A nonparametric data mining approach for risk prediction in car insurance: a case study from the Montenegrin market

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

Analysis of investors’ preferences in the Montenegro stock market using data mining techniques

This article analyses the preferences of different types of investors to stock characteristics in the Montenegrin stock market. The majority of papers deal with stock portfolio analysis of the institutional investors. Since the number of individual investors in the Montenegrin market is much higher, the analysis of their trading behaviour is also very significant. In this article, using data mining techniques, we tested trading behaviour with stocks for both types of investors. We prove that data mining techniques, such as logistic regression, clustering and ecision trees, provide good results in this type of analysis. The analysis may be useful to the future investors, brokers and stock exchange

Analysis of the Diffusion of E-services in Public Sector Using the Decision Tree Method

The results of the this study showed that there is a difference in individual and interactive impact of technological, organizational and environmental factors on the diffusion of e-services in the public sector, as well as a difference regarding the factors that independently impact the diffusion of e-services oriented to citizens and those oriented to business. The results of the study also showed that external factors have a predominant impact on the diffusion of e-services oriented to citizens, while, in the case of e-services oriented to business, in addition to external factors, technical factors also have a high impact. For the purposes of this paper, based on the Technology-Organization-Environment framework (TOE), the conceptual model suitable for e-services in the public sector is developed, while the Decision Tree (DT) Method is used for testing the effects of the proposed variables. This study offers valuable inputs both for the government, for creators of national strategies oriented towards the promotion and support of the availability and usage of electronic services in the public sector, which is very important especially for developing countries. This work is licensed under a&nbsp;Creative Commons Attribution-NonCommercial 4.0 International License.</p

Drivers of E-Business Diffusion in Tourism: A Decision Tree Approach

The aim of this paper is to examine the individual and interactive influence of organizational, technical and environmental factors on the diffusion of e-business in Montenegrin tourism organizations. In accordance with this goal a decision tree method is used in this paper. The results showed that organizational factors have the highest individual impact on e-business diffusion and that there is a strong interactive influence of organizational, information technology integration and external support factors. The obtained results provide important information for management and decision-making in conditions when diffusion of information technology innovation needs to be accelerated in such a way that instead of influencing only one group of factors, it affects several factors simultaneously which, in interaction with one another, lead to a greater degree of e-business diffusion

Customer Response Model in Direct Marketing: Solving the Problem of Unbalanced Dataset with a Balanced Support Vector Machine

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

Customer Response Model in Direct Marketing: Solving the Problem of Unbalanced Dataset with a Balanced Support Vector Machine

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&rsquo; 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&rsquo; 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

COMPARATIVE ANALISYS OF AGRO-FOOD TRADE IN MONTENEGRO AND EU CANDIDATE COUNTRIES

The aim of the paper was the analysis of the competitiveness and changes in the foreign trade of the Montenegro from 2006-2013. The main contribution of the work was supplemented by the comparison of the Montenegro agro-food trade performance with candidate and potential candidate EU countries. Taking into account the many differences among the analyzed countries we used an unconventional methodical practice based on the calculation of trade balance per one inhabitant. During the analyzed period, only 2 countries exceeded the level of 100% self-sufficiency and permanently reached the positive trade balance with agricultural and food products. As to the “self-sufficiency level” calculated on the basis of the average trade balance per one inhabitant in 2006–2013 within the candidate and potential candidate EU countries the last position belongs to the Montenegro closely before Albania and Bosnia and Herzegovina

The interaction between social media, knowledge management and service quality: A decision tree analysis.

The existing literature fails to identify to which extent the utilization of social media could be relevant for increasing the effectiveness of knowledge management, in respect to overall business operations. In order to shed some light on this area we define three goals. Firstly, we investigate to what extent the different activities of clients on social media (SM), are important to the processes of knowledge management (KM) in companies. Secondly, we examine to what extent KM functions can be relevant in attaining the quality of IT services. Thirdly, we analyze to what extent KM mediates between SM and the quality of IT services, that is, which client activities on SM should be formalised in the form of KM processes so as to influence the quality of IT services. In order to asses these goals, the decision tree method was used at the sample of B2B companies, more specifically at the sample of those companies offering Knowledge Intensive Business Services (KIBS). The study has shown that: (i) SM client activities have the largest importance for building efficient KM; (ii) KM functions are relevante to the overall quality of IT services, and (iii) those SM options and activities have been identified, whose importance for the assessment of the quality of IT services is indirectly transmitted through KM. This paper offers new empirical evidence which can lead to a better understanding of the role of KM in KIBS. Thanks to the obtained findings, managers will be able to define the goals of their companies in relation to the utilization of SM, more specifically: their presentation on SM, monitoring the outcomes of the SM, usage improving their KM practices and, thus, define strategies to increase the quality of the IT services they offer

A machine learning approach for time series forecasting with application to debt risk of the Montenegrin electricity industry

Level of customer electricity debts is a relevant information for the electricity production company, as it represents uncollected revenue for the provided service. Higher level of debts may affect the provider’s financial stability and the ability to invest and maintain their network. On the other hand, high levels of debt may indicate larger macroeconomic problems, such as the lower standard of the citizens or high unemployment. In this paper, a Machine Learning approach for electricity debt prediction was applied, using Support Vector Regression method and data from the Montenegrin electricity provider. The obtained results indicate an excellent model performance, proving that the chosen method is an outstanding choice for this task, compared to other machine learning methods. The forecast of electricity user debts using machine learning techniques adds a new research area to the existing research, as the previous literature mostly concentrated on the prediction of electric load, consumption, and demand. The risk of default may be larger in lower income nations, as is the case in this research, therefore risk prediction and mitigation are crucial for the power supplier in these countries. The results obtained in this research on the test set are 1.63% and 0.854, for Relative Error and R2, respectively, showing excellent predictive performance. Additionally, correlation coefficient is close to 1 in cross-validation and on unknown data. Thus, it can be confirmed that the debt prediction was efficient