This book, adopting machine learning techniques for the financial planning field, explores the demand for life insurance as seen in previous literature and both estimates and predicts the demand for the adoption of life insurance using these techniques. Previous studies used diverse perspectives, like actuarial and life span, in order to understand the demand for life insurance, though these approaches have shown inconsistent findings. Employing two theoretical backgrounds—ecological systemic theory and artificial intellectual methodology—this book explores a better estimation and a prediction of the demand for life insurance and will be of interest to academics and students of insurance, financial planning, and risk management.

Insurance companies, as well as banks and thrift institutions, have traditionally reported assets and liabilities on the basis of their amortized cost, or book value. But following the turmoil in securities markets due to highly volatile interest rate fluctuations in the 1980s and the early 1990s, and problems caused by inadequate liquidity, in the mid-1990s the Financial Accounting Standards Board (FASB) issued a new ruling calling for financial intermediaries to report the fair, or market, value of most assets. Called FAS 115, this new standard is the first step in the eventual change to valuing all the assets and liabilities belonging to financial intermediaries under the fair value accounting method. Thus, these changes will pose tremendous future implications for three key business measures of a financial intermediary: Solvency: if the fair values of assets and liabilities are out-of-step, then healthy companies may report negative net worth and insolvent companies may appear to be in sound financial condition. Reported Earnings: if the fair values of assets and liabilities are out of step, then reported earnings will not accurately represent the financial operations of the company. Risk Management: FASB recently postponed the implementation of its new rules on accounting for the use of derivatives instruments. However, if the final set of rules for figuring the fair value of derivatives is not carefully crafted, it may be possible that companies prudently hedging their risks are subject to penalties in their financial reports, while companies taking greater risks appear to have less volatile financial performance. Compared to banks and other financial intermediaries, life insurance companies have the longest term and most complex liabilities, and hence the new FASB requirement poses the most severe challenges to the life insurance industry. The lessons learned from the debate among life insurance academics and professionals about how respond to the fair value reporting rule will be instructive to their counterparts in other sectors of the insurance industry, as well as those involved with other financial institutions. Of particular note are the two papers which comprise Part III. The first provides examples of the fair valuing of annuity contracts, while the second offers examples of the fair valuing of term insurance products. As the papers collected in The Fair Value of Insurance Business extend and update some of the issues treated in a previous Salomon Center conference volume, The Fair Value of Insurance Liabilities, this new volume may be viewed as a companion to the earlier book.

With the Brexit referendum more than 50% of the British population voted in favour of the UK leaving the EU. Since that event, much speculation has been about Brexit impact on the global economy, also affecting the insurance market. Existing papers and literature indicate potential significant Brexit implications on the structure of the European insurance market due to the loss of the European Passport, the European Solvency II supervisory regime, as well as on stock prices of insurance companies based in the EU and UK, but so far there is no holistic view on this topic. The dissertation aims to bring together the fragmentary information on Brexit implications for the European insurance market, develop methodological approaches further, conduct new analyses, and reflect a holistic picture of the impact of Brexit on the European insurance market, focusing on the structure of the European insurance market, stock market reactions and the Solvency II supervisory regime.

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Two different applications have been considered, automobile claims from Massachusetts and health expenses from the Netherlands. We have fit 11 different distributions to these data. The distributions are conveniently nested within a single four parameter distribution, the generalized beta of the second type. This relationship facilitates analysis and comparisons. In both cases the GB2 provided the best fit and the Burr 3 is the best three parameter model. In the case of automobile claims, the flexibility of the GB2 provides a statistically siE;nificant improvement in fit over all other models. In the case of Dutch health expenses the improvement of the GB2 relative to several alternatives was not statistically significant. * The author appreciates the research assistance of Mark Bean, Young Yong Kim and Steve White. The data used were provided by Richard Derrig of The Massachusetts Automobile Rating and Accident Prevention Bureau and by Bob Van der Laan and The Silver Cross Foundation for the medical insurance claim data. 2~ REFERENCES Arnold, B. C. 1983. Pareto Distributions. Bartonsville: International Cooperative Publishing House. Cummins, J. D. and L. R. Freifelder. 1978. A comparative analysis of alternative maximum probable yearly aggregate loss estimators. Journal of Risk and Insurance 45:27-52. *Cummins, J. D., G. Dionne, and L. Maistre. 1987. Application of the GB2 family of distributions in collective risk theory. University of Pennsylvania: Mimeographed manuscript. Hogg, R. V. and S. A. Klugman. 1983. On the estimation of long tailed skewed distributions with actuarial applications.

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