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AbstractWe present a claims reserving technique that uses claim-specific feature and past payment information in order to estimate claims reserves for individual reported claims. We design one single neural network allowing us to estimate expected future cash flows for every individual reported claim. We introduce a consistent way of using dropout layers in order to fit the neural network to the incomplete time series of past individual claims payments. A proof of concept is provided by applying this model to synthetic as well as real insurance data sets for which the true outstanding payments for reported claims are known.

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

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

Actuaries working in claims reserving are often faced, among others, with the following two tasks: the prediction of future outstanding loss liabilities, as well as the quantification of their risk. Within claims reserving there exist various methods in which vagueness and subjective judgement is often not considered. A formal approach is given e.g. by fuzzy set theory. Besides an overview of applications of fuzzy set theory in claims reserving the author presents three ways of how subjective assessment can be implemented in the chain-ladder as well as the Bornhuetter Ferguson method.

Acknowledgements; Contents; List of Figures; List of Tables; List of Symbols; List of Abbreviations; 1 | Introduction; 2 | Fuzzy Theory; 3 | Applications of Fuzzy Theory in Insurance; 4 | Methods of Claims Reserving; 5 | The Fuzzy Chain-Ladder Model -- An Approach with Fuzzy Numbers; 6 | Another Fuzzy Chain-Ladder Model -An Application of Fuzzy Regression Techniques; 7 | The Fuzzy Bornhuetter Ferguson Method; 8 | Conclusion; A | Statistical Basics; Zusammenfassung; Summary; Bibliography.