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Stochastic Claims Reserving Methods in Insurance

Stochastic Claims Reserving Methods in Insurance
Author: Mario V. Wüthrich
Publisher: John Wiley & Sons
Total Pages: 438
Release: 2008-04-30
Genre: Business & Economics
ISBN: 0470772727

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Claims reserving is central to the insurance industry. Insurance liabilities depend on a number of different risk factors which need to be predicted accurately. This prediction of risk factors and outstanding loss liabilities is the core for pricing insurance products, determining the profitability of an insurance company and for considering the financial strength (solvency) of the company. Following several high-profile company insolvencies, regulatory requirements have moved towards a risk-adjusted basis which has lead to the Solvency II developments. The key focus in the new regime is that financial companies need to analyze adverse developments in their portfolios. Reserving actuaries now have to not only estimate reserves for the outstanding loss liabilities but also to quantify possible shortfalls in these reserves that may lead to potential losses. Such an analysis requires stochastic modeling of loss liability cash flows and it can only be done within a stochastic framework. Therefore stochastic loss liability modeling and quantifying prediction uncertainties has become standard under the new legal framework for the financial industry. This book covers all the mathematical theory and practical guidance needed in order to adhere to these stochastic techniques. Starting with the basic mathematical methods, working right through to the latest developments relevant for practical applications; readers will find out how to estimate total claims reserves while at the same time predicting errors and uncertainty are quantified. Accompanying datasets demonstrate all the techniques, which are easily implemented in a spreadsheet. A practical and essential guide, this book is a must-read in the light of the new solvency requirements for the whole insurance industry.


Claims Reserving in General Insurance

Claims Reserving in General Insurance
Author: David Hindley
Publisher: Cambridge University Press
Total Pages: 513
Release: 2017-10-26
Genre: Business & Economics
ISBN: 1107076935

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This is a single comprehensive reference source covering the key material on this subject, and describing both theoretical and practical aspects.


Bayesian Claims Reserving Methods in Non-life Insurance with Stan

Bayesian Claims Reserving Methods in Non-life Insurance with Stan
Author: Guangyuan Gao
Publisher: Springer
Total Pages: 205
Release: 2018-12-31
Genre: Mathematics
ISBN: 9811336091

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This book first provides a review of various aspects of Bayesian statistics. It then investigates three types of claims reserving models in the Bayesian framework: chain ladder models, basis expansion models involving a tail factor, and multivariate copula models. For the Bayesian inferential methods, this book largely relies on Stan, a specialized software environment which applies Hamiltonian Monte Carlo method and variational Bayes.


A Comparison of Stochastic Claims Reserving Methods

A Comparison of Stochastic Claims Reserving Methods
Author: Sukriye Tuysuz
Publisher:
Total Pages: 29
Release: 2018
Genre:
ISBN:

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In order to preserve their solvency, it is very important for insurance companies to accurately estimate their future required reserves. The aim of this article is to determine reserves by using different stochastic models: 1) distribution-free model (Mack's model), 2) probability distribution based models (Normal, Poisson, Gamma and Inverse Gaussian distributions), and 3) these latter probability based models combined with bootstrapping. To implement these models we used data on life-insurance and non-life insurance. Our findings indicate among distribution based methods, Mack's model (dataset 1 and 2) and Gamma probability distribution based model (dataset 3) are the best model in estimating reserves. The model based on Normal distribution produces the worst results, whatever the dataset. Regarding results of bootstrapping based on probability distribution models, they show that method based on Normal probability distribution (dataset 1 and 3) and ODP distribution (dataset 2) fit better. Our results also indicate that bootstrap method based on Chain-Ladder performs quit similarly than the best fitting probability distribution based bootstrap models. Among all retained models, methods based on bootstrapping present higher good-of-fit.


Handbook on Loss Reserving

Handbook on Loss Reserving
Author: Michael Radtke
Publisher: Springer
Total Pages: 317
Release: 2016-10-26
Genre: Business & Economics
ISBN: 3319300563

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This handbook presents the basic aspects of actuarial loss reserving. Besides the traditional methods, it also includes a description of more recent ones and a discussion of certain problems occurring in actuarial practice, like inflation, scarce data, large claims, slow loss development, the use of market statistics, the need for simulation techniques and the task of calculating best estimates and ranges of future losses. In property and casualty insurance the provisions for payment obligations from losses that have occurred but have not yet been settled usually constitute the largest item on the liabilities side of an insurer's balance sheet. For this reason, the determination and evaluation of these loss reserves is of considerable economic importance for every property and casualty insurer. Actuarial students, academics as well as practicing actuaries will benefit from this overview of the most important actuarial methods of loss reserving by developing an understanding of the underlying stochastic models and how to practically solve some problems which may occur in actuarial practice.


Stochastic Loss Reserving Using Generalized Linear Models

Stochastic Loss Reserving Using Generalized Linear Models
Author: Greg Taylor
Publisher:
Total Pages: 100
Release: 2016-05-04
Genre:
ISBN: 9780996889704

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In this monograph, authors Greg Taylor and Gráinne McGuire discuss generalized linear models (GLM) for loss reserving, beginning with strong emphasis on the chain ladder. The chain ladder is formulated in a GLM context, as is the statistical distribution of the loss reserve. This structure is then used to test the need for departure from the chain ladder model and to consider natural extensions of the chain ladder model that lend themselves to the GLM framework.


Claims Reserving in General Insurance

Claims Reserving in General Insurance
Author: David Hindley
Publisher: Cambridge University Press
Total Pages: 514
Release: 2017-10-26
Genre: Mathematics
ISBN: 1108514847

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This is a comprehensive and accessible reference source that documents the theoretical and practical aspects of all the key deterministic and stochastic reserving methods that have been developed for use in general insurance. Worked examples and mathematical details are included, along with many of the broader topics associated with reserving in practice. The key features of reserving in a range of different contexts in the UK and elsewhere are also covered. The book contains material that will appeal to anyone with an interest in claims reserving. It can be used as a learning resource for actuarial students who are studying the relevant parts of their professional bodies' examinations, as well as by others who are new to the subject. More experienced insurance and other professionals can use the book to refresh or expand their knowledge in any of the wide range of reserving topics covered in the book.


A Comparison of Stochastic Claim Reserving Methods

A Comparison of Stochastic Claim Reserving Methods
Author: Eric M. Mann
Publisher:
Total Pages:
Release: 2011
Genre:
ISBN:

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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.


Loss Reserving

Loss Reserving
Author: Gregory Taylor
Publisher: Springer Science & Business Media
Total Pages: 396
Release: 2012-12-06
Genre: Business & Economics
ISBN: 1461545838

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All property and casualty insurers are required to carry out loss reserving as a statutory accounting function. Thus, loss reserving is an essential sphere of activity, and one with its own specialized body of knowledge. While few books have been devoted to the topic, the amount of published research literature on loss reserving has almost doubled in size during the last fifteen years. Greg Taylor's book aims to provide a comprehensive, state-of-the-art treatment of loss reserving that reflects contemporary research advances to date. Divided into two parts, the book covers both the conventional techniques widely used in practice, and more specialized loss reserving techniques employing stochastic models. Part I, Deterministic Models, covers very practical issues through the abundant use of numerical examples that fully develop the techniques under consideration. Part II, Stochastic Models, begins with a chapter that sets up the additional theoretical material needed to illustrate stochastic modeling. The remaining chapters in Part II are self-contained, and thus can be approached independently of each other. A special feature of the book is the use throughout of a single real life data set to illustrate the numerical examples and new techniques presented. The data set illustrates most of the difficult situations presented in actuarial practice. This book will meet the needs for a reference work as well as for a textbook on loss reserving.


Claims Reserving in Non-life Insurance

Claims Reserving in Non-life Insurance
Author: Gregory Clive Taylor
Publisher: North Holland
Total Pages: 252
Release: 1986
Genre: Business & Economics
ISBN:

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