Stochastic Loss Reserving Using Generalized Linear Models

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

Download Stochastic Loss Reserving Using Generalized Linear Models Book in PDF, Epub and Kindle

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.

Stochastic Claims Reserving Methods in Insurance

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

Download Stochastic Claims Reserving Methods in Insurance Book in PDF, Epub and Kindle

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.

Bayesian Claims Reserving Methods in Non-life Insurance with Stan

Bayesian Claims Reserving Methods in Non-life Insurance with Stan
Title Bayesian Claims Reserving Methods in Non-life Insurance with Stan PDF eBook
Author Guangyuan Gao
Publisher Springer
Pages 210
Release 2018-12-31
Genre Mathematics
ISBN 9811336091

Download Bayesian Claims Reserving Methods in Non-life Insurance with Stan Book in PDF, Epub and Kindle

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.

Using the ODP Bootstrap Model

Using the ODP Bootstrap Model
Title Using the ODP Bootstrap Model PDF eBook
Author Mark R. Shapland
Publisher
Pages 116
Release 2016
Genre Actuarial science
ISBN 9780996889742

Download Using the ODP Bootstrap Model Book in PDF, Epub and Kindle

Non-Life Insurance Pricing with Generalized Linear Models

Non-Life Insurance Pricing with Generalized Linear Models
Title Non-Life Insurance Pricing with Generalized Linear Models PDF eBook
Author Esbjörn Ohlsson
Publisher Springer Science & Business Media
Pages 181
Release 2010-03-18
Genre Mathematics
ISBN 3642107915

Download Non-Life Insurance Pricing with Generalized Linear Models Book in PDF, Epub and Kindle

Non-life insurance pricing is the art of setting the price of an insurance policy, taking into consideration varoius properties of the insured object and the policy holder. Introduced by British actuaries generalized linear models (GLMs) have become today a the standard aproach for tariff analysis. The book focuses on methods based on GLMs that have been found useful in actuarial practice and provides a set of tools for a tariff analysis. Basic theory of GLMs in a tariff analysis setting is presented with useful extensions of standarde GLM theory that are not in common use. The book meets the European Core Syllabus for actuarial education and is written for actuarial students as well as practicing actuaries. To support reader real data of some complexity are provided at www.math.su.se/GLMbook.

Claim Models

Claim Models
Title Claim Models PDF eBook
Author Greg Taylor
Publisher MDPI
Pages 108
Release 2020-04-15
Genre Business & Economics
ISBN 3039286641

Download Claim Models Book in PDF, Epub and Kindle

This collection of articles addresses the most modern forms of loss reserving methodology: granular models and machine learning models. New methodologies come with questions about their applicability. These questions are discussed in one article, which focuses on the relative merits of granular and machine learning models. Others illustrate applications with real-world data. The examples include neural networks, which, though well known in some disciplines, have previously been limited in the actuarial literature. This volume expands on that literature, with specific attention to their application to loss reserving. For example, one of the articles introduces the application of neural networks of the gated recurrent unit form to the actuarial literature, whereas another uses a penalized neural network. Neural networks are not the only form of machine learning, and two other papers outline applications of gradient boosting and regression trees respectively. Both articles construct loss reserves at the individual claim level so that these models resemble granular models. One of these articles provides a practical application of the model to claim watching, the action of monitoring claim development and anticipating major features. Such watching can be used as an early warning system or for other administrative purposes. Overall, this volume is an extremely useful addition to the libraries of those working at the loss reserving frontier.

Claims Reserving in General Insurance

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

Download Claims Reserving in General Insurance Book in PDF, Epub and Kindle

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.