This research paper explores the ethical considerations in using financial technology (fintech), focusing on big data, artificial intelligence (AI), and privacy. Using a systematic literature-review methodology, the study identifies ethical and privacy issues related to fintech, including bias, discrimination, privacy, transparency, justice, ownership, and control. The findings emphasize the importance of safeguarding customer data, complying with data protection laws, and promoting corporate digital responsibility. The study provides practical suggestions for companies, including the use of encryption techniques, transparency regarding data collection and usage, the provision of customer opt-out options, and the training of staff on data-protection policies. However, the study is limited by its exclusion of non-English-language studies and the need for additional resources to deepen the findings. To overcome these limitations, future research could expand existing knowledge and collect more comprehensive data to better understand the complex issues examined.

This paper examines the financial factors affecting profitability across the listed Jordanian service companies. Insufficient attention has been paid to the influence of profitability specifically revealed by financial indicators. This study adopts the firm effect model. The investigation is established on panel data from 2015 to 2020, based on annual company reports. Regression was used to test the study hypotheses. The research sample is collected from the 46 public service firms listed. The profitability of Jordan’s service firms is measured by three proxies, including, Return on Equity (ROE), Earnings Per Share (EPS) and Return on Assets (ROA). The results reveal that firm size and liquidity positively and significantly impact profitability. Conversely, the findings verify that company efficiency and market power have no significant impact on profitability. Moreover, among Jordan’s service firms’ financial indicators, the findings confirm that neither firm nor sales growth have a significant influence on profitability, while sustainable growth rate has a positive, significant effect on profitability. The firm effects are higher for ROE than ROA and EPS. The study provides beneficial insights for managers and investors by providing effective policies designed to improve profitability. The results also provide shareholders with statistics that will ensure the profitability of companies operating in developing countries, such as Jordan.

Integrated reporting (IR) has become a groundbreaking model that connects both financial and non-financial reporting in order to promote corporate transparency and value generation. Nonetheless, variations in implementation, governance frameworks, and digital integration constrain its effectiveness. This paper seeks to review the current trends in IR in the framework of the convergence of environmental, social, and governance (ESG), digital transformation, and Industry 5.0. Major academic databases were used to analyse 48 studies on the PRISMA method that were published between 2024 and 2025. It is found that technologies like artificial intelligence (AI), natural language processing (NLP), and blockchain can considerably enhance reporting accuracy and timeliness, but add ethical, regulatory, and standardisation issues. Quality of governance, diversity of boards, and ethical responsibility turn out to be key forces behind IR credibility. The paper concludes that IR is shifting from a symbolic tool of compliance to a strategic governance tool that is in line with stakeholder-centric and sustainability-oriented models. The study is an addition in that it incorporates digital ethics and governance innovation into the IR debate and provides policy-makers and organisations with harmonized and transparent reporting systems.

This review is a critical analysis of 29 peer-reviewed articles that investigate the application of Artificial Intelligence (AI) and Natural Language Processing (NLP) innovations to transform financial decision-making using social media data. A systematic review and subsequent study selection was in connection with PRISMA scheme using relevancy to sentiment analysis, predictive modeling, fraud detection, and sustainability in financial ecosystems to select studies between 2021 and 2025. Using unstructured social media data, AI and NLP can greatly improve sentiment analysis, risk forecasting, and personalized financial services. Nevertheless, there are still fears of algorithmic secrecy, data bias, ethical responsibility, and privacy theft. The literature demands explainable AI (XAI), models that are fair, and solid governance structures that can guarantee transparency and trust. The review is a synthesis of developing evidence regarding the potential of AI-NLP to incorporate social media signals into finance to provide insights into sustainable, transparent, and ethical financial innovation.

This study presents a bibliometric analysis of the evolving research landscape at the intersection of digital technology, corporate reporting, and disclosure practices. Analysing data from Web of Science, Scopus, and Dimensions (2015–2024), this study maps the intellectual structure of the field through citation, co-citation, and co-authorship networks, as well as keyword co-occurrence. Our findings reveal a significant academic shift toward digital themes, yet a persistent dominance of traditional domains such as financial reporting and governance. Notably, critical areas such as digital transformation, data analytics, risk management, and stakeholder engagement remain markedly underrepresented. This disconnect suggests a misalignment between scholarly focus and the practical challenges corporations face. The key implication of this work is that a significant realignment of research priorities is urgently needed to address critical practical challenges and ensure the relevance of academic output in the digital age. This study provides a comprehensive map of the field’s evolution and issues an urgent call for research to bridge the identified gap. The objective of this future work must be to transform corporate disclosures, making them resilient, technologically advanced (e.g., via interactive dashboards and blockchain audit trails), and directly responsive to a diverse array of stakeholders.

Artificial Intelligence (AI), Blockchain, and Cloud Computing (CC) are key technologies to improve financial automation and risk management of developing countries in particular. The purpose of this research is to explore the combined impact of AI, Blockchain, and CC on financial automation and how it, in turn, affects improved risk management in financial institutions in Oman. This study adopted a quantitative method by conducting questionnaires with 201 financial and IT experts in banks and fintech firms in Oman using convenience and snowball sampling methods. The respondents were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) based on Technology-Organization-Environment (TOE) and Innovation Diffusion Theory (IDT). Results indicate that Blockchain (β = 0.561) and AI (β = 0.264) have a strong impact on financial automation, with a moderate positive influence observed for CC (β = 0.173). Financial automation has a significant positive effect on improved risk management (β = 0.827, R² = 0.81) and regulatory compliance, market responsiveness, and technology access. This research is novel in its integrated empirical model, which brings together three frontier technologies in one model and empirically validates their impact on financial performance and risk management in a developing country, offering practical insights for policymakers and financial practitioners.