This study examines the perceived impact of artificial intelligence (AI) applications on accounting practices, the ability of Jordanian industrial companies to anticipate future financial needs, and the effective utilization of organizational resources. The study adopts a descriptive-analytical design and uses a structured questionnaire administered to accountants, financial managers, heads of accounting departments, and internal auditors in industrial companies listed on the Amman Stock Exchange. A total of 150 questionnaires were distributed; 12 were excluded because of incomplete or inaccurate responses, leaving 138 valid questionnaires for analysis. The instrument was assessed for internal consistency using Cronbach’s alpha, and the overall coefficient was 0.875, indicating strong reliability. Descriptive statistics and one-sample t-tests were then used to assess respondents’ evaluations against a neutral benchmark of 3 on a five-point Likert scale. The results indicate strong positive assessments of AI applications across all three study dimensions. The overall mean for accounting-related applications was 4.46, the overall mean for improving future financial forecasting was 3.96, and the overall mean for effective resource utilization was 4.01. The corresponding reported t-statistics were statistically significant at the 5% level. The findings suggest that AI-enabled accounting technologies are perceived as useful for improving the timeliness and quality of accounting information, strengthening financial forecasting, supporting risk identification, and improving the utilization of physical, financial, and human resources. The study contributes empirical evidence from the Jordanian industrial context and highlights the need for organizational readiness, employee training, internal controls, and continuous technological adaptation.

This study tries to assess whether aggregated domestic and global macro-financial conditions can assist to explicate daily equity-market returns and volatility in India and considers the implications for financial risk and project-related decision making. Daily BSE and NSE index returns are analysed for January 2000–March 2024 using principal component analysis (PCA), EGARCH and FIEGARCH specifications, with event-period indicators for the Global Financial Crisis, demonetization, COVID-19 and the Russia–Ukraine war. The PCA results point out that the retained components summarize a large share of the common variation in the underlying macro-financial variables. In the conditional-mean equations, the domestic and global PCA factors are usually statistically insignificant, whereas market uncertainty measured by India VIX is significant in selected specifications. Event-period indicators divulge strong conditional-mean effects for the Global Financial Crisis and COVID-19, while demonetization is associated with a negative return effect. In the variance equations, the models identify substantial conditional heteroskedasticity and asymmetric volatility. The Student-t EGARCH specifications provide lower in-sample AIC values than the normal EGARCH and FIEGARCH alternatives. FIEGARCH estimates indicate statistically significant fractional integration, with a larger estimated long-memory parameter for BSE than NSE; however, the available model-comparison evidence does not establish that fractional persistence is superior to asymmetric short-memory modelling. The findings suggest that financial project managers, treasury functions and investment decision-makers should treat market uncertainty and crisis regimes as central risk-management inputs, while avoiding the assumption that low-frequency macroeconomic indicators have an immediate daily effect on equity risk.

Building Information Modeling (BIM)-based cost estimation has moved beyond automated quantity take-off toward workflows that combine structured cost data, open data exchange, semantic models, machine learning, provenance, and decision support. This study maps that transition and identifies the reliability gap that separates automated calculation from defensible cost decisions. Three targeted Scopus RIS sets yielded 497 records, 393 unique records, and 147 studies for bibliometric mapping; a broader four-set synthesis identified 1,436 records, 1,299 unique records, and 167 studies for critical review. A claim-level audit of 100 published and verified sources was used to construct the conceptual framework. Publication output rose from 24 papers in 2022 to 41 in 2024, with 27 papers already recorded by 1 August 2026. Keyword co-occurrence revealed four dominant structures around BIM-quantity take-off, integration-machine learning, semantics-3D modelling, and cost estimation-5D BIM, while quality control remained peripheral. The synthesis shows that accuracy alone is insufficient because reliability also depends on completeness, consistency, reproducibility, agreement, traceability, semantic interoperability, and version integrity. The resulting Audit-Ready BIM Cost Estimation (ARBICE) architecture integrates seven layers from evidence grounding to decision assurance. The framework repositions BIM cost estimation as a traceable data-to-decision system and provides testable directions for cross-platform validation, explainable discrepancy diagnosis, and audit prioritization.

From a discriminated approach, this study examined cryptocurrencies and fraud with a focus on audit trail and regulatory slack. The is anchored on the rapid growth of cryptocurrencies in the global financial systems with their unique attributes of being decentralized, fast, and borderless transaction platforms. While these innovations have improved financial inclusion and efficiency, they have concomitantly enacted new opportunities for fraud and regulatory challenges. Thus, from a qualitative approach, a content analysis of the open-source current literature was deployed to glean evidence-driven insight. It was observed that while cryptocurrencies serve as alternative investment growth and wealth storage means, they are highly vulnerable to fraud, due to anonymity, lack of centralized control, and rapid technological growth. Therefore, the structural characteristics of cryptocurrencies precinct, significantly weakening traditional control mechanisms and creating opportunities for fraudulent exploitation. Also, blockchain technology which anchors the crypto world creates digital bread crumbs that often fissile out depending on the technological prowess of the perpetrator, enacting difficulties in the audit trail. These have serious implications along theoretical, policy and practical lines. Therefore, the study recommends achieving an optimal effective mitigation of cryptocurrencies downsides within the espoused thematic focus of the study requires a holistic and integrated approach that combines strong regulatory frameworks, advanced technological tools, institutional capacity building, and increased public awareness, as no single mechanism is sufficient to address the complexity and dynamic nature of fraud in digital financial systems.

Stock markets and stocks have been around for centuries. For investors, forecasting changes in the price of stocks has long been an aim. Using computer tools for information systems can lead to better and more accurate results. Machine learning uses machines to mimic human thinking and habits. This helps in making predictions and decisions based on data. Today, machine learning is common in areas like face recognition, investment advice, and natural language processing. This study proposes a robust framework for stock trading signal classification within the Indian equity market using a Multivariate Random Forest (MRF) approach. Separate Random Forest classifiers are designed to capture the unique market traits of each company. Bootstrap sampling and random feature selection help the ensemble model to find intricate relationships among various technical variables. This method reduces the variance that comes with individual decision trees. By leveraging the ensemble learning capabilities of Random Forest, the model effectively mitigates the risks of overfitting while managing the high-dimensional feature space typical of financial time series. The Multivariate Random Forest-based stock market model offers a more reliable and practical way to classify stock market signals. The model aids in making smarter, data-driven choices for portfolio management and key market drivers.