successful within multiple other disciplines which has made it very popular. The gradient boosting technique is compared with the generalized linear model(GLM) which is widely used for modelling claims. We compare the models by using a claims data set provided by Länsförsäkringar AB which allows us to train the models and evaluate their performance on data not yet seen by the models. The models were implemented using R. The results show that the GLM has a lower prediction error. Also, the gradient boosting method requires more fine tuning to handle claims data properly while the GLM already possesses certain features that makes it suitable for claims reserving without making as many adjustments in the model implementation. The advantage of capturing complex dependencies in data is not fully utilized in this thesis since we only work with 6 predictor variables. It is more likely that gradient boosting can compete with GLM when predicting more complicated claims.
En av de centrala verksamheterna ett försäkringsbolag arbetar med handlar om att uppskatta skadekostnader för att kunna ersätta försäkringstagarna. Denna procedur kallas reservsättning och utförs av aktuarier med hjälp av statistiska metoder. Under de senaste årtiondena har statistiska inlärningsmetoder blivit mer och mer populära tack vare deras förmåga att hitta komplexa mönster i alla typer av data. Dock har intresset för dessa varit relativt lågt inom försäkringsbranschen till förmån för mer traditionella försäkringsmatematiska metoder. I den här masteruppsatsen undersöker vi förmågan att reservsätta med metoden \textit{gradient boosting}, en icke-parametrisk statistisk inlärningsmetod som har visat sig fungera mycket väl inom en rad andra områden vilket har gjort metoden mycket populär. Vi jämför denna metod med generaliserade linjära modeller(GLM) som är en av de vanliga metoderna vid reservsättning. Vi jämför modellerna med hjälp av ett dataset tillhandahålls av Länsförsäkringar AB. Modellerna implementerades med R. 80\% av detta dataset används för att träna modellerna och resterande 20\% används för att evaluera modellernas prediktionsförmåga på okänd data. Resultaten visar att GLM har ett lägre prediktionsfel. Gradient boosting kräver att ett antal hyperparametrar justeras manuellt för att få en välfungerande modell medan GLM inte kräver lika mycket korrigeringar varför den är mer praktiskt lämpad. Fördelen med att kunna modellerna komplexa förhållanden i data utnyttjas inte till fullo i denna uppsats då vi endast arbetar med sex prediktionsvariabler. Det är sannolikt att gradient boosting skulle ge bättre resultat med mer komplicerade datastrukturer.​
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Mann, Eric M. "A comparison of stochastic claim reserving methods." Kansas State University, 2011. http://hdl.handle.net/2097/13125.

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Master of Science
Department of Statistics
Haiyan Wang
Estimating unpaid liabilities for insurance companies is an extremely important aspect of insurance operations. Consistent underestimation can result in companies requiring more reserves which can lead to lower profits, downgraded credit ratings, and in the worst case scenarios, insurance company insolvency. Consistent overestimation can lead to inefficient capital allocation and a higher overall cost of capital. Due to the importance of these estimates and the variability of these unpaid liabilities, a multitude of methods have been developed to estimate these amounts. This paper compares several actuarial and statistical methods to determine which are relatively better at producing accurate estimates of unpaid liabilities. To begin, the Chain Ladder Method is introduced for those unfamiliar with it. Then a presentation of several Generalized Linear Model (GLM) methods, various Generalized Additive Model (GAM) methods, the Bornhuetter-Ferguson Method, and a Bayesian method that link the Chain Ladder and Bornhuetter-Ferguson methods together are introduced, with all of these methods being in some way connected to the Chain Ladder Method. Historical data from multiple lines of business compiled by the National Association of Insurance Commissioners is used to compare the methods across different loss functions to gain insight as to which methods produce estimates with the minimum loss and to gain a better understanding of the relative strengths and weaknesses of the methods. Key
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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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Liu, Huijuan. "Stochastic claims reserving for methods which combine information from multiple data sets." Thesis, City University London, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.492349.

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This thesis is concerned with the approximations of prediction error and predictive distribution of the best reserve estimate produced by the models which combine information from multiple data sets. Two models are studied . ~; within the GLM framework, i.e. SClmieper's: model proposed by Schnieper (1991) and the MeL method introduced by Quargand Mack (2004). Theoretical and empirical approximation approaches for the MSEP of these two models are discussed and compared. This includes derivations of closed formulae following the approaches introduced by· both Mack (1993) and Murphy (1994) and also the empirical approach, i.e. the bootstrap method. And finally, various models which combine information from multiple data sets are investigated and compared, providing new insights to the claims .reserving area.
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Verrall, Richard John. "Stochastic models for triangular tables with applications to cohort data and claims reserving." Thesis, City University London, 1989. http://openaccess.city.ac.uk/7407/.

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Stochastic models for triangular data are derived and applied to claims reserving data. The standard actuarial technique, the so-called "chain-ladder technique" is given a sound statistical foundation and considered as a linear model. This linear model, the '"Chain Ladder Linear Model" is extended to encompass Bayesian, empirical Bayes and dynamic estimation. The empirical Bayes results are given a credibility theory interpretation, and the advantages and disadvantages of the various approaches are highlighted. Finally, the methods are extended to two-dimensional systems and results based on classical time series and Kalman filtering theory are produced. The empirical Bayes estimation results are very useful, practically, and can be compared to the Kalman filter estimates. They have the advantage that no prior information is required: the Kalman filter method requires the state and observation variances to be specified. For illustration purposes the estimates from the empirical Bayes procedure are used. The empirical Bayes results can also be compared with credibility theory estimators, although they retain the general statistical advantages of the linear modelling approach. For the classical theory, unbiased estimates of outstanding claims, reserves and variances are derived, and prediction intervals for total outstanding claims are produced. Maximum likelihood theory is utilised to derive the distributions of quantities relating to the column parameters which have actuarial interpretations. The row totals are also considered. Bayesian estimates of similar quantities are derived for the methods based on Bayes theory.
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Barnouski, Jebidiah Lee. "Using one-year claim development to chose a large claim reserving technique." Master's thesis, Instituto Superior de Economia e Gestão, 2016. http://hdl.handle.net/10400.5/11589.

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Mestrado em Ciências Actuariais
Neste relatório será também explicado ao seu leitor o que é que está na base da separação dos sinistros graves dos restantes, bem como dos métodos frequentemente utilizados para o cálculo de reservas para sinistros com danos corporais. Será também exposto o processo de gestão de sinistros graves actualmente utilizado em Portugal e nas restantes sucursais da Liberty. Para chegar à conclusão de qual o melhor método a sugerir serão primeiramente completados os triângulos tanto de pagamentos ocorridos como de frequências esperadas. As reservas agregadas serão depois obtidas utilizando três métodos diferentes, Chain Ladder, Cape Cod e Benktander. Por forma a estimar o desvio padrão associado a cada método utilizado serão simulados diferentes cenários e calculada a diferença entre as reservas agregadas obtidas para 2014 e 2015 (exceptuando o período de 2015 correspondente ao qual em 2014 não foi possível recolher valores de reservas). A esta técnica é dado o nome de OCD, isto é, The One-Year Claim Development. De entre os três métodos utilizados será eleito aquele cuja medida de sensibilidade para um ano (média dos desvios padrão obtidos) for menor, e consequentemente considerar-se-á esse o melhor método para tratar sinistros graves em Portugal. Devo ainda referir que a leitura e compreensão deste relatório pressupõe o conhecimento prévio das bases da actividade seguradora no ramo automóvel, bem como do processo de cálculo de reservas matemáticas.
This report will arrive at a conclusion by explaining to the reader the basic reasoning behind splitting large claims as well as the most common methods for BI reserving. It is assumed that the reader has fundamental understanding of the insurance industry, motor insurance and BI, and the reserving process. It is necessary to explain the practices used by Portugal and other Liberty International countries to form an opinion of those practices by applying them to Portugal's claim information. Furthermore, the question of whether to split large claims or not will be thoroughly evaluated. Finally, there will be an aggregate suggestion as to the best splitting practice and reserving methodology specific for Liberty Seguros Portugal. To do this, several shocked scenarios will be simulated. Additional large claims will be introduced to the total incurred claims triangle and large claim count triangles. The one-year claim development (OCD) will then be compared using different reserving methodologies, the Chain Ladder Method, Cape Cod Method, and Benktander Method. The one-year claim development is measured by the change in the aggregate reserve ultimate between 2014 and 2015 (excluding the 2015 cohort for which no aggregate reserve ultimate was available in 2014). The standard deviation of each method's one-year uncertainty will be calculated by computing the OCD of each method under the three shocked scenarios. The technique that yields the lowest average of standard deviations, called the one-year sensitivity measure by the author, will be selected as the best approach for handling large claims.
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Happ, Sebastian Verfasser], and Michael [Akademischer Betreuer] [Merz. "Stochastic Claims Reserving under Consideration of Various Different Sources of Information / Sebastian Happ. Betreuer: Michael Merz." Hamburg : Staats- und Universitätsbibliothek Hamburg, 2014. http://d-nb.info/1053811489/34.

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Sloma, Przemyslaw. "Contribution to the weak convergence of empirical copula process : contribution to the stochastic claims reserving in general insurance." Thesis, Paris 6, 2014. http://www.theses.fr/2014PA066563/document.

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Dans la première partie de la thèse, nous nous intéressons à la convergence faible du processus empirique pondéré des copules. Nous fournissons la condition suffisante pour que cette convergence ait lieu vers un processus Gaussien limite. Nos résultats sont obtenus dans un espace de Banach L^p. Nous donnons des applications statistiques de ces résultats aux tests d'adéquation (tests of goodness of fit) pour les copules. Une attention spéciale est portée aux tests basées sur des statistiques de type Cramér-von Mises.Dans un second temps, nous étudions le problème de provisionnement stochastique pour une compagnie d'assurance non-vie. Les méthodes stochastiques sont utilisées afin d'évaluer la variabilité des réserves. Le point de départ pour cette thèse est une incohérence entre les méthodes utilisées en pratique et celles publiées dans la littérature. Pour remédier à cela, nous présentons un outil général de provisionnement stochastique à horizon ultime (Chapitre 3) et à un an (Chapitre 4), basé sur la méthode Chain Ladder
The aim of this thesis is twofold. First, we concentrate on the study of weak convergence of weighted empirical copula processes. We provide sufficient conditions for this convergence to hold to a limiting Gaussian process. Our results are obtained in the framework of convergence in the Banach space $L^{p}$ ($1\leq p <\infty $). Statistical applications to goodness of fit (GOF) tests for copulas are given to illustrate these results. We pay special attention to GOF tests based on Cramér-von Mises type statistics. Second, we discuss the problem of stochastic claims reserving in general non-life insurance. Stochastic models are needed in order to assess the variability of the claims reserve. The starting point of this thesis is an observed inconsistency between the approaches used in practice and that suggested in the literature. To fill this gap, we present a general tool for measuring the uncertainty of reserves in the framework of ultimate (Chapter 3) and one-year time horizon (Chapter 4), based on the Chain-Ladder method
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Moerup, Casper Jacob. "Prediction of claim cost in general insurance." Master's thesis, Instituto Superior de Economia e Gestão, 2019. http://hdl.handle.net/10400.5/18176.

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Mestrado em Actuarial Science
O trabalho seguinte foi realizado durante uma colocação de estágio na If Industrial P & C Insurance, em Estocolmo, na Suécia. Este relatório destaca e discute algumas das diferenças entre o seguro industrial e privado e percorre o processo de “Análise do Ano Normal”. A análise avalia os dados das reivindicações com o objetivo de projetar as perdas em um ano no futuro. A Teoria do Risco Colectivo e a Estimação da Máxima Verossimilhança são utilizadas para obter uma estimativa da gravidade das reivindicações. Além disso, as reservas são estimadas usando o método Chain-ladder. A seção final do relatório descreve uma análise de sensibilidade de um modelo para as reservas de ajuste de sinistros. Esta análise mostra o impacto da introdução de dois novos parâmetros, o que explica a parte já desenvolvida das reivindicações abertas.
The following work was carried out during an internship placement at If Industrial P&C Insurance in Stockholm, Sweden. This report highlights and discusses some of the differences between Industrial and Private insurance and walks through the “Normal Year Analysis”-procedure. The analysis assesses the claims data with the goal of projecting the losses one year into the future. Collective Risk Theory and Maximum Likelihood Estimation is used to obtain an estimate of the severity of the claims. In addition, the reserves are estimated, using the Chain-ladder method. The final section of the report describes a sensitivity analysis of a model for the Claims Adjustment Reserves. This analysis shows the impact of introducing two new parameters, which accounts for the already developed part of the open claims.
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Books on the topic "Claims reserving"

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

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Wüthrich, Mario V., and Michael Merz, eds. Stochastic Claims Reserving Methods in Insurance. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781119206262.

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Michael, Merz, ed. Stochastic claims reserving methods in insurance. Chichester: John Wiley & Sons, 2008.

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Hesselager, Ole. On models and methods in claims reserving. Copenhagen: University of Copenhagen, 1988.

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Gao, Guangyuan. Bayesian Claims Reserving Methods in Non-life Insurance with Stan. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-3609-6.

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Claims Reserving in General Insurance. Cambridge University Press, 2017.

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Hindley, David. Claims Reserving in General Insurance. Cambridge University Press, 2017.

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Stochastic Claims Reserving Methods in Insurance. Wiley & Sons, Incorporated, John, 2008.

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Merz, Michael, and Mario V. Wüthrich. Stochastic Claims Reserving Methods in Insurance. Wiley & Sons, Limited, John, 2015.

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Thrich, Mario V., and Michael Merz. Stochastic Claims Reserving Methods in Insurance. Wiley & Sons, Incorporated, John, 2008.

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Book chapters on the topic "Claims reserving"

1

Schmidli, Hanspeter. "Claims Reserving." In Risk Theory, 71–82. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-72005-0_4.

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Castellani, Gilberto, Massimo De Felice, and Franco Moriconi. "Claims Reserving in Non-life Insurance: A Fully Bayesian Model." In Communications in Computer and Information Science, 134–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31724-8_15.

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Forte, Salvatore, Matteo Ialenti, and Marco Pirra. "Claims reserving uncertainty in the development of internal risk models." In Mathematical and Statistical Methods for Actuarial Sciences and Finance, 203–10. Milano: Springer Milan, 2012. http://dx.doi.org/10.1007/978-88-470-2342-0_24.

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Gao, Guangyuan. "Introduction." In Bayesian Claims Reserving Methods in Non-life Insurance with Stan, 1–8. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-3609-6_1.

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Gao, Guangyuan. "Bayesian Fundamentals." In Bayesian Claims Reserving Methods in Non-life Insurance with Stan, 9–33. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-3609-6_2.

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Gao, Guangyuan. "Advanced Bayesian Computation." In Bayesian Claims Reserving Methods in Non-life Insurance with Stan, 35–71. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-3609-6_3.

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Gao, Guangyuan. "Bayesian Chain Ladder Models." In Bayesian Claims Reserving Methods in Non-life Insurance with Stan, 73–115. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-3609-6_4.

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Gao, Guangyuan. "Bayesian Basis Expansion Models." In Bayesian Claims Reserving Methods in Non-life Insurance with Stan, 117–52. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-3609-6_5.

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Gao, Guangyuan. "Multivariate Modelling Using Copulas." In Bayesian Claims Reserving Methods in Non-life Insurance with Stan, 153–83. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-3609-6_6.

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Gao, Guangyuan. "Epilogue." In Bayesian Claims Reserving Methods in Non-life Insurance with Stan, 185–90. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-3609-6_7.

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Conference papers on the topic "Claims reserving"

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Heberle, Jochen, and Anne Thomas. "Combining chain-ladder claims reserving with fuzzy numbers." In 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS). IEEE, 2013. http://dx.doi.org/10.1109/ifsa-nafips.2013.6608383.

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Faluközy, Tamás, Ildikó Ibolya Vitéz, and Miklós Arató. "Stochastic models for claims reserving in insurance business." In Recent Advances in Stochastic Modeling and Data Analysis. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812709691_0013.

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Kartikasari, Mujiati Dwi, Adhitya Ronnie Effendie, and Yuciana Wilandari. "Reserving by detailed conditioning on individual claim." In STATISTICS AND ITS APPLICATIONS: Proceedings of the 2nd International Conference on Applied Statistics (ICAS II), 2016. Author(s), 2017. http://dx.doi.org/10.1063/1.4979428.

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Raeva, Elitsa, Velizar Pavlov, and Simona Georgieva. "Claim reserving estimation by using the chain ladder method." In SEVENTH INTERNATIONAL CONFERENCE ON NEW TRENDS IN THE APPLICATIONS OF DIFFERENTIAL EQUATIONS IN SCIENCES (NTADES 2020). AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0040192.

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DE ANDRES-SÁNCHEZ, JORGE. "CLAIM RESERVING WITH FUZZY REGRESSION AND THE TWO WAYS OF ANOVA." In Proceedings of the MS'10 International Conference. WORLD SCIENTIFIC, 2010. http://dx.doi.org/10.1142/9789814324441_0017.

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Ma, LiHua. "The Potential of Remaining Oil and Exploration Methods of the Second Class Reservior." In 2017 2nd International Conference on Materials Science, Machinery and Energy Engineering (MSMEE 2017). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/msmee-17.2017.10.

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Chen, Jiao, Yuan Li, Jianfeng Yu, and Wenbin Tang. "Profile Tolerances Modeling: A Unified Framework for Representing Geometric Variations for Line Profiles." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-51154.

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Abstract:
Tolerance modeling is the most basic issue in Computer Aided Tolerancing (CAT). It will negatively influence the performance of subsequent activities such as tolerance analysis to a great extent if the resultant model cannot accurately represent variations in tolerance zone. According to ASME Y14.5M Standard [1], there is a class of profile tolerances for lines and surfaces which should also be interpreted correctly. Aim at this class of tolerances, the paper proposes a unified framework called DOFAS for representing them which composed of three parts: a basic DOF (Degrees of Freedom) model for interpreting geometric variations for profiles, an assessment method for filtering out and rejecting those profiles cannot be accurately represented and a split algorithm for splitting rejected profiles into sub profiles to make their variations interpretable. The scope of discussion in this paper is restricted to the line profiles; we will focus on the surface profiles in forthcoming papers. From the DOF model, two types of errors result from the rotations of the features are identified and formulized. One type of the errors is the result of the misalignment between profile boundary and tolerance zone boundary (noted as type 1); and if the feature itself exceeds the range of tolerance zone the other type of errors will form (noted as type 2). Specifically, it is required that the boundary points of the line profile should align with the corresponding boundary lines of the tolerance zone and an arbitrary point of the line profile should lie within the tolerance zone when line profile rotates in the tolerance zone. To make DOF model as accurate as possible, an assessment method and a split algorithm are developed to evaluate and eliminate these two type errors. It is clear that not all the line features carry the two type errors; as such the assessment method is used as a filter for checking and reserving such features that are consistent with the error conditions. In general, feature with simple geometry is error-free and selected by the filter whereas feature with complex geometry is rejected. According to the two type errors, two sub-procedures of the assessment process are introduced. The first one mathematically is a scheme of solving the maximum deviation of rotation trajectories of profile boundary, so as to neglect the type 1 error if it approaches to zero. The other one is to solve the maximum deviation of trajectories of all points of the feature: type 2 error can be ignored when the retrieved maximum deviation is not greater than prescribed threshold, so that the feature will always stay within the tolerance zone. For such features rejected by the filter which are inconsistent with the error conditions, the split algorithm, which is spread into the three cases of occurrence of type 1 error, occurrence of type 2 error and concurrence of two type errors, is developed to ease their errors. By utilizing and analyzing the geometric and kinematic properties of the feature, the split point is recognized and obtained accordingly. Two sub-features are retrieved from the split point and then substituted into the DOFAS framework recursively until all split features can be represented in desired resolution. The split algorithm is efficient and self-adapting lies in the fact that the rules applied can ensure high convergence rate and expected results. Finally, the implementation with two examples indicates that the DOFAS framework is capable of representing profile tolerances with enhanced accuracy thus supports the feasibility of the proposed approach.
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