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The performances of insurance companies can be investigated using multivariate analyses where the specific financial variables are used. The results of the analysis may change according to the method and the variables used in. Widely used input and output variables can be determined through the literature review or some approaches. However, before to performance measurement, variable selection using statistical methods is important with regard to determining significant variables. In this study, determinants that measure the financial performance of 30 non-life insurance companies operating in a five-year period in Turkey are investigated by correlation analysis, regression analysis and a five-year panel data analysis. Because of inconsistency in the results of regression analysis, variables are selected according to panel data analysis. Results of panel data models based on all variables and only significant variables are compared. It is noticed that the model with financial ratios instead of trasformed variables using some transformations explains the financial performance better.

In this study, the demand for comprehensive insurance is analysed using utility theory and system simulation. A simulation study is performed to assess the behaviour of individuals with different income levels for the demand of comprehensive insurance. Simulation assumptions and input-output variables are determined using the real data set from a Turkish insurance company and the report about the insurance activities in Turkey for year 2014. The effects of income level, expected claim severity and premium level on the demand for insurance are investigated. It is concluded that while an increase in income level and expected claim severity causes an increase in the demand, an increase in premium level causes a decrease in demand.

In this study, financial performances of certain selected non-life insurance companies operating in Turkey between 2014-2018 are compared with a combination of two multi-criteria decision-making methods. The analytical hierarchy process is integrated into the grey relational analysis. First, the weights of the financial ratios are computed with the analytical hierarchy process, and then the companies are ranked by grey relational analysis using the weights found. The grey relational analysis results in which the financial ratios are taken as equally weighted and in which the weights found with the help of the analytical hierarchy process are compared. It is realized that the grey relational analysis results are affected by the weight of the financial ratios calculated with the analytical hierarchy process. In addition, it is observed that the ranking obtained by the combination of grey relational analysis and analytical hierarchy process is more compatible with the data in the financial reports.

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