For prediction of risk in car insurance we used the nonparametric data mining techniques such as clustering, support vector regression (SVR) and kernel logistic regression (KLR). The goal of these techniques is to classify risk and predict claim size based on data, thus helping the insurer to assess the risk and calculate actual premiums. We proved that used data mining techniques can predict claim sizes and their occurrence, based on the case study data, with better accuracy than the standard methods. This represents the basis for calculation of net risk premium. Also, the article 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 as Montenegrin.

Environmental degradation by greenhouse gas (GHG) emissions has been an important challenge of sustainable economic development and climate changes control. Industry is the major source of CO₂ emissions, whereas 84% of global anthropogenic methane and nitrous-oxide emissions emerge from agriculture. The impact of agro-economic factors on GHG emissions in European developing economies (Southeastern Europe in focus) as compared with European advanced economies has been examined in this paper. The results have confirmed the existence of significant differences in impact of these factors depending on the level of economic development. For both groups of economies, we have confirmed the Environmental Kuznets Curve (EKC) hypothesis (inverted U-shaped relationship between GDP per capita and carbon dioxide emissions), but different sectoral outputs, too. We have also established different impacts of agro emission sources. In developing economies, we have recognized livestock breeding as a predominant factor and recommended measures for reducing the emissions in this sector, following developed economies. The findings may be useful to European developing economies as a support to implementation of binding commitments emerging from the UN Framework Convention on Climate Change (UNFCCC). In the panel analysis, we have taken into consideration the non-stationarity of the series, heterogeneity of the sample, and also examined a dynamic specification.

The transition to green economies is considered to be a key driver of sustainable economic development. Growing awareness of environmental costs and their integration into corporate performance reporting has led to the development of a new environmental dimension of accounting. Although it is a modern concept, its integration into the tourist business community is important, especially because tourism is recognized as energy and emission intensive. As preserving the environment can create an optimal framework that is in the function of the development of sustainable tourism in Montenegro, this paper discusses and analyzes the degree of knowledge of tourism companies on the application of the green concept, especially through the prism of socially responsible business. The research study was conducted by applying the classification Decision tree model (DT) on the data obtained by surveying employees and owners of Montenegrin tourism companies. The DT method has shown that socio-demographic factors have a significant impact on understanding the importance of green accounting in the tourism sector of Montenegro and that most tourism companies include corporate social responsibility in their business strategy and policy. The fact that the same or similar research on this topic has not been done in Montenegro, but also in neighboring countries, indicates an important contribution that this paper may have for the scientific and professional community, primarily in the form of incentives to create and publish reports on environmental protection.

This paper proposes a predictive approach to segmenting credit card users, based on their value to the bank. The approach combines the Recency, Frequency and Monetary (RFM) method, clustering using the k-means method, and predictive classification by the Support Vector Machine (SVM) method. Clustering by non-encoded RFM attributes overcomes the subjectivity in selecting the number of segments and losing information (small differences in the values of these attributes) which are problems of classic RFM segmentation. In order to overcome the problem of class imbalance in predictive classification (which occurs due to the small number of valuable customers), the Support Vector Machine (SVM) method was applied as a pre-processor of data due to its extraordinary generalization capabilities. The end result of predictive classification should be a set of rules that describes the identified customer segments in order to tailor the offer to each segment individually. The extraction of rules from the SVM output was achieved using the Decision Tree (DT) classification method. Using a proposed approach that addresses the issue of the small class, marketing managers can more effectively target the most valuable customers, thereby increasing revenue, but also reducing unnecessary costs due to wrongly targeted valuable clients.

Customer response models have gained popularity due to their ability to significantly improve the likelihood of targeting the customers most likely to buy a product or a service. These models are built using databases of previous customers’ buying decisions. However, a smaller number of customers in these databases often bought the product or service than those who did not do so, resulting in unbalanced datasets. This problem is especially significant for online marketing campaigns when the class imbalance emerges due to many website sessions. Unbalanced datasets pose a specific challenge in data-mining modelling due to the inability of most of the algorithms to capture the characteristics of the classes that are unrepresented in the dataset. This paper proposes an approach based on a combination of random undersampling and Support Vector Machine (SVM) classification applied to the unbalanced dataset to create a Balanced SVM (B-SVM) data pre-processor resulting in a dataset that is analysed with several classifiers. The experiments indicate that using the B-SVM strategy combined with classification methods increases the base models’ predictive performance, indicating that the B-SVM approach efficiently pre-processes the data, correcting noise and class imbalance. Hence, companies may use the B-SVM approach to more efficiently select customers more likely to respond to a campaign.