پژوهشنامه بیمه
سال سی و ششم شماره 3 (پاییز 1400)

Feeling happy plays a constructive role in ensuring the mental, physical and physical health of individuals and increasing their motivation and productivity. Hence, happiness has become one of the measures of sustainable development. In this regard, the present study examines the impact of life insurance on the 8 happiness indicators considered by the Legatum Institute (including economy, entrepreneurship, governance, education, health, safety and security, individual freedom and social capital) in selected countries during the period 2018- 2007.

The research model was estimated using multivariate regression analysis, panel data approach and generalized torque method in 8 cases. In each case, one of the happiness indicators was used as a dependent variable in the model.

The estimated results showed that the effect of life insurance development on all indicators of happiness in selected countries is positive and significant.

Life insurance should be designed in such a way that people according to their needs and priorities can use them in various aspects of daily life, including education, health, safety, entrepreneurship, etc., and increase their happiness. Representatives.JEL Classification: G22, J28, I11, I21, D46.

Demographic indicators such as mortality rates play a very important role in health, financial and pension policies. Therefore, the accuracy of mathematical models in estimating mortality rates is an important challenge. One of the tasks of actuaries is to construct a suitable mortality model for the available data so that these mortality models can calculate mortality for different ages and longevity, as well as the different information available to individuals on retirement plans. Missing data is a problem that may be faced by actuaries when they are analyzing the real data. Missing data can occur for a variety of reasons, such as unanswered or censored. The presence of missing data can pose a threat to the accuracy of the data analysis results. The purpose of this study is to model the mortality in a retirement plan. In this regard, it is assumed that data are available at the individual level, including date of birth, date of joining the retirement plan, date of completion of the observation, and reason for discontinuation (usually death or right censoring). Information on covariate variables such as gender, benefits or size of pension, demographic geography or health status will also be available. More precisely, this study aims to model the mortality in a retirement plan based on missing data and access to information from various covariate variables, to carefully analyze the structure of different models, to estimate and finally to investigate the financial implications for different mortality experiences containing missing data.

In this article, we deal with a pension plan in which each member's future life expectancy is modeled using parametric survival models incorporating covariates which may be missing for some individuals. Likelihood-based techniques estimate parameters, and in this regard, an algorithm is proposed that can perform the estimation task in the best possible way. One of the necessary features to check the adequacy of the statistical model, especially when the data contains missing values, is identifiable. If not identifiable, it can be claimed that the statistical model is not a full rank and is not a suitable model for the data. It is worth noting that the Jacobin matrix needs to be calculated to verify identifiability. As mentioned, in the analysis of mortality models with the presence of missing values, the maximum likelihood method can be used. In such cases, an estimation error may often occur when fitting the model, which can be reduced by modeling from a larger population. For this reason, hybrid retirement plans that remain homogeneous are often used. This proposed method can also be useful for calculating financial quantities based on pension factors. In fact, in this proposed method, different data sets with equal or similar death experiences are combined, sample size increases and risk of parameter decreases, which also leads to a reduction in capital requirement. Socio-economic variables such as the level of benefits and geographical characteristics of the population are also considered more if interest rates are low. 

First, complete data are analyzed and modeled for observations of members of a retirement plan, which includes survival time and ancillary variables for each individual. Estimation of parameters is obtained using the maximum likelihood method. however, when the data is missing, it is not easy to estimate the parameters with the maximum likelihood method. In this case, the model parameters are estimated by the maximum likelihood method which are calculated using the proposed algorithm; then, statistical indicators such as identifiability of parameters are calculated to evaluate the performance of the proposed structure and algorithm. Furthermore, the financial effects, in particular the annuity factors, and the mis-estimation risk capital requirements for the mortality experience which includes the maximum covariates variables are calculated and compared with the individual segments when the data are missing. In addition, it can be seen that when the two statistical variables are not observed together, the model is not identifiable according to the data. 

It was found that if the data are missing, the statistical model is not always identifiable using the maximum likelihood, and data combination from two or more experiments can avoid identifiable barriers. The methods proposed in this paper can be useful for actuaries when calculating financial committees based on annuity factors. These methods may combine different datasets with equal or similar mortality experiences, increase sample size, and reduce parameter risk, thus, reducing capital requirements. Socio-economic variables such as the level of benefits and geographical characteristics of the population are given more attention if the interest rate is low.JEL-Classification: C13, C24, C51

Today, customers have become a critical factor in directing investors, producers, and even researchers and innovators. For this reason, organizations need to know about their customers and plan for them. Insurance companies and in general the insurance industry in each country, is one of the most important financial institutions active in financial markets, especially the capital market, which in addition to providing security for economic activities, can play a very fundamental role in providing insurance services. In other words, insurance companies play a vital role in the mobility, dynamic of financial markets and the provision of investable funds in economic activities. In this research, it has been attempted to answer one of the most important questions of insurance organizations, namely, predicting the level of customers’ losses and investing on profitable customers.

Data mining methods were used to discover knowledge to meet business needs and customer relationship management strategies. In addition, an overview of the various applications of data mining in customer relationship management in various insurance companies has been done. In the model implementation stage, a real data-set is used to evaluate the proposed model. To perform the data mining techniques in the insurance industry as data of customers, the vehicle body insurance from 2015 to 2017 has been under investigation. The total number of data used in this study from the beginning was more than 19,356, which during data preparation using Rapidminer 7.1 software became 19,356. After the initial processing, an attempt is made to extract good features from the 15 variables in the data-set that is tangible and help this research in its goal. As a result, by using clustering, drivers are divided into separate clusters based on the amount of loss, and the characteristics of each cluster are expressed. In the clustering section, three algorithms of data mining are examined. First, k-means, k-medoids, and DBSCAN implemented on data-set. Then, the conclusion of three algorithms compared with each other based on the time of calculation and accuracy.

Data mining was a good tool in this research, owing to the large volume of data, to discover the needs and identify customers. The data mining technique which was the main approach of this study fully covered the information needs by methods such as classification, prediction and clustering. The k-means algorithm was selected as the most optimal one in time and accuracy. In the following, the implementation of the algorithms in the modeling step, the decision tree algorithm was selected and by the decision tree related to the forecasting model, it can be predicted future customers by what characteristics would be in what category. It will be valuable for the insurance companies. Using a decision tree, a forecasting model is proposed to help insurance companies to identify profitable customers which can be used for future plans of organizations.

The customer plays an important role in today's industry. Through studying the data obtained from customer behavior, appropriate action can be taken for marketing-related planning and customer acquisition. The use of predictive models and preventive roadmaps has always been one of the goals of the tools that various organizations have been looking for. In this research, the insurance industry as one of the most important pillars of economic in developing countries has been chosen. By reviewing the share of the insurance industry in the economy of a developing country, it can be seen that insurance has a significant role compared to other services.  In this study, the role of insurance companies in optimizing the investment process and ways to expand the interaction between insurance examined. Customers can lead to the growth and development of the insurance industry and the capital market and thus the growth and development of the national economy. Therefore, in the implementation of this research, the data of insurance customers have been used and a forecasting model has been presented. As a good prediction model, the decision tree with 86.21% accuracy was the best model that reached in this study. The insurers’ income criterion is considered as the node root, which shows the used method can help insurance companies make more profit by focusing on profitable customers.JEL Classification: B31, C38, C22, D12

 The insurance industry is a part of the service sector, and it is undoubtedly one of the innovative achievements of human society to compensate for the consequences of burdensome events. Therefore, this research aims to analyze the components of social marketing in the insurance industry.

The statistical population includes active experts in social marketing in the insurance industry, consisting of all experienced managers, experts, and academics in insurance marketing and experienced managers and experts from verified insurance companies. The purposive sampling method was used until theoretical saturation is achieved. Finally, 13 people were selected as the sample. The components of social marketing were identified using qualitative content analysis of interviews.

Then, the validity of identified components was assessed using the fuzzy Delphi technique and questionnaires. First, eight main components and 146 subcomponents were identified based on the results of the content analysis. Then, 113 subcomponents as subordinates to eight main components of social marketing in the insurance industry were selected using fuzzy Delphi.

The main components of social marketing in the insurance industry are as following:Creating new values and ideas: In this component, social marketing is a way of marketing for insurance applied to create value and innovation. This component is formed by putting together subcomponents such as value creation and strategies, competitive advantages of insurance companies, new ideas, and creating and presenting new products.Developing insurance knowledge and insight: This component is built by integrating subcomponents such as risk management knowledge, complete familiarity with insurance, developing public insurance knowledge, familiarity with insurance from childhood, etc.Tactfulness and change in awareness toward insurance services: This component is composed of subcomponents such as revising people tactics and attitude, changing attitudes toward insurance services, attitude toward society’s events, and long-term attitude in the insurance industry, and changes in attitude toward available insurance services, etc.Creating the sense of need for insurance in people: This component includes subcomponents such as creating the need in people’s minds, understanding the needs of the insurance community, designing a product in line with the insurance community’s needs, and identifying insurance needs of people, etc.Society’s culture: This component is composed of subcomponents such as assessing cultural and economic conditions of the society, familiarity with society’s culture, insurance culture building, etc. Society’s culture is an important component that can be directed toward the acceptance of insurance by social marketing.Customer orientation: This component is composed of subcomponents such as encouraging customers to a safe lifestyle, providing customers with personalized insurance policies, creating appropriate groundwork in line with retaining current customers, increasing customer satisfaction, etc.Paying attention to society: This component is composed of subcomponents such as people’s financial backing, supporting vulnerable and low-income people, identifying people’s problems, considering people’s financial ability, etc.Foresight: This component is composed of subcomponents such as stimulating the sense of foresight, providence, providing the people’s future welfare, risk transfer, etc.JEL Classification: M31, G22, C49

The crime of insurance fraud has attracted the attention of American researchers for many years, and the result of their studies has been the success in curbing this crime. The US legal system has not limited the fight against insurance fraud to punishment, but has taken precautionary measures to stifle criminal actions. The establishment of the Special Investigation Unit (SIU) as well as the American Coalition Against Insurance Fraud (ACAIF) are among the preventive and countermeasures in this system. The Special Investigation Unit is required to investigate fraudulent acts of insurance claims. The Coalition Against Insurance Fraud is also a policy-making body that proposes macro-level policies to combat this crime. In contrast, the Iranian legal system has many shortcomings in the face of this crime, both in terms of legislation and enforcement. The philosophy of the insurance institution is to examine the damages that have been caused to other economic institutions for any reason, and this institution is legally obliged to compensate it. If insurance is damaged, the repair of other economic institutions is disrupted and the country's macro-economy becomes a sick economy. The weakness of the Iranian legal system will inevitably lead to serious damage to the country's economic system and its prevention requires legal studies of pathology of the regulations. The present study has focused on the pathology of the existing regulations in the area of insurance fraud and the provision of solutions to deal with this problem more effectively.

The method of this article is desk research, which has been done by referring to articles and books related to the subject under discussion and taking notes from them. We referred to the existing laws in the American legal system to encounter the offence of insurance fraud. Also in this field, the research and legislative background and its evolution are given. Due to the fact that the subject of this article is new and so far, no legal article or book has been written in Persian related to this subject and most of the existing articles and books were economic, it was very difficult to prepare related sources and put it into practice. The translation of English sources has been of particular importance to this. This issue can be criticized considering the importance of the crime of insurance fraud and its effect on the country's economy, and it is better for legal researchers to pay more attention to the important issue of insurance fraud.

Prior to the Compulsory Insurance Law adopted in 2016, instances of insurance fraud were considered fraud. However, after the enactment of this law, the legislator put examples of this conduct under a special title and a great problem became widespread in the Iranian legal system. Since unit criminal conduct requires a single response, discrimination between instances of a single criminal title indicates a lack of desired justice. The legislature is hesitant and helpless in choosing the appropriate title and punishment for instances of insurance fraud. One of the contradictions in the law is the hesitation in adopting the title of fraud or a special title. On the other hand, knowing this fraud has many advantages, such as having the necessary intensity and also minimizing the possibility of using arbitrary policies in its severe examples. From the legislator's point of view, the title of fraud may not be sufficient and appropriate, and the enactment of a special law is deemed necessary to face this crime. Therefore, in 2016, the legislature, in an unprecedented move, included some examples of the mentioned conduct under a special title under Articles 60, 61 and 62 of the Compulsory Insurance Law of 2016 and introduced new titles in the criminal law. Of course, the penalties imposed for insurance fraud in these articles, with the exception of Article 60 of the law, were much lighter than the penalties for fraud, which is a violation of the legislature's intent because only in two cases, The idea of ​​making a lex specialis comes to his mind. One is that the intended conduct at the time of writing the law is not a crime and he seeks to create a new criminal nature. The other is that in his view, the current punishments and actions are not sufficient to deal with the conduct, which in both cases, the punishment and the measures intended for criminal behavior will be more severe than before. In the US legal system, however, a law has been enacted since 1995 called the Model Insurance Fraud Act, which covers various instances of insurer fraud. This law, which was amended in 1998 and 2019, respectively, contains various measures against the fraudster, which, in addition to deterring the offender, also include compensating the insurer. Finally, the existence of disproportionate penalties, the lack of preventive measures, a lack of comprehensive law of compulsory insurance approved in 2016 and its shortcomings against the Model Insurance Fraud Act are the main findings of this study.

Bring all the various instances of insurance fraud within the framework of lex specialis measure the punishment in proportion to the amount taken from the insurer, use the methods of compensation of the insurer as soon as the sentence is issued and as in the example of the Model Insurance Fraud Act, the appropriate and reasonable use of arbitrary policies in minor type of the crime of insurance fraud as well as the application of theories of restorative justice and consensual criminal procedure are among the results of the present study. In order to develop a proper infrastructure in society, politicians are obliged to create a culture in this area so that people believe that insurance serves the people and its philosophy is to use their budget to compensate for damages. JEL Classification: K14, K42, Z18.

The present study attempts to examine the instances of damages to property caused by traffic accidents and the restrictions and conditions governing their reimbursement, which under Compulsory Insurance Act 2016, an insurer will compensate if there is a valid third party insurance and ultimately make suggestions to amend  relevant laws.

The method used in this study is descriptive-analytical using library tools. In this article, first, the issue is examined according to Compulsory Insurance Act 2016 and then the opinions of lawyers on the issue are discussed and finally, along with the analysis and review of opinions in the Iranian legal system, suggestions for amending Compulsory Insurance Act 2016 are presented.

Unconditional definition of damages to property in paragraph b of article 1 of Compulsory Insurance Act 2016 includes damages to third party property (whether object or benefit or right). Of course, damages to property are covered by third party insurance if the property belongs to a third party and is located outside the vehicle causing the accident. According to paragraph 1 of Article 17 of Compulsory Insurance Act 2016, damages to the cargo of the vehicle that caused the accident, whether it belonged to the driver who caused the accident or a third party, were excluded from third party insurance. Moreover, if the damages resulting from the deprivation of benefits (loss of use) can be claimed according to the general rules of civil liability, there is no reason not to claim such damages from the insurer under Compulsory Insurance Act 2016. As a result, the reference of article 39 of Compulsory Insurance Act 2016 only to vehicle, is related to the prevailing case because the property and objects under the ownership or legal possession of third parties that are damaged may not be his only car, as paragraph b of Article 1 of Compulsory Insurance Act 2016, in the definition of damages to property, it refers to "third party property", which unconditional definition includes any property. Also benefiting from paragraph b of article 1 of Compulsory Insurance Act 2016 and the concept contrary to paragraph “a” of article 17 of that law and the provision of article 2 of Law on Immediate Investigation of Damages Caused by Motor Accidents, approved 1966, the loss of market value of a car (price reduction) that results from a car accident is one of the obvious damages that may be suffered by a third party and is therefore covered by third party insurance and can be claimed on the insurer, because in note 3 of article 8 of Compulsory Insurance Act 2016, financial compensation is the responsibility of the insurer or the tortfeasor of the accident. If the price reduction is not considered as damages to property and is not considered compensable by the insurer, the tortfeasor of the accident should not be responsible. In addition, the corresponding damage is the damage that will be calculated corresponding to the damage done to the most expensive conventional car, not to the proportion of the conventional car. In predicting the institution of the corresponding damage, the legislator has departed from the conventional theories about the philosophy and purpose of civil liability and the principle of complete compensation and has attempted to work on the basis of distributive justice as well as ethical and economic considerations and social policy considerations to moderate effect of civil liability for the benefit of the weaker part of society economically. The last part of note 3 of article 8 states a provision that implies the provision of a special system of financial compensation for traffic accidents. Because the corresponding damage to the most expensive conventional car is applicable not only in the presence of third party insurance but also is applied in favor of the tortfeasor of the accident when the car of the tortfeasor of the accident does not have valid third party insurance. A ruling that has no precedent in the Iranian legal system and its justification is impossible in the traditional framework of civil liability and according to traditional principles and rules, especially the no-harm rule. Given that the institution of the corresponding damage is exceptional and irregular, it seems that the application of the specific rule of the corresponding damage is applicable only on the assumption that the subject of the third party's property damages was his vehicle. In cases where damages to property have been incurred on property other than his vehicle, all damages to property, as the case may be, are reimbursed by the insurer of the vehicle causing the accident (up to the limit of the financial obligations of the insurance policy) or by the person responsible for the accident.

The lack of transparency of Compulsory Insurance Act 2016 on some instances of damages to property caused to third parties such as cargo belonging to the occupant of the vehicle, deduction of car price (price reduction) of the damaged property and deprivation of the third party from gaining benefits of the property and the conditions governing their compensation such as the limit of financial obligations contained in the insurance policy and the corresponding damage, have caused doubts and ambiguities which have caused disagreement and conflict of opinions that requires the legislator to intervene to reform the law in this regard. Accordingly, in order to prevent any disagreement and conflict of opinions, it is proposed: 1) Possibility of compensating the price reduction of the vehicle, deprivation of benefits of the damaged property, compensation for damage to the cargo of the occupant of the vehicle should be explicitly foreseen to provide full compensation, which was one of the most important goals of Compulsory Insurance Act 2016. 2) The legislator shall explicitly limit the application of special rule of corresponding damage to the third party vehicle.JEL Classification: K11, K13, K15.

Auto insurances in Iran accounts for a significant share of sales among various types of insurance, ao sensitivity and attention to its future developments is very high. The aim of present study is to identify the influential factors and future characteristics of auto insurance in the world.

A systematic review has been conducted on this study on the future of vehicles, transportation system contain urbanization, and especially insurance. As mentioned, the main goal is to identify the influential factors and future features of auto insurance in the world. For this purpose, the publications of Journal of Insurance Research, NoorMags, Civilica, Science Direct, Emerald, Springer open, Insurance Research Center of Iran, Ayandeban, McKinsey, RAND, PwC, Ernst&Young, Willis Towers Watson, Capgemini databases have been cited, and screening databases based on the words including insurance, vehicle, automobile and transportation has been conducted.

In this study, which started with numbers of documents, 71 articles and 64 reports were deeply reviewed after several screening steps. From a review of 551 trends, mega trends, and factors and their impacts, and identifying causal relationships affecting the future of auto insurance, 197 categories in 31 structures have been identified as influential factors. They were categorized into six categories: technology, environmental, social, economic, political, legal, and business.

According to the study, numbers of factors will directly or indirectly change the future of auto insurance. JEL Classification: G22, O32, M10