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.

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 study sheds light on how service quality aspects affect customer satisfaction in the Saudi banking sector’s particular socio-cultural setting. Thus, the study examines the role of service quality dimensions on customer satisfaction in the banking industry of Saudi Arabia. The study examined how reliability, assurance, empathy, tangibility, and responsiveness affect customer satisfaction in the Saudi Arabian banking market using 250 bank clients. 250 Saudi bank customers completed a standardised questionnaire. These were normal bank customers with proper bank accounts. IBM SPSS correlational and multiple regression analysis investigated variable connections. The study found a significant favourable influence of reliability on customer satisfaction. However, assurance was not significant. Empathy had a significant impact on customer satisfaction. Tangibility shown a significant impact on customer satisfaction. Responsiveness was not significant. The study emphasises on reliability, empathy, and physical service delivery to boost banking customer happiness. The study found 3 of 5 service quality factors to be significant predictors. Service empathy, tangibility, and reliability greatly impacted customer satisfaction. Managers in Saudi banking should prioritize reliability, empathy, and tangibility to boost customer satisfaction. To keep customers happy, managers should monitor these service quality dimensions and adjust strategies based on feedback. Technology can improve service quality by streamlining processes and personalizing experiences.

This study explores the pivotal factors influencing the adoption of International Financial Reporting Standards (IFRS) in the banking sector of Vietnam, focusing on the perceptions of its benefits, the competence of accountants, the involvement of managers, and the guidance from the accounting and auditing community. Employing Exploratory Factor Analysis (EFA) on data collected from 236 professionals across accounting, auditing, banking, and finance, the research reveals that the perceived benefits of IFRS, active managerial participation, and advice from the accounting-auditing community significantly encourage the adoption of IFRS within Vietnamese commercial banks. Interestingly, the competence of accountants was not identified as a significant determinant. These findings suggest a nuanced landscape of IFRS adoption, emphasizing the importance of managerial support and community guidance over individual accountant competence. The study contributes to the broader discourse on IFRS adoption, offering actionable insights for banks, policymakers, and potentially applicable strategies for firms in Vietnam or similarly positioned economies on the path to IFRS compliance.

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.