Volume 2, Issue 1

This paper investigates adverse selection in crop insurance markets using both parametric and nonparametric methods. Our results reject the conditional independence of the choice of insurance contracts and risk of loss, implying the presence of informational asymmetries between insurers and insureds in the markets. Results also show that actual premium rates are significantly different from both pure and fair premium rates. Our findings provide useful insights into asymmetric information problems and methods to assess the risk of loss in crop insurance markets.

We consider a renewal risk model in which the claim inter-arrival distribution is generalized exponential (GE). We obtain the probability distribution for the ladder height distribution and use it to find the bounds for the ultimate ruin probabilities for individual claim amount distributions. The method suggested by Dufresne and Gerber (1989) is used for finding the bounds for ruin probabilities.

In this paper we discuss the use of modern data mining (DM) methods to design risk-based insurance premiums for motor vehicles. Our objective is to predict the likelihood and expected value of future claims for each insured based on a myriad of attributes available in the database on the customers and their “peers.” The model results may then be used for underwriting and for rate making. We employ a two-stage approach, involving a survival analysis model and a linear regression model, to estimate the risk level of each customer and the proneness to file a claim. The study was performed on actual data set obtained from a small insurance company. We demonstrate our ability to discover new underwriting parameters, build accurate predictive models and to distinguish between distinct groups of policies. The new method creates a new ordering of the policies where the most risky people were, on the average, 12 times more expensive than the least risky people. The importance of the study is not in the particular results, which are specific for the particular company and its environment, but rather in the demonstration of the general ability to use data mining for insurance rate making purposes, and in the original use of the concept of survival analysis and the concept of mean time between claims for this purpose.

It is possible in France to finance the purchase of a property with a viager in lieu of obtaining a traditional mortgage loan, where the purchase price of the property, or balance of the price if a down payment is made (the bouquet), is converted into an annuity that the buyer agrees to pay for the remainder of the seller's life. Insurance companies in France have been significant investors in the viager market for many years. The cash flows generated by pools of viagers can thus mirror those from pools of life insurance policies. We propose in this paper a model that values the possible securitization of viagers and that values life extension risk. The securitization of viagers gives insurance companies an opportunity to create portfolios where mortality risk can be hedged by life extension risk.

The purpose of this research is to discuss Japanese financial instability and both private life insurance and public life insurance (Kampo) demand. At the same time, we also consider Kampo’s main role and what Kampo should be as an insurance service provider in the future. From empirically estimating private life insurance and Kampo demand functions using household-level data provided by the Postal Services Research Institute, the authors find that differences in income, children, pension and knowledge affect households’ decisions on the purchase of life insurance products. Income and financial assets also appear to have significant effect on the purchase of private life insurance products and Kampo. However, pension and bankruptcy experience appear to have an impact only on Kampo, while age (less than 40) and occupation (public official) factors affect only private life insurance. Dummy variables representing insurer comparison, knowledge and bankruptcy experience are not found to have any significant effect on decisions concerning private life insurance. The authors also conducted simultaneous estimations to examine the reasons why households with private life insurance products additionally purchase Kampo and vice versa. The results indicate that differences in income, children and bankruptcy experience variables do not affect the decision-making process, and that a knowledge dummy has a negative impact on additional purchases.