Modern Actuarial Risk Theory contains what every actuary needs to know about non-life insurance mathematics. It starts with the standard material like utility theory, individual and collective model and basic ruin theory. Other topics are risk measures and premium principles, bonus-malus systems, ordering of risks and credibility theory. It also contains some chapters about Generalized Linear Models, applied to rating and IBNR problems. As to the level of the mathematics, the book would fit in a bachelors or masters program in quantitative economics or mathematical statistics. This second and much expanded edition emphasizes the implementation of these techniques through the use of R. This free but incredibly powerful software is rapidly developing into the de facto standard for statistical computation, not just in academic circles but also in practice. With R, one can do simulations, find maximum likelihood estimators, compute distributions by inverting transforms, and much more.

I am pleased to participate in this Summer School and look forward to sharing some ideas with you over the next few days. At the outset I would like to describe the approach I will take in 1 presenting the material. I aim to present the material in a non rigorous way and hopefully in an intuitive manner. At the same time I will draw attention to some of the major technical problems. It is pitched at someone who is unfamiliar with the area. The results presented here are unfamiliar to actuaries and insurance mathematicians although they are well known in some other fields. During the next few minutes I will make some preliminary comments. The purpose of these comments is to place the lectures in perspective and motivate the upcoming material. After this I will outline briefly the topics to be covered during the rest of this lecture and in the lectures that will follow. One of the central themes of these lectures is RISK-SHARING. Risk-sharing is a common response to uncertainty. Such uncertainty can arise from natural phenomena or social causes. One particular form of risk-sharing is the insurance mechanism. I will be dealing with models which have a natural application in the insurance area but they have been applied in other areas as well. In fact some of the paradigms to be discussed have the capacity to provide a unified treatment of problems in diverse fields."

Canadian financial institutions have been in rapid change in the past five years. In response to these changes, the Department of Finance issued a discussion paper: The Regulation of Canadian Financial Institutions, in April 1985, and the government intends to introduce legislation in the fall. This paper studi.es the combinantion of financial institutions from the viewpoint of ruin probability. In risk theory developed to describe insurance companies [1,2,3,4,5J, the ruin probability of a company with initial reserve (capital) u is 6 1 -:;-7;;f3 u 1jJ(u) = H6 e H6 (1) Here,we assume that claims arrive as a Poisson process, and the claim amount is distributed as exponential distribution with expectation liS. 6 is the loading, i.e., premium charged is (1+6) times expected claims. Financial institutions are treated as "insurance companies": the difference between interest charged and interest paid is regarded as premiums, loan defaults are treated as claims.

I am pleased to participate in this Summer School and look forward to sharing some ideas with you over the next few days. At the outset I would like to describe the approach I will take in 1 presenting the material. I aim to present the material in a non rigorous way and hopefully in an intuitive manner. At the same time I will draw attention to some of the major technical problems. It is pitched at someone who is unfamiliar with the area. The results presented here are unfamiliar to actuaries and insurance mathematicians although they are well known in some other fields. During the next few minutes I will make some preliminary comments. The purpose of these comments is to place the lectures in perspective and motivate the upcoming material. After this I will outline briefly the topics to be covered during the rest of this lecture and in the lectures that will follow. One of the central themes of these lectures is RISK-SHARING. Risk-sharing is a common response to uncertainty. Such uncertainty can arise from natural phenomena or social causes. One particular form of risk-sharing is the insurance mechanism. I will be dealing with models which have a natural application in the insurance area but they have been applied in other areas as well. In fact some of the paradigms to be discussed have the capacity to provide a unified treatment of problems in diverse fields.

Canadian financial institutions have been in rapid change in the past five years. In response to these changes, the Department of Finance issued a discussion paper: The Regulation of Canadian Financial Institutions, in April 1985, and the government intends to introduce legislation in the fall. This paper studi.es the combinantion of financial institutions from the viewpoint of ruin probability. In risk theory developed to describe insurance companies [1,2,3,4,5J, the ruin probability of a company with initial reserve (capital) u is 6 1 -:;-7;;f3 u 1jJ(u) = H6 e H6 (1) Here,we assume that claims arrive as a Poisson process, and the claim amount is distributed as exponential distribution with expectation liS. 6 is the loading, i.e., premium charged is (1+6) times expected claims. Financial institutions are treated as "insurance companies": the difference between interest charged and interest paid is regarded as premiums, loan defaults are treated as claims.

Modern Actuarial Risk Theory contains what every actuary needs to know about non-life insurance mathematics. It starts with the standard material like utility theory, individual and collective model and basic ruin theory. Other topics are risk measures and premium principles, bonus-malus systems, ordering of risks and credibility theory. It also contains some chapters about Generalized Linear Models, applied to rating and IBNR problems. As to the level of the mathematics, the book would fit in a bachelors or masters program in quantitative economics or mathematical statistics. This second and much expanded edition emphasizes the implementation of these techniques through the use of R. This free but incredibly powerful software is rapidly developing into the de facto standard for statistical computation, not just in academic circles but also in practice. With R, one can do simulations, find maximum likelihood estimators, compute distributions by inverting transforms, and much more.