In this paper, we assess the impact of the COVID-19 crisis on the bankruptcy risk of a sample of 100 hotel companies and, consequently, on the hotel industry in the Republic of Serbia. The assessment applies to the period, 2019–2026, with the use of the data on the financial indicators for 2015–2020. Five novel structural time-series models, which have the indicators derived from Altman’s EM Z”-score model as predictors, were used, and a new conceptual framework for assessing bankruptcy risk is provided. The framework expands the applicability of credit-risk-scoring models to multiyear predictions, and it takes into account the dynamism of the transitions of the firms among Altman’s risk zones. The predictions that were obtained when the Springate and Zmijewski scores were applied along with the Altman Z”-scores demonstrate the fair applicability of the scores for the models that are introduced here. The results of the models were confirmed by 270 artificial neural networks and they were compared to the results of the classical time-series models. The crisis started to have a negative effect on bankruptcy risk in 2020, and this effect is expected to rise until 2023; currently, in 2022, the highest number of hotel companies may be headed for bankruptcy. Amelioration in the position of the companies cannot be expected before 2024; however, even in 2026, the risk of bankruptcy will remain high when compared to the pre-COVID-19 period and, thus, the surviving companies will become more fragile to any further exogenous changes. These results provide a basis for the adaption of state-supported measures and business policies in order to withstand the crisis and to ensure sustainability.

In recent decades, predicting company bankruptcies and financial troubles has become a major concern for various stakeholders. Furthermore, because financially sustainable businesses are affected by numerous highly complex factors, both internal and external, the situation is even more complex. This paper applies Altman’s Z-score models; more precisely, the paper applies the initial Z-score model (a model for manufacturing companies), the Z′-score model (for companies operating in emerging markets), and the Z-score bankruptcy probability calculation. Therefore, this paper offers the results of the application of different Z-score models and the calculation of bankruptcy probability on a sample of agricultural companies listed on the Belgrade Stock Exchange in the period 2015–2019. In addition, different Z-score models are used for the same sample so that the difference between their results and application can be determined. In addition, the validity of the data published in the financial statements of the respective companies was confirmed using the Beneish M-score model with five and eight variables. The results obtained by applying Altman’s Z-score model (initial and adapted to emerging markets) indicate that a certain number of companies had impaired financial stability during the observed period, i.e., that they were in danger of bankruptcy. In addition, based on the results obtained using the Beneish M-score model, it was identified that a number of companies showed signals that indicate possible fraudulent financial reporting. Further, it was found that less than half of the observed companies reported on environmental protection in their annual reports, and they did so by providing a modest amount of information. The originality and value of the paper lies in suggesting that policymakers in the Serbian emerging markets should pay more attention to the operations of companies from the observed sector, as well as to their financial and non-financial reporting. Future research should focus on comparisons with agricultural companies from the same sector whose securities are listed on stock exchanges in the region.

Research Question: This paper reviews different artificial intelligence (AI) techniques application in financial risk management. Motivation: Financial technology has significantly changed the business operations which required transformation of financial industry. The financial risk management needs to be restructured because the methods that have been used in the past became low effective. The artificial intelligence techniques proved their efficiency and contributed to fast, low–cost and improved financial risk management in both financial institutions and companies. Idea: The aim of this paper is to present a state of AI techniques application in financial risk management, as well as to point out the direction in which further application and development could be expected. Data: The analysis was conducted by reviewing various papers, books and reports on AI applications in financial risk management. Tools: The relevant literature systematization was used to provide answers to the question to what extent AI techniques (especially machine learning) could be used in managing financial risk management. Findings: Artificial intelligence largely improved the market risk and credit risk management through data preparation, modelling risk, stress testing and model validation. Artificial intelligence techniques can be useful in data quality assurance, text-mining for data augmentation and fraud detection. The financial technology will continue to affect the financial sector through requiring the adaption to new environment and new business models. Because of that, it could be expected that artificial intelligence will become part of the financial risk management framework. Contribution: This paper provides a review of artificial intelligence applications in market risk management, credit risk management and operational risk management. The paper identified the key AI techniques that could be used for financial risk management improvement because of financial industry transformation.

Scholars have emphasised the importance of green settings in today’s business paradigms. Studies on green behaviour have produced a plethora of noteworthy discoveries, whether focused on financial success, individual capabilities, or development. However, despite significant growth in interest in green business practices, the relationship between individuals’ willingness and green competencies has received little attention. This article used the customised green competencies conceptual model to investigate how green skills influence organisational performance and their relationship with the willingness moment. This article developed an innovative human resource management approach to address these difficulties. A questionnaire was used to perform empirical statistical research with 516 respondents from Serbian universities. Different mathematical and statistical methodologies were used to analyse the results. The findings corroborate the suggested theoretical model, and they suggest that green competencies will influence people’s willingness to participate in green activities. This article gives new information on human behaviour and organisational effectiveness in a green atmosphere. It includes managerial and practical consequences and recommendations for businesses looking to improve their social responsibility and environmental sustainability.

This study proposes implementation of Boolean consistent fuzzy inference system for credit scoring purposes. Fuzzy inference system (FIS) allows domain experts to express their knowledge in the form of fuzzy rules, which enables combination of automatic rating with human judgment. Crucial for this model is that fuzzy rules are being evaluated using Boolean consistent fuzzy logic, which preserves all Boolean axioms. Experimental results show that the Boolean consistent FIS outperforms the conventional FIS in terms of classification accuracy, precision, and recall. Consistent fuzzy logic could contribute to the rightful approval of more loans which in turn would have positive effects on economic growth.