This paper has proposed a data mining approach for risk assessment in car insurance. Standard methods imply classification of policies to great number of tariff classes and assessment of risk on basis of them. With application of data mining techniques, it is possible to get functional dependencies between the level of risk and risk factors as well as better results in predictions. On the case study data it has been proved that data mining techniques can, with better accuracy than the standard methods, predict claim sizes and occurrence of claims, and this represents the basis for calculation of net risk premium and risk classification. This paper, also, discusses advantages of data mining methods compared to standard methods for risk assessment in car insurance, as well as the specificities of the obtained results due to small insurance market, such is the one in Montenegro.

The growing importance of life insurance in the world imposes a greater need for research in this area, particularly in the Western Balkans where the trend of growth has been closely accompanied by life insurance for the past two decades. Taking into consideration that life insurance companies are significant participants in the financial market, this research paper examines the impact of the premium reserve on the volume of financial investments of life insurance companies in Western Balkan countries, based on aggregate data on country level. In order to test its effect, linear correlation and regression models were used, based on data collected for the period 2006-2016. Additionally, comparative analysis was used to compare the position of life insurance companies in financial markets. The results obtained by applying correlation and regression analysis showed that there is a strong positive correlation between premium reserve and financial investments in all of the aforementioned countries in the region. This result is an important strategic guideline for the regulators and policymakers to make advancements in the life insurance sector as well as in the financial market of the Western Balkans.

This paper has proposed a data mining approach for risk assessment in car insurance. Standard methods imply classification of policies to great number of tariff classes and assessment of risk on basis of them. With application of data mining techniques, it is possible to get functional dependencies between the level of risk and risk factors as well as better results in predictions. On the case study data it has been proved that data mining techniques can, with better accuracy than the standard methods, predict claim sizes and occurrence of claims, and this represents the basis for calculation of net risk premium and risk classification. This paper, also, discusses advantages of data mining methods compared to standard methods for risk assessment in car insurance, as well as the specificities of the obtained results due to small insurance market, such is the one in Montenegro.