This study aims at finding out the determinants of Indian commercial banks profitability. Profitability of Indian banks is measured by three important variables namely, Return on Assets (ROA), Return on Equity (ROE) and Net Interest Margin (NIM). The study also uses a set of independent variables such as bank-specific factors which include bank size, assets quality, capital adequacy, liquidity, operating efficiency, deposits, leverage, assets management and the number of branches. Pooled, fixed and random effects models and Generalized Method of Moments (GMM) are built on panel data of 10 years for more than 60 commercial banks of India. The study also takes into account Gross Domestic Product (GDP), inflation rate, interest rate and exchange rate as macroeconomic determinants. The results of the study show that all bank-specific factors, except the number of branches, exhibited significant impacts on profitability as measured by NIM. The findings also show that all macroeconomic determinants used in the study are found to be significant with negative impacts on Indian commercial banks profitability. Furthermore, the results show that bank size, number of branches, assets management ratio and leverage ratio are highly significant variables of profitability in the context of Indian commercial banks as measured by ROA. The results give a better insight into the Indian banking sector and the determinants of its profitability

IT governance is a framework that manages the efficient use of information technology within an organization, focusing on strategic alignment, risk management, resource management, performance measurement, compliance, and value delivery. This study investigates the role of IT governance in integrating artificial intelligence (AI) in accounting and auditing operations. Data were collected from 228 participants from Saudi Arabia using a combination of convenience sampling and snowball sampling methods. The collected data were then analyzed using structural equation modeling. Unexpectedly, the results demonstrate that AI, big data analytics, cloud computing, and deep learning technologies significantly enhance accounting and auditing functions’ efficiency and decision-making capabilities, leading to improved financial reporting and audit processes. The results highlight that IT governance plays a crucial role in managing the complexities of AI integration, aligning business strategies with AI-enabled technologies, and facilitating these advancements. This research fills a gap in previous research and adds significantly to the academic literature by improving the understanding of integrating AI into accounting and auditing processes. It builds on existing theoretical frameworks by investigating the role of IT governance in promoting AI adoption. The findings provide valuable insights for accounting and auditing experts, IT specialists, and organizational leaders. The study provides practical insights on deploying AI-driven technology in organizations to enhance auditing procedures and financial reporting. In a societal context, it highlights the broader implications of AI on transparency, accountability, and trust in financial reporting. Finally, the study offers practitioners, policymakers, and scholars valuable insights on leveraging AI advancements to optimize accounting and auditing operations. It highlights IT governance as an essential tool for effectively integrating AI technologies in accounting and auditing operations. However, successful implementation encounters significant organizational challenges like organizational support, training, data sovereignty, and regulatory compliance.

This study aims to examine the impact of internal and external determinants of 37 commercial banks' profitability listed on Bombay stock exchange (BSE), India for a period from 2008 to 2017. Both static models (pooled, fixed and random effects) and generalised method of moments (GMM) are used. The results show that bank size, assets quality, liquidity, assets management, and net interest margin are important internal determinants which affect ROA. Capital adequacy, deposits, operation efficiency, gross domestic product and inflation rate are found to have a negative significant impact on ROA. Further, the results indicate that capital adequacy, bank size, operation efficiency, gross domestic product and inflation rate have a significant negative influence on ROE. However, assets quality and assets management exhibit a positive effect on ROE but liquidity, deposits, net interest margin, and non-interest income have an insignificant impact on ROE.

The objective of this study is to examine the liquidity (LQD) determinants of Indian listed commercial banks. The study has applied both GMM and pooled, fixed and random effect models to a panel of 37 commercial banks listed on the Bombay Stock Exchange (BSE) in India for the period from 2008 to 2017. The banks’ LQD was taken as a dependent variable which functioned against both bank-specific and macroeconomic determinants. The results indicated that among the bank-specific factors, bank size, capital adequacy ratio, deposits ratio, operation efficiency ratio, and return on assets ratio are found to have a significant positive impact on LQD, while assets quality ratio, assets management ratio, return on equity ratio, and net interest margin ratio are found to have a significant negative impact on LQD. With respect to macroeconomic factors, the results indicated that interest rate and exchange rate are found to have a significant effect on LQD. The Reserve Bank of India (RBI) should give benchmarks for the above mentioned ratios to achieve smooth LQD of commercial banks in India. The study recommended that bankers should consider assets quality in such a way that improves banks’ performance. Finally, the current study provides useful insights for bankers, analysts, regulators, investors, and other interested parties on the LQD of listed commercial banks.