پژوهشنامه بیمه
سال سی و هشتم شماره 3 (تابستان 1402)

Based on financial statements of 2013 to 2021 for Civil Servants Pension Fund, fiscal sustainability index (ratio of resources to expenditures) was 40% in average while this index should be equal or more than 100% so that the Fund would be able to fulfill its liabilities; Due to lack of financial ability, about 90% of the credits needed to pay pension salaries were provided by the government aid. In this paper, analyzing of asset - liability management of Civil Servants Pension Fund is carried out using a model based on multi-stage stochastic programming and suggestions for managing the assets and liabilities of the Fund with the aim of fiscal sustainability and gradually reducing dependence on government aid to fulfill annual liabilities have been presented.

Autoregressive Moving-Average Model was used for predicting interest rate of different asset classes in next 20 years (2022 to 2041) and for modeling Asset - Liability Management for the Fund, multi-stage stochastic programming was applied based on predicted data for next 20 years and generating 300 scenarios with a 95% confidence interval, and the results have been analyzed. The model was implemented in GAMS software and results of the model were evaluated with application of conditional value at risk index. Data used for the study are based on financial statements of 2007 to 2021 for Civil Servants Pension Fund.

The implementation of the multi-stage stochastic programming model during years of 2011 to 2021 has shown a rational behavior based on compliance with investment policies and defined limits and an acceptable allocation of the fund's capital resources to all types of asset classes. Implementation of the model during years of 2022 to 2041, taking into account the investment and management policies of the Fund (case study) and restrictions (including a ceiling of 65% for stocks, a ceiling of 15% for real estate, a minimum share of 19% for bonds and bank deposits and a minimum share of five percent for cash of the total assets of the fund) was able to achieve a feasible solution in all scenarios, and the yearly value at risk of the portfolio with a probability of 5% was averagely about 2.3% of total value of the portfolio.

Results of the model and comparison with results of traditional method used for asset - liability management of the case study showed that if the proposed model is used for a period of 20 years in the future, the value of the Fund's assets will grow more than the value resulted from traditional method and the need for receiving government aid to fulfill Fund obligations will be reduced.

One of the criteria for deciding to invest in a listed company is the amount or changes in the stock price of the company in the future days and months. Various methods have been studied to predict the stock price and investment risk in a company. In most of these methods, the stock price is predicted as a continuous response variable. For this purpose, time series models are used in which assumptions such as the normality of disturbances or the linearity of the model are important. The purpose of this research is to introduce a two-category response variable based on the direction of share price movement in the next day and to introduce some statistical classification methods to predict it. These models do not have the limitations of the previous models, and for that reason they are of interest. The main objective of this article is to implement the studied methods and compare their accuracy in predicting the orientation of stock price movement of stock exchange insurance companies.

In the current research, we have predicted the direction of stock price movement by using K-nearest neighbors, decision tree and random forest algorithms, which are among the non-parametric classification methods of statistical learning. The data used in this research includes information on the stock price of one of the insurance companies during the years 2019 to 2020, which has a suitable and high share in the portfolio of the insurance industry. To determine the accuracy of the studied models, the data were randomly divided into two groups, training and testing. Then, the statistical learning models were implemented on training data and their validity was measured using experimental data.

The research results indicate the high accuracy of all three non-parametric models in predicting the stock price category of the insurance company. Likewise, among the studied models, the K-nearest neighbors algorithm performed better than other algorithms in predicting the direction of stock price movement.

Considering the importance of the risk of investing in an insurance company for customers, attainment to a valid model for stock price classification and specifying the variables that increase or decrease the price can help customers and insurance companies make better decisions.

In this research, our main objective is more accurate pricing of life insurance products with a new approach of predicting mortality or survival rates. Currently, a life table is used to calculate the current value of pensions, insurance premiums, etc. Therefore, to increase the accuracy of our calculations, we are looking for a mortality prediction model for such calculations. Therefore, in this research, instead of static pricing (only using the latest life table), we used life table prediction and dynamically rated life insurance products.

In this research, a new model proposed to predict the probability of human mortality (survival) based on the Markov process, a limited state with an absorption state (death). This model measured based on the physiological age, because the physiological age of each person can be checked based on different laboratory indicators, and finally it has led to the results of the individual health index. In addition, the parameters of this model are the initial probability vector and the sub-intensity matrix of a Markov chain that changes over time. In other words, in this model, according to a possible process in the model, the initial probability vector over time selects the possible interval of the physiological age equivalent to the chronological age.

To show the satisfactory performance of this model, the relevant data set from the United States of America was analyzed. The predicted results with the presented model are better than Lee Carter''''s model. It should be noted that the number of parameters of the model introduced in this research is much less compared to the Lee Carter model and other mortality or survival prediction models. Based on this model, a closed form for life insurance pricing relationships is obtained, which simplifies these calculations for users.

The relationships obtained for pricing were investigated based on two products, 5-year term life insurance and also a 5-year term pension. The fitted results for the model used in the predictions of the probability of mortality as well as the probability of survival and pricing are very satisfactory.

The insurance industry has a great influence on the dynamics and progress of the economy in countries.  The penetration of technologies in the structure of insurance organizations has converted the nature of the insurance business. In this research,  by using of the business ecosystem, the effective factors of insurtech have been tried to be investigated on the blocks of the business landscape that make up the business model of insurance companies, as well as the influence and effectiveness of these factors. 

In this research, after revisal the literature, the effective agents of insurtech on the business ecosystem of the insurance industry were  gathered and provided to the experts in the form of a questionnaire. The statistical society of the research consists of information technology directores and insurance industry research and development managers and insurance industry experts who are related to insurance technology.  In this platform, the opinion questionnaire of experts was completed by 15 experts in this field. After examining the answers of the experts to the questionnaire, 26 sub-criteria affecting the business environment of the insurance industry were determined. Then, the Dematel questionnaire was completed by 8 experts in the insurance industry, and effective factors were identified and ranked by using the combination of network analysis and Dimtel methods.

Based on the results of Dematel analysis method, the criteria of key resources, effective activities, customer orientation, customer sections and channels affect the system. Also, income flow criteria, key partners, value proposition and cost structure are affected by the system. The results of the network analysis method have shown that at the level of the main criteria, the criterion of key partners by a weight of 0.206 ranks first, value proposition by a weight of 0.192 ranks second, cost structure by a weight of 0.185 ranks third, key resources by a weight of 0.125 ranks fourth, customer orientation by The weight of 0.099 ranks fifth, key activities by a weight of 0.097 ranks sixth, channels by a weight of 0.044 rank seventh, revenue stream by a weight of 0.037 ranks eighth, and customer segments by a weight of 0.015 ranks ninth.

Insurance companies should update their business environment to take advantage of Insurtech opportunities. The directors of insurance companies should pay most attention to the company's key partners first and then to the value proposition and cost structure.

 In this article, a life insurance product is designed with the help of stochastic control approach. These products are defined in such a way that in exchange for receiving an amount as insurance premium that is paid at specified times, the insurer undertakes to pay insurance benefits when the insured is alive at the end of the contract.

This research is an analytical study in terms of developmental-applicative purpose. In the literature of life insurance, there are various products that are not the same in the type of benefit payment and the timing of their implementation. Among these examples, term life insurance, term life insurance, and mixed life insurance can be mentioned. Traditional insurance products with fixed benefits are quickly losing their appeal due to inflationary markets. In this research, it is focused on the design of a life insurance product on the condition of life, which is connected to the investment markets. Stochastic differential calculus models have been used to simulate capital markets assets. All the numerical results of this research have been calculated with the help of Matlab and Maple software.

To achieve the best choice of investment type, with the help of stochastic optimal control tool, the best investment strategy was calculated for a person who has the CRRA utility function and buys this product, so that the most benefits are paid at the end of the contract. To invest in this contract, modeling was done in a non-risky market such as a bank and a risky market such as stocks, which have price jumps. In addition, to model the risk asset, the Merton model, which is a representative of the models with finite activity, was used, and at the end, a comparison was made for several mortality functions.

The main purpose of this article is investment for the insured who bought this product. In the product designed in this article, the insurer undertakes to pay the premiums received at a guaranteed rate at the end of the contract. Also, the insured will share the profit from the investment based on a certain percentage that is determined at the beginning of each year. The simulations show that the behavior of the optimal consumption rate is the same as the Merton model with the approach that the behavior of full price jumps is transparent in the optimal consumption rate designed in this article. Investment results for several mortality functions are reported in the Numerical Results section.

The purpose and necessity of conducting research is to examine the difference in the basis of responsibility of legal entities, including the insurer and the bodily injury fund, in terms of the contract and the legality of the full compensation payment by natural persons including the driver who caused the accident and the owner of the vehicle.

The research methodology is based on the study of jurisprudence and legal sources, while adapting to the judicial procedure analysis in the library method. This research has been completed in an analytical-descriptive and practical way to explain the basis of liability of legal entities included in the compulsory insurance law.

From a basic and philosophical point of view, in the new mandatory insurance law approved in 2016, compensation for losses caused to third parties through insurance has been approved. Among all the different theories, the group guarantee theory is the best basis that can be compatible with the philosophy of the aforementioned law.

In Iranian civil law, fault is accepted as the general basis of civil liability. Liability without fault is also an exception to the mentioned rule. However, with the approval of the Islamic Penal Law approved in 2012, and according to Article 528 of that general rule, the liability resulting from traffic accidents changed from fault to danger. In the new compulsory insurance law approved in 2016, cases such as the insurance company, the bodily damage insurance fund, may be identified as responsible for compensating the financial and physical damages to the injured persons due to vehicle accidents. With the description that the basis of liability of the insurance company is based on contractual liability and in compliance with the mandatory rules of the Compulsory Insurance Law approved in 2016, and the basis of the liability of the fund is also the decree of the legislator.