Non-life Business Research Articles

ALM for insurers with multiple underwriting lines and portfolio constraints: a Lagrangian duality approach

We investigate how an insurance firm can optimally allocate its assets to back up liabilities from multiple non-life business lines. The insurance risks are modeled by a multidimensional jump-diffusion process that accounts for simultaneous claims in different insurance lines with policy limits. We use Lagrangian convex duality techniques to derive optimal investment-underwriting strategies that maximize the expected utility from dividends and final wealth over a finite horizon. We examine how risk aversion, prudence, portfolio constraints, and multivariate insurance risk affect the firm’s earnings retention. We obtain explicit solutions for optimal strategies under constant relative risk aversion preferences. Finally, we illustrate our results with numerical examples and show the impact of co-integration for asset-liability management with multiple sources of insurance risk.

Non-life insurance cancellation: a systematic quantitative literature review

The purpose of this paper is to provide an overall picture of the current investigation of insurance cancellation in the non-life business, taking into consideration the influence of intermediaries on customers’ decisions using the systematic quantitative literature review method. This article identifies the most important factors that explain switching behaviour in non-life insurance. It also highlights the impact and responsibility of insurance companies’ marketing strategies and tactics on insurance policy cancellations and customer churn. This research contributes to increasing scientific knowledge on insurance cancellation in the non-life business and to the development of actions to retain customers, with a higher level of effectiveness, improving the results of insurance companies and the performance of the industry.

Profitability and Growth in Motor Insurance Business: Empirical Evidence from Germany

Over recent years, the German motor insurance business has faced significant changes, including a growing importance of direct insurance offerings. Motor insurance products are offered by a wide range of insurers, with companies differing in terms of legal status, size, product portfolio, distribution strategy and operational efficiency. Furthermore, one distinguishes between two main products, namely motor third-party liability (MTPL) and motor own damage (OD). In our research, we analyse to what extent the characteristics of the companies can explain the premiums, the total claims costs and the operating expenses per contract in MTPL and OD. For our analysis, we use panel data of insurance companies, offering motor insurance products in Germany, for the years 2002–2014. The panel data provide almost full market coverage. In our study, we apply different statistical tests and multilinear regression models. We show that mutuals relate to lower premiums, lower total claims costs and lower operating expenses per contract when compared to listed companies. In addition, direct insurance companies get along with lower premiums and lower operating expenses per contract compared to traditional companies selling via agents or brokers. Furthermore, we find major differences related to the range of the product portfolio, the size of the motor business, the dominance of the motor business within the non-life business, and the calendar year. Our results are relevant to academics and practitioners alike and help to better understand the German motor insurance business.

A Stochastic Forward-Looking Model to Assess the Profitability and Solvency of European Insurers

In this paper, we develop an analytical framework for conducting forward-looking assessments of profitability and solvency of the main euro area insurance sectors. We model the balance sheet of an insurance company encompassing both life and non-life business and we calibrate it using country level data to make it representative of the major euro area insurance markets. Then, we project this representative balance sheet forward under stochastic capital markets, stochastic mortality developments and stochastic claims. The model highlights the potential threats to insurers solvency and profitability stemming from a sustained period of low interest rates particularly in those markets which are largely exposed to reinvestment risks due to the relatively high guarantees and generous profit participation schemes. The model also proves how the resilience of insurers to adverse financial developments heavily depends on the diversification of their business mix. Finally, the model identifies potential negative spillovers between life and non-life business thorugh the redistribution of capital within groups.

LIFE INSURANCE INDUSTRY IN INDIA-AN OVERVIEW

The first insurer of life was the marine insurance underwriters who started issuing life insurance policies on the life of master and crew of the ship, and the merchants. The first insurance policy was issued on 18th June 1583,on the life of WILLIAM GIBBONS for the period of 12 months. The oriental life insurance company is the first insurance companies in India which is started on 1818 by Europeans at Kolkata. The Indian Life Assurance Companies Act, 1912 was the first statutory measure to regulate life business. In 1928, the Indian Insurance Companies Act was enacted to enable the Government to collect statistical information about both life and non-life business transacted in India by Indian and foreign insurers including provident insurance societies. In 1938, with a view to protecting the interest of the Insurance public, the earlier legislation was consolidated and amended by the Insurance Act, 1938 with comprehensive provisions for effective control over the activities of insurers. In 1956 the life insurance companies was nationalized. The LIC absorbed 154 Indian, 16 non-Indian insurers as also 75 provident societies—245 Indian and foreign insurers in all. The LIC had monopoly till the late 90s when the Insurance sector was reopened to the private sector.

A Stochastic Forward-Looking Model to Assess the Profitability and Solvency of European Insurers

In this paper, we develop an analytical framework for conducting forward-looking assessments of profitability and solvency of the main euro area insurance sectors. We model the balance sheet of an insurance company encompassing both life and non-life business and we calibrate it using country level data to make it representative of the major euro area insurance markets. Then, we project this representative balance sheet forward under stochastic capital markets, stochastic mortality developments and stochastic claims. The model highlights the potential threats to insurers solvency and profitability stemming from a sustained period of low interest rates particularly in those markets which are largely exposed to reinvestment risks due to the relatively high guarantees and generous profit participation schemes. The model also proves how the resilience of insurers to adverse financial developments heavily depends on the diversification of their business mix. Finally, the model identifies potential negative spillovers between life and non-life business thorugh the redistribution of capital within groups.

Interlocking Directorates as a Trust Substitute: The Case of the Italian Non-Life Insurance Industry

This paper investigates the market structure of the insurance business by analyzing the (interlock) linkages among companies created by their directors. We focus on the non-life business since this is a sector relatively closed with respect to the competition with other financial activities; an absence of industry competition cannot thus be compensated by other agents. We apply the graph theory to describe the network and the principal component analysis to summarize information and verify the correlation between direct interlocking and companies' market shares.

Cyprus: Financial Sector Assessment Program Update - Technical Note: Insurance Regulation and Supervision

This paper examines the Insurance Regulation and Supervision for Cyprus’s Financial Sector Assessment Program. The domestic nonlife market is dominated by motor insurance which accounted for 58 percent of net premiums. All Cypriot business is reinsured with nondomiciled reinsurers. Health insurance is regulated as nonlife business when written as add-ons to accident and sickness or as life business when written with life policies. A number of professional bodies and self-regulatory industry associations complement the regulatory regime for the insurance industry.

Application of classical and robust chain-ladder methods: results for the Belgian non-life business

The solvency II regulation for the non-life insurance business focuses on the application of the chain-ladder method in the EU. A recent memorandum of the EU also expresses its concern about the treatment of outlying data. Not only the appearance but certainly the consequences for the solvency risk capital are brought up. We will discuss the results of applying classical and robust chain-ladder methods to real data from several lines of business in the non-life insurance branch in Belgium.

An International Analysis of Life Insurance Demand

Introduction The service sector of the world economy has grown substantially since World War II. In 1989, the service sector accounted for approximately 60 percent of the world's gross domestic product (United Nations Conference on Trade and Development, 1990). The worldwide insurance industry, which makes up a significant portion of the service sector, has grown at a rate of over 10 percent annually since 1950. This growth rate has far exceeded that of economic development globally (United Nations Conference on Trade and Development, 1972, 1991; Dowling, 1982; and Swiss Reinsurance Company, 1990a). Table 1 presents data on the worldwide insurance industry, which has grown even more rapidly in recent years, at a rate of approximately 24 percent annually from 1984 to 1988. Rapid growth in the life insurance sector has contributed much to this increase. The life insurance industry has grown at a rate of approximately 30 percent annually, while the nonlife industry has grown at a rate of 19 percent annually during this period. The share of total insurance premium volume accounted for by life insurance first exceeded the share of nonlife business in 1987, and the gap has widened since that time (Swiss Reinsurance Company, 1990b). The share attributable to the life insurance sector increased from 43 percent of the total premium volume in 1984 to 53 percent in 1988. The life insurance industry has grown from 2.1 percent of world gross domestic product in 1984 to 3.4 percent in 1988. Table 1 World Premium Volume in 1984 and 1988 Premiums in Millions of U.S. Dollars Percent Share in Total Total Nonlife Life Total Nonlife Life 1984 498,000 281,500 216,500 100 57 43 1988 1,171,000 555,100 615,900 100 47 53 Source: Swiss Reinsurance Company (1986, 1990b). While the worldwide insurance market, especially the life insurance market, has grown rapidly and the internationalization of the insurance business is becoming more widespread, these areas have not been greatly researched. Skipper (1987) classified and discussed the nature of protective barriers in international insurance services and provided an analysis of the protectionists' rationales. Shroath and Korth (1989) examined the foreign market entry patterns of U.S. property-liability insurers. Beenstock, Dickinson, and Khajuria (1988) studied the relationship between property-liability insurance expenditures and income for 12 industrialized countries. Outreville (1990) investigated the relationship between property-liability insurance premiums and the economic and financial development of 55 developing countries. Weiss (1991) examined the productivity of the property-liability insurance industries in the United States, West Germany, Switzerland, France, and Japan. Cummins (1973) studied the effect of macroeconomic variables on the U.S. life insurance industry and found life insurance and insured pension reserves are correlated with GNP and permanent income. This article identifies the factors that lead to variations in the demand for life insurance across nations. In the next section, we set forth a model of the demand for life insurance. Each of the factors expected to affect the demand for life insurance is discussed and the hypotheses of the study are formulated. The empirical model used to test the hypotheses is then presented. The article concludes with a discussion of the major findings and suggestions for future research. The Theory of the Demand for Life Insurance Theoretical models of the demand for life insurance have been derived by Yaari (1965), Fischer (1973). Pissarides (1980), Campbell (1980), Karni and Zilcha (1985, 1986). Lewis (1989), and Bernheim (1991). The models view life insurance as the means by which uncertainty in the household's income stream, related to the possible premature death of a household's primary wage earner, is reduced. …

How to live with inflation in insurance business

Abstract This paper deals exclusively with non-life insurance business. The problems caused by inflation in life insurance business are quite different from the problems caused within non-life business and will not be dealt with here.

A Computer approach to ordinary Life Assurance Policy Valuation—A new look at concepts

This paper has been written as an ‘Expository Article’ and, as such, is not meant to be complete. Its main object is to describe useful concepts and methods that are not covered by the standard reading for the Institute's examinations. As the title of the paper implies not all of these concepts are new. Some, in fact, are first principles which are particularly useful when performing an actuarial valuation using a computer. The title also suggests that the paper is confined to Ordinary Life business. However, much of what is said could also be applied to Industrial or even Non-life business.

The Problem of Solvency in Life Assurance

In the train of the economic integration of Europe, a lively discussion has, for some time, been carried on about appropriate criteria of solvency for insurance undertakings in respect of life and non-life assurance. The conflict over the proposals for solvency requirements in non-life assurance still occupies the foreground. Here it is the proposed directive put up for discussion by the E.E.C. authorities which is of particular importance. This directive puts forward quantitative conditions for the solvency reserves of companies carrying on non-life business.

Some Statistical Aspects of Non-Life Insurance

When invited to prepare a paper to the Students' Society I had in mind selecting a topic from each of the various branches to illustrate the types of problems that are inherent in non-life business. However, as my ideas took positive shape, I found myself drawn into a more active study of certain aspects which have received little attention in this country, although some considerable theoretical and a limited amount of practical work has been done on the Continent. Instead, therefore, of giving a general description of various features I have largely limited myself to techniques which could be used in practical application and thus endeavoured to bring into relief some problems which indicate the almost unlimited scope for practical and theoretical investigations.2. Initially the treatment is simplified; I hope that I have succeeded in picking out the spots where intuition rather than formal proof has been my guide, but if I have not I hope my critics will give me latitude in this exploration into largely unexplored territory.

The standard deviation of the distribution of sickness

The standard deviation of the distribution of sickness is a statistic that appears to have been ignored by British Actuaries, with the exception of Sir G. F. Hardy who derived an approximation in his well-known Essay (J.I.A.Vol. XXVII, 1889, p. 245). By considering the graduated results of the A.O.F. Experience 1871–75 he concluded that the mean deviation of expected sickness was approximately 2√(expected weeks of sickness).This note has arisen from some investigations being made into the statistics of non-life business, from which it appeared that the results obtained could be used to place the derivation of the standard deviation of expected sickness on a more satisfactory basis, and it is being put forward in the hope that some member with access to the necessary statistics will be able to make a more detailed study of this problem.