Current Issue : July-September Volume : 2025 Issue Number : 3 Articles : 5 Articles
This research aims to investigate the impact of the remote board meeting mechanism on the overall profitability of banks in the Middle East and North Africa (MENA). Hence, this research adopted resource dependency theory as the proposed theoretical framework to investigate the impact of remote board meeting mechanisms on the profitability of banks in the MENA. By exploring the interplay between the banks’ resource dependencies and the adoption of remote board meetings, a generalized linear model has been conducted to test the influence of remote board meetings on the banks’ profitability. In addition, it examines the mediating effect of remote board meetings on the association between busy board members and banks’ profitability. Therefore, the main results of this research confirm that remote board meetings significantly boosted the banks’ overall profitability. Furthermore, remote board meetings allow busy board members to join the meetings and share their great experiences, significantly promoting banks’ profitability....
This study examines the transmission of capital-based macroprudential policy in the Romanian economy using a Dynamic Stochastic General Equilibrium (DSGE) model with a monopolistic banking sector. The need for such research arises from the limited focus on smaller non-euro area economies, which face unique macroeconomic challenges compared to larger economies like the eurozone. The study addresses this gap by adapting a widely recognized DSGE model to reflect the specificities of the Romanian economy, calibrating it with data from Romania’s banking system. Methodologically, the model simulates the effects of macroprudential policies, particularly capital buffer adjustments, on the economy through various shocks. Findings indicate that increasing capital requirements effectively curbs overheating in the housing market and reduces lending. The shock responses tend to have larger amplitudes but shorter durations compared to those observed in eurozone economies. The results suggest that while macroprudential policy is useful for managing economic overheating, its impact is shorter-lived than traditional monetary policy interventions....
In the era of big data and artificial intelligence, machine learning is one of the hot issues in the field of credit rating. On the basis of combing the literature on credit rating methods at home and abroad, this paper uses the support vector machine (SVM) method of machine learning to set up a three-classification credit rating model for Chinese listed commercial banks, which is compared with the existing methods of credit rating, the data show that the model classification accuracy on the test set is 80%. The support vector machine (SVM) method can identify the credit rating of Chinese-listed commercial banks, and it has wide applicability and good promotion value. The paper enriches the traditional credit rating method and has important significance in standardizing the healthy development of the financial market....
This study aimed to identify the impact of audit committee characteristics on voluntary risk disclosure and to discover the moderating effect of family ownership on the relationship between audit committee characteristics and voluntary risk disclosure. Its population is represented by Jordanian commercial banks registered and operating in Jordan from 2017 to 2023. Significantly, it concluded by revealing that the characteristics of the audit committee, namely, independence, experience, and committee size, obviously impact the disclosure of voluntary risk in the selected banks. However, the results made it obvious that the number of audit committee meetings did not affect the degree of voluntary risk disclosure. In addition, the results reveal that family ownership moderately affects the relationship between some audit committee characteristics and voluntary risk disclosure....
We developed a method for the evaluation and selection of customer business analysis in two stages. First, using the bank’s existing expert model, artificial rules of thumb were used to evaluate the value of each field of the data and establish screening rules. Secondly, the machine learning feature screening method was applied based on the customer’s transaction data to find out whether the customer’s contribution to the bank had a significant impact as a feature of the model. Based on the results, the best classification model was selected through data verification. The effectiveness of the proposed model was validated through actual case analysis, taking wealth management in banks as an example. The classification method, using support vector machines (SVMs), effectively assists banks in identifying potential customers efficiently and in planning to manage customers. This method helps to avoid the traditional blind spots, which emerge based on subjective judgment, and allows bank wealth managers to promote customer relationship management (CRM)....
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