Agriculture is a determining factor regarding the development of the Romanian economy, noting its importance for population consumption and as a supplier of raw materials for the relaunch of other industries. Agricultural financing consists of credits granted to natural or legal persons for developing agricultural activities, expanding agricultural holdings, and commercializing agricultural production. The objective of this research is the statistical analysis of the determining factors in granting loans to Romanian farms. The study is based on the content analysis of the accounting reports of the 45 Romanian farms included in the research sample, based on which the profile of the farmer from the selected counties (Alba, Cluj, Mures, Sibiu, Dambovita and Prahova) is outlined. The obtained results highlight the fact that factors such as the requested amount (SUSO) are directly influenced by the worked area (TELU), by the turnover (CIAF), R = 0.6228, but also by the total value of the assets (TOTAL) R = 0.454. At the opposite pole, there is a weak correlation between SUSO and current liquidity (LICU), R = 0.2754, and the value of recorded expenses (CHEL), R = 0.3102. Implementing a credit policy that facilitates access to financing sources would support farms in modernization and development, increasing their competitiveness and general viability.

The need to expand the range of banking services in Ukraine is stipulated with technological progress, the European integration processes and the legal regime of martial law introduced in the country. Under the conditions of war, the need to strengthen the security of banking activities and protect the banking system from the influence of any internal and external factors gains meaning. The topical direction of economic and legal research of scientists today is the possibility to introduce digital technologies with elements of artificial intelligence (AI) into the banking activity in Ukraine to improve its protection. The AI law as an independent branch of the Ukrainian law has not been developed so far. The sources of AI law, its functions, tasks, scope, risks and limits of legal responsibility for prohibited practices of artificial intelligence have not been defined. The purpose of the article is to analyze the theoretical and legal provisions that underpin the regulation of AI application in Ukrainian banking. The comparative legal method made it possible, considering the provisions of the draft law on AI of the European Union, to determine the trends in the development of the legal regulation of AI in Ukraine. Following the study, proposals to the legislation of Ukraine were formulated, which will contribute to the legal regulation of banking activities using digital technologies with elements of AI.

Ancient Minipe Anicut, Sri Lanka is world-famous for its engineering excellence. Due to its importance, conserving the ancient anicut, another anicut was constructed downstream in the 20th century. Nevertheless, the water diverted from the ancient anicut to the Minipe Left Bank (LB) Canal was kept as it was due to inherited agricultural importance. This research focuses on studying the contributions made by the adjacent catchment along the Minipe LB Canal. There are several level crossings along the Minipe Left Bank Canal from which the runoff of the local catchment flow into the Minipe LB Canal. Hydrologic Modeling System (HEC-HMS) is used to obtain the yield from each catchment into the Canal, which was compared with the annual diversions from Minipe anicut. The total yield from each stream has been compared with the annual diversion of the Minipe LB Canal from 2014 to 2020. The results obtained from this study reveal that there is sufficient water available for water augmentation in the basin, with an estimated annual average cumulative yield from the catchment of 453.6 MCM. This cumulative yield is 1.7 times the annual average diversion from the Mahaweli River, which is 271.9 MCM. With the findings, it is concluded that there is a potential to augment water from the catchment to address pertaining water shortages conveyance in the command area.

Despite the unpleasant conditions, such as lower and insufficient wages, higher working hours, longer length of service, total absence of casual workers union, indirect employment aided by the bank’s top directors, etc., casual workers are highly committed in performing their roles and achieving their organizational goals. Neoliberal theory and Equity theory were used for guidance in this study. The study employed qualitative analysis style; Total of ten banks were selected as a sample of the study involving sixty participants who were all casual workers; twenty-four female and the remaining thirty-six were male, have been selected using purposeful sampling. Content analysis was used as the method of data analysis. The study shows some of the functions performed by the casual workers of Nigerian banks include quick client service, amenability to work, client care services, opening of accounts, marketing, and timely task completion. Others include furnishing prompt client service, being prepared to work, and finishing assignments on schedule., thus, the study concludes that despite the outstanding performance of casual workers in achieving their organizational pretensions, they’re largely exploited. The recommendation of the study is that employment should be grounded on fair stipend, safety at work and protection for casual workers, in short, work should encompass fairness, equivalency, and freedom of association. Also, Payment of the benefit accumulated by the casual workers should be linked directly between casual workers and their associations.

The objective of this paper is to assess the influence of various types of crises, including the Subprime, COVID-19, and political crises, on corporate governance attributes, regulations, and the association with bank risk. The consecutive occurrences of crises have significantly impacted the global economy, causing substantial disruptions across various facets of the international banking system. Our hypothesis posits that these crises not only influence governance characteristics and regulations but also impact their correlation with the risk and financial distress experienced by banks. Our study is conducted within the Tunisian context spanning from 2000 to 2021, utilizing a GMM regression on a dataset comprising 221 bank-year observations. Our findings indicate that crises have a discernible effect on the relationship between corporate governance and bank risk, as well as between regulation and bank risk. Our results are strong in a range of sensitivity checks, including the use of alternative proxies to measure the bank risks and corporate governance metrics.

Credit policies for clean and renewable energy businesses play a crucial role in supporting carbon neutrality efforts to combat climate change. Clustering the credit capacity of these companies to prioritize lending is essential given the limited capital available. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are two robust machine learning algorithms for addressing complex clustering problems. Additionally, hyperparameter selection within these models is effectively enhanced through the support of a robust heuristic optimization algorithm, Particle Swarm Optimization (PSO). To leverage the strength of these advanced machine learning techniques, this paper aims to develop SVM and ANN models, optimized with the PSO, for the clustering problem of green credit capacity in the renewable energy industry. The results show low Mean Square Error (MSE) values for both models, indicating high clustering accuracy. The credit capabilities of wind energy, clean fuel, and biomass pellet companies are illustrated in quadrant charts, providing stakeholders with a clear view to adjust their credit strategies. This helps ensure the efficient operation of banking green credit policies.