Accounting digital transformation in today’s fast-digitizing banking environment is becoming an imperative force driving transparency and governance in the banking industry. This study explores the effect of accounting digital transformation on transparency in finance, with emphasis on the mediating effect of good governance in Jordan’s commercial banking. Based on survey data from 386 bank experts, the study utilizes Structural Equation Modelling (SEM) to analyze the relationships among organizational, technical, and human dimensions of digital transformation, governance, as well as financial transparency. The findings show that all three dimensions have positive impacts on transparency in finance, and good governance partially mediates these relationships. These insights provide policy makers and practising managers with actionable advice to enhance financial reporting through governance and digitization.

As a new economic factor, digitalization plays a vital role in society, economy, and the environment. Based on the expanded STIRPAT model, this paper empirically tests the impact of digital economy on carbon emissions by panel data from 2011 to 2021 in BRICS countries. Utilizing a variety of econometric techniques, the study's objectives were met. For instance, unit root properties are investigated using the CIPS and CADF techniques, while cointegration is examined using the panel cointegration approach of Westerlund and Edgerton (2008). The findings demonstrate the existence of a long-term correlation between carbon emissions, population, GDP, technical level, and digital economy. We propose that governments must not only implement hedging policies to reduce Carbon emissions caused by the digital economy at an early stage but also promote the development of the digital economy in order to accomplish the objective of global collaborative environmental protection.

This study aims to examine the impact of artificial intelligence through robotic process automation systems on internal audit operation efficiency in Jordanian commercial banks. The study uses a descriptive methodology to evaluate how robotic process automation systems enhance the different dimensions of internal audit efficiency: planning, management, execution, and communication. The study designed a structured questionnaire for data collection. The sample consisted of 12 commercial bank employees whose working processes are directly affected by robotic process automation system procedures which puts them in a unique position to comment on the practical applicability of this technology and its implications for internal audit. In this study, 480 electronic questionnaires were distributed via Google Forms, and 390 completed forms were collected for further analysis. The study employs descriptive statistics and advanced statistical techniques, such as linear regression analysis, for data analysis. The findings indicate that robotic process automation systems enhance the internal audit process by reducing the cost of operations, eliminating human errors, and smoothing work processes. The robotic process automation system will allow continuous auditing, real-time risk management, and proper reporting; hence, it will change the role of internal auditors and, in the end, improve organizational compliance and performance. This study asserts that the banking industry must integrate AI-driven automation to maintain its competitiveness in the constantly changing financial landscape.

The continued digitalisation of banking systems has raised a demand for more reliable data-based decision-making, in particular when referring to financial forecasts as covered by e-banking applications. This research also investigates the usage of AI-based decision-making systems to facilitate forecasting effectiveness in Jordanian commercial banks. Field research was carried out and 390 employees, working at 14 commercial banks in Jordan, responded to an organised questionnaire. Although the minimum required sample size was 384 respondents, a total of 390 valid responses were collected and used in the final analysis, thereby exceeding the minimum sample requirement. This research concentrates on three dominant categories of AI applications, including expert systems (ES), machine learning (ML), and Robotic Process Automation (RPA), which together are analysed for their effect on forecasting results in the context of customer churn, debt repayment, as well as investment analysis. The results of the multiple regression analysis indicate that AI applications contribute to improvements in forecasting accuracy, with machine learning and RPA showing relatively stronger effects. Expert systems were found to support investment analysis and debt repayment forecasting; however, their influence on customer churn prediction was more limited. In general, the findings indicate that AI applications are not confined to routine automation but are increasingly used as decision-support tools that assist financial analysis and forecasting activities in banking systems.

This study aimed to find out the influence of Accounting Information Systems on the performance of Jordanian commercial banks, and the degree to which internal audit moderates this relationship. This was a current quantitative study designed to be at a descending trend of the survey conducted primarily on 12 major Jordanian banks’ accounting departments using descriptive and cross-sectional designs. From a total distribution of 358 questionnaires, 249 were returned, and after screening only 243 were deemed valid. Partial Least Square Structural Equation Modeling (PLS-SEM) was used as a statistical tool for data analysis, which is appropriate when exploring convoluted relationships and testing hypotheses. The findings indicate that there is a clear positive relationship between the implementation of AIS systems and improvement in the performance metrics at Jordanian banks; thus, hypothesis 1 is corroborated at a significant level of p < 0.01. More significantly, the findings suggest that internal audit highly strengthens the positive effect of AIS on bank performance, thereby supporting hypothesis 2 at the same significance level. This dual realization reiterates the needed adoption of advanced AIS by Jordanian banks and a general improvement to the internal auditors of the mechanisms that ensure they maximize on the benefits accruing in performance. More fundamentally, the findings underscore the dire urgency to incorporate efficacious AIS alongside with robust internal audit mechanisms in banking strategies, thereby providing crucial implications for bank managers and sectoral policymakers.