The problem of the current study is to study the moderating role of Blockchain technology on the impact of the use of financial technology (FinTech) on the competitive advantage of Jordanian banks. Quantitative analysis is appropriate. The study population consists of (600) employees in three banks at Jordan (Arab Bank, Islamic Bank, Ahli Bank) with its branches in various governorates. A questionnaire was developed to collect study data and distributed electronically. The number of participants was (240) respondents. The study confirms that there is an impact of the mediating role of Blockchain technology in the impact of the use of financial technology (FinTech) on competitive advantage. The study recommends increasing spending on financial technology applications to improve banking services provided to customers, especially through electronic applications and technologies. The study also recommends rebuilding current banking systems using Blockchain technology, which will remove the central database structure and replace it with a decentralized data environment via the blockchain, thus reducing the risk of database hacking. Since transactions via blockchain technology are verified by every node of the chain, it will make transactions more secure which will make the world’s banking systems faster and more secure.

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.

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 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.

Contract workers are the direct victims of casualization but beyond that, the effects they suffer transcend to their families and the larger society. The study examined the effects of casualization on the contract workers of banks in Sokoto, Nigeria. The primary methods of gathering data for the study were in-depth and key informant interviews, with sixty individuals who were specifically chosen. Following content analysis, the gathered data were presented narratively with verbatim quotations. According to the study, there are a number of negative effects of casualization, such as low wages that contribute to a low standard of living and the inability of employees and their families to adequately meet their basic needs, the arbitrary termination of casual employees without cause, and the lack of a claim for work-related injuries or diseases in the event of an accident or death. The overall inference is that the temporary employees are working in appallingly subpar conditions. The study suggests that in order to raise the living standards of their temporary employees, banks should provide welfare packages. Additionally, because inflation is on the rise, contract employees’ compensation should be reviewed upward.

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.