IRANIAN JOURNAL OF INSURANCE RESEARCH (SANAAT-E-BIMEH)
Year:2011 | Volume:25 | Issue:4 (100)|Papers Count:10

This research surveys the relation of insurance industry manager’s intention (environmental scanning) to common and task environments and their subsections with organizational performance examined. Statistical population was executives and middle managers of private and public organizations. Results show that uncertainty of insurance industry is high and except technological subsection, attention and scanning of other subsection have influence on organizational performance. Also, companies with different performances (high, medium and low degree) have different attention and scanning behavior. High performance companies use gathered information and data in decision making and strategic choices more than low performance companies. They use information and data in competitive and operational activities. Finally preference and importance of subsections are not equal among companies and their attention to them are different, but there is not any meaningful difference among their scanning quantity.

Value at Risk (VaR) is an index which is used by insurance companies in order to make appropriate decision about accepting or rejecting the risk of different types of risks, defining the amount of their reinsurance retention level according to their maximum allowed capacity and also make decision about their reinsurance ceding level. In this paper, VaR is calculated by using GARCH and ARMA models and then, by anticipating the model for future years (if company’s decision making trend in insurance risk selection method is continued on the same trend) we defined that the company has a suitable situation and uncommon risks will not threat it.

Heterogeneous mortality models are one of the most important subjects in survival analysis studies. In such models, because of individual and genetic differences between individuals which are influenced on their lifetime, we encounter heterogeneous populations. To make clear this heterogeneity, we use a non-negative real valued quantity called frailty. Since frailty variable is an unobservable quantity, so it is not possible to measure it. To consider this variable in mortality models by introducing a generalized Gompertz distribution, we show that in heterogeneous mortality models with Gompertz as a baseline force of mortality and Gamma distributed frailty, the lifetime random variable can be considered as a difference of two generalized Gompertz random variables. In this difference, the first random variable indicates the population's force of mortality which is distributed with Gompertz and the second random variable indicates frailty. Due to the relation between generalized Gompertz distribution and its reversed version, we consider the frailty variable with reversed generalized Gompertz distribution. The method used in this article allows the difference distribution to interpret the frailty distribution as a lifetime reduction distribution.

In Iranian insurance market, car insurance has the highest volume in terms of issuance and loss. So, the need for financial decisions and predicting the behavior of this market is imperative for insurers. Time series are powerful statistical methods that use statistical methods based on random processes to modelise and predict the behavior of such variables depend on time. In this paper, damage of hull car insurance (Parsian Insurance Company from March 1385 to July 1389) is considered as a variable, then by using two models time series, i.e. Box- Jenkins and Winters, the behavior of this variable is modelized. Since observations has seasonal trends, SARIMA and Winters multiplicative models are used. MAPE error indicator tools show that Winter’s multiplicative model has more accurate fitting. Finally, by using Winters model, damage of hull car insurance for a period of six months is predicted.

In this study, we investigated the relationship between non-financial measures and financial performance in public insurance companies’ branches of Tehran province. So, two measures, including customer satisfaction and employees’ satisfaction are considered as non- financial measures and gross profit of insurance transactions are considered as financial performance measures.The results of this study show that there are significant differences between insurance companies in terms of customer satisfaction, and relatively, insurance companies with high gross profit has high customer satisfaction. There were significant differences between insurance companies in terms of employees’ satisfaction, and it can be concluded that insurance companies with high gross profit, has high employees’ satisfaction. Also, the results of this study show that there is not any significant difference in terms of customer satisfaction between insurance companies with high employees’ satisfaction and insurance companies with low employees’ satisfaction.

In today’s world, customer relationship management is one of the most important fields for applying data mining. It can help us to predict each customer with what probability will purchase our product and it is useful for decision makers to access all customers effectively with developing various marketing strategies. The purpose of this study is to identify input features for making a customer recognition system that is computationally efficient and effective. Although forecasting methods are popularly used, it cannot be useful unless irrelevant features are removed because they will present inappropriate customer recognition and make poor results. In this paper, regression decision tree is used for feature selection in order to design a customer recognition system and forecasting future customers. The novelty of the method lies in the implementation of the mentioned approach in customer recognition and forecasting field. A real case of the insurance data set in the Netherlands is used in order to indicate the application of the influencing set of features identification on customer purchasing behavior model. A significant reduction in the set of features is obtained. As a result, applying regression decision tree, optimizes input features for forecasing. Also, the results show that reduction of dimensionality leads to decrease computation.

This paper reviews the differences between customer expectation and perception of service quality in car insurance claim payment units (collision and third party liability) in B and A insurance companies and compares the performance of each company using SERVQUAL model. The results indicate that there are significant differences between expectations and perceptions of clients in two companies. Results from the first hypothesis show that there are significant differences between customers of both companies in two aspects, i.e. reliability and responsiveness and the average customer waiting, in company B is more than company A. The result of second hypothesis indicate that there is no difference between customer perceptions in two companies in all five dimensions. The results of third hypothesis show that the gap between services in the B insurance company is more than A. In the other hand; A insurance company meets customer expectations more successfully.