Prediction insolvency is one of the most important issues during creditworthiness assessment, especially in the turmoil environment. That is why the problem of insolvency and bankruptcy prediction has been the subject of numerous studies focused on its causes, consequences, and prediction. The main goal of the study was to develop a prediction model that can be effectively used in practice to analyze and signal the risk of insolvency and bankruptcy of a construction firms. Also, the research must identify the key factors that would allow for early identification of the symptoms of the upcoming financial failure of companies from a construction sector. To reach the goal of the study discriminant analysis, logistic regression and classification trees were used. The final estimated models included nine variables related to the profitability; revenues; liquidity; asset’s structure; and dynamics of own and foreign capitals, some of which referred to the industry and market situation in a construct sector, which is a novelty compared to previous research. What is more, results show that the method chosen to estimate the insolvency prediction model could have an impact on both partial and general effectiveness in the process of creditworthiness assessment.

As the global economy undergoes rapid digital transformation, cryptocurrencies have emerged as a prominent alternative class of financial assets. Their decentralized nature, pseudonymity, and lack of centralized oversight have attracted considerable interest among investors while simultaneously raising significant concerns among regulators and compliance professionals. While cryptocurrencies offer benefits such as enhanced accessibility and transactional privacy, they also pose notable risks, particularly their potential misuse in financial crimes, including money laundering. This study explores the perceived risks associated with cryptocurrencies in the context of money laundering, drawing on insights from a survey conducted among 50 financial sector professionals. A quantitative research design was employed, using a structured online questionnaire to assess participants’ awareness, investment behavior, and perceptions of the role of cryptocurrencies in illicit finance and financial system security. The results reveal a complex perspective: while 70% of respondents acknowledged the potential for cryptocurrencies to facilitate money laundering, 60% expressed support for their wider adoption. Notably, statistically significant correlations emerged between active investment in cryptocurrencies and the belief that they could enhance financial market security and reduce laundering risks. However, self-reported knowledge levels and general awareness did not show a significant relationship with perceived risk. The findings underscore the importance of a balanced approach to regulation, one that fosters innovation while mitigating illicit finance risks. The study recommends increased investment in user education, the development of blockchain analytics, the adoption of global regulatory standards and enhanced international cooperation to ensure the responsible evolution of the cryptocurrency ecosystem.

The main aim of the research was to determine the key factors determining the level of credit risk of individual clients (clients in the form of natural persons, excluding companies) on the example of Polish cooperative banks according to the following features: transaction characteristics, socio-demographic characteristics of the customer, the customer’s financial situation, the customer’s history of cooperation with the cooperative bank where they applied for a loan, and the customer’s history of cooperation with other financial institutions. For the research gathered data from 1000 credit applications submitted by individual customers when applying for a credit in five different cooperative banks were used for the analyses. To assess the credit risk of retail clients we use logit regression models, and additionally, score cards were calculated. The results of the research indicate that among the factors with high predictive power there were the features characterizing the client’s history of cooperation with the cooperative bank, where they applied for a loan. It may mean that when assessing credit risk related to financing individual customers, cooperative banks due to their local character, have an advantage over other financial institutions.

Aim: The study aims to find an answer to the question of whether and how the economic indicators in the industrial processing, construction, trade, and services sectors reflect changes in the market size related to investments and working capital loans for micro-enterprises. Methods: The study used data on investment and working capital loans and data on economic conditions. Monthly data for the years 2019–2021, covering the period of the COVID-19 pandemic, were used for the analyses. The estimation of dynamic models was carried out using the Generalized Method of Moments (GMM). Results: The conducted research showed that economic factors play a significant role in shaping the demand for investment and working capital loans in the micro-enterprise sector, but the impact of the economic situation was more significant in the case of investment loans than working capital loans. In periods of economic prosperity, micro-enterprises are more likely to take out investment loans than in periods of economic downturn. Micro-enterprises’ propensity to take out working capital loans is more dependent on their current situation and expectations regarding the demand for products and services. Conclusions: The research revealed differences in the characteristics of the factors significantly influencing the demand for credit, depending on the sector to which the indicator is concerned. The research shows that access to external financing is crucial for the functioning, survival, and expansion of micro, small, and medium-sized enterprises. The research fills the literature gap by showing how the economic situation in individual industries affects the credit market for the micro-enterprises sector.

The study aimed to identify the dependence of the financial results of Poland’s transport and warehousing sector on the current macroeconomic situation and security conditions related to the war in Ukraine. The econometric method was used – a dynamic panel model. Data from January 1, 2007, to June 30, 2023, from the Central Statistical Office and the National Bank of Poland were used. It was found that the impact of macroeconomic factors on the financial efficiency of individual industries in Poland’s warehouse and transport sector varies in terms of direction and strength of effects. Key factors were identified as macroeconomic determinants of the financial condition of this sector and industries (subsectors) sensitive to hostilities in Ukraine were indicated. The implications of the findings for decision-makers, investors, businesses and researchers are outlined.

W artykule dokonano oceny zależności między dynamiką obliga kredytowego a efektywnością banków spółdzielczych. Badania przeprowadzono na grupie 150 banków spółdzielczych. Z przeprowadzonych analiz wynika, że banki spółdzielcze charakteryzujące się najwyższym poziomem dynamiki obliga kredytowego osiągały najwyższy poziom efektywności, wyrażonej jako rentowność aktywów, rentowności funduszy własnych oraz C/I. Natomiast najwyższą efektywność pod względem poziomu marży finansowej uzyskiwały banki o najniższym i średnim poziomie dynamiki obliga kredytowego.