This study aimed to explore the cross-section of digitalization and sustainability in banking and its effect on bank performance. The sample consisted of all of the banks (n = 25) operating in the Republic of Serbia from 2011 to 2020. The research results show that the banks focusing on digitalization and sustainability are profitable, even in the face of coronavirus disease 2019 (COVID-19). Furthermore, using the Pearson’s correlation, the study shows that the level of investment in digital transformation has a strong relationship with the net result. We advocate that digitainability in banking is an important factor in uncertain times and should be fostered and included in bank strategies in the post-COVID 19 world. To the best of our knowledge, this is the first study that provides insight into digitainability and bank performance.

Purpose: This study aims to explore the factors moderating possibly indirect relationships between gender diversity and its effect on bank performance. The causality of this relationship remains unclear. Design/methodology/approach: The sample consists of all banks (n = 27) operating in Serbia. Findings: The gender diversity-performance relationship is indirect. The gender diversity of executive boards positively impacts bank performance, over a threshold level. This is observed only in banks where gender diversity is extended to more than one level of executive authority. Research limitations/implications: Gender diversity should be fostered, particularly in small and competitive markets. The gender diversity-performance link is based on gender-related social interactions, which are interdependent and should not be taken into account as isolated factors. Originality/value: To the knowledge, this is the first study to provide insight into indirect, gender related, moderatory interactions effecting gender diversity – performance link, in banking.

Background and purpose: The goal of the paper is to determine the level of concentration in the insurance sector in the following eight countries of South and Eastern Europe: Serbia, Croatia, Bosnia and Herzegovina, Montenegro, Former Yugoslav Republic of Macedonia, Romania, Bulgaria and Albania in the period from 2007 to 2012. Design/Methodology/Approach: In this context, the analysed indicators of concentration were the market share of the four leading financial institutions (CR4), the Herfindahl-Hirschman Index (HHI), the coefficient of entropy (E), the coefficient of relative entropy (RE) and Gini coefficient (G). Results: The study showed that the insurance sectors in the analysed countries are highly concentrated on average (according to CR4 indicator), medium concentrated (according to HHI) with high levels of inequality of distribution of market shares between individual participants (in terms of G coefficient), and in the zone of relative uniformity and equality of business entities (according to RE coefficient). The research results point out that the existence of different levels of correlation between the analysed indicators of concentration in the insurance sector, which confirms the conclusion that, in order to obtain relevant and quality conclusions about the level of concentration, it is necessary to review and analyse several indicators of concentration integrally. Conclusion: In all observed indicators of concentration in relation with the density level GDP pc move in the zone of very low value, which on the one hand points to the fact that the analysed countries at a relatively similar level of development have significantly different levels of concentration, but also on the fact that some countries although at different levels of development, have similar levels of concentration.

The advancement of technology has led humanity into the era of the information society, where information drives progress and knowledge is the most valuable resource. This era involves vast amounts of data, from which stored knowledge should be effectively extracted for use. In this context, machine learning is a growing trend used to address various challenges across different fields of human activity. This paper proposes an ensemble model that leverages multiple machine learning algorithms to determine the key factors for successful foreign direct investment, which simultaneously enables the prediction of this process using data from the World Bank, covering 60 countries. This innovative model, which adds to scientific and research knowledge, employs two sets of methods—binary regression and feature selection—combined in a stacking ensemble using a classification algorithm as the combiner to enable asymmetric optimization. The proposed predictive ensemble model has been tested in a case study using a dataset compiled from World Bank data across countries worldwide. The model demonstrates better performance than each of the individual algorithms integrated into it, which are considered state-of-the-art in these methodologies. Additionally, the findings highlight three key factors for foreign direct investment from the dataset, leading to the development of an optimized prediction formula.

Consolidation of financial markets used to be the trend on the global market in the previous three decades up to the recent economic crisis. Global phenomena, technological innovation, liberalization and deregulation used to be the factors which made the process of mergers and acquisitions in the financial sector much easier. In the banking sector of Southeastern Europe, in a turbulent environment, changes are occurring on a daily basis, forcing the banks to adjust to the new situation. The restructuring process is related to the various activities whose main result is the changed ownership structure and business strategy with a view to maximizing profitability and improving efficiency. The aim of our research is to present the banks' restructuring processes in the region of Southeastern Europe before and during the crisis, as well as to stress the fact that the ownership transformation, followed by an increased share of foreign ownership, affected the changes in the market structure and its share in total equity (in the banking balance sheet size).

Many methods exist for solving the problem of evaluating efficiency in different processes. They are divided into two basic groups, parametric and non-parametric methods, which can have significant differences in the results. In this study, the authors consider the process of assessing the business climate depending on realized foreign investments. Due to the expected difference in efficiency assessment using different approaches, the goal of this paper is to create an optimization model of an ensemble for efficiency assessment that uses both types of methods with the aim of creating a symmetrical approach that achieves better results than each type of method individually. The proposed solution simultaneously analyzes the impact of different factors on foreign investments in order to determine the most important factors and thus enable each local government to ensure the best possible efficiency in this process. The innovative idea of this study is in the inclusion of classification and feature selection methods of machine learning to fulfill the set goal. Our research, focused on a specific case study in various cities across the Republic of Serbia, evaluated the effectiveness of that process. This study extends previous research and confirms the published results, highlighting the advantages of the newly proposed model.