Applying Bayesian networks in analysis of actuarial risks

Actuarial risk is an important part of analysis actuarial science, because it relates to different types of uncertainties in finances. It emerges from incorrect calculations or given hypotheses, which were provided in specific model. To efficiently estimate probability of any type of risks and its power of outcome in case of occurrence actuaries use broad range of mathematical and statistical methods and algorithms. Estimation of actuarial risk is very important because it allows people to make optimized decisions. The growing volume of information imposes increasing demands on time and resources required by domain experts to extract relevant insights. This case becomes more difficult due to such data uncertainties as noisy or missing data, outliers and leads to forecasts with low precision. Bayesian methodology is considered as the most known modeling techniques that allows for reduce uncertainty. Networks can use statistical data and expert ratings. It gives an understanding of relationship between drivers of the process and in-cludes statistic, parametric and structural uncertainty. Models are fully compatible with human actions during decision-making. They can be applied in different areas, especially in finance. The implementation of this method includes defining the topological structure and probabilistic inference. The latter one is quite complicated, because of network structure. Different algorithms of probabilistic inference give different results. In this experiment LS-method was implemented. Real actuarial data from insurance company were used for construction. Approach of empirical distribution was applied for building conditional probability tables. Also, logistic regression was implemented as a classical classification algorithm. For different queries Bayesian network demonstrated better results in most cases than logistic regression. In future studies it is suggested to build intellectual decision support system, which includes this type of Bayesian network.