Mobile banking has been experiencing unsustainable growth in the last ten years, as the volume of digital payments worldwide is currently more than USD 8.49 trillion and is estimated to be more than USD 20 trillion by 2026. At the same time, fraudulent attacks have been directed toward mobile banking platforms to a significant extent, causing losses amounting to USD 485.6 billion worldwide alone in 2023. The current fraud detection architectures have been heavily based on a centralized cloud-based model which is inherently associated with a latency delay of between 200 to 800 milliseconds that introduces a time delay that advanced attackers can use to transact fraudulent transactions before the defensive countermeasures are activated. The paper will suggest a new Edge-to-Cloud Collaborative Fraud Detection (EC-CFD) model that allocates inference workloads to three hierarchical levels. The structure is a combination of differential privacy, mutual authentication, and federated learning to allow joints in improving the model without having to centralize sensitive financial information. The feature engineering has 18 dimensions which include transaction statistics, geo-location anomalies, device fingerprinting and behavioral biometrics. Synthetic dataset of 2.4 million transactions, experimented on with samples of PaySim and IEEE-CIS benchmarks on fraud detection methods to provide realistic behavioural diversification. The findings indicate that the suggested EC-CFD model is characterized by a 98.7 % detection rate of fraud, where the F1-score is 0.974 and the AUC-ROC value is 0.992. It is important to note that the hierarchical structure allows accomplishing 87.2% raw transactions at the edge level and achieves a mean detection latency of 8.3 ms. The proposed framework lowers false positives by 31.4 per cent, the average end-to-end latency by 73, and could be extended to support 50,000 active users without lowered performance, which proves the effectiveness of hierarchical edge intelligence as the primary paradigm of next-generation financial cybersecurity.

This study investigates the impact of social media overuse, fear of missing out, and notification overload on digital burnout among university students in the United Arab Emirates, with a particular focus on the mediating role of cognitive overload. It examines how excessive social media engagement, psychological pressure to remain constantly connected, and frequent digital interruptions contribute to increased cognitive demands and subsequent digital burnout. Data were collected from 392 university students using a structured questionnaire and analyzed using Structural Equation Modeling (SEM) via SmartPLS. The findings reveal that social media overuse, fear of missing out, and notification overload all have significant positive effects on cognitive overload and digital Burnout. In addition, cognitive overload shows a strong positive effect on digital burnout. Moreover, Cognitive Overload was found to significantly mediate the relationships between social media overuse, fear of missing out, and Notification Overload with digital burnout, confirming its critical role in translating digital stressors into burnout outcomes. All hypotheses were statistically supported. This study contributes to the digital wellbeing and cognitive overload literature by clarifying how modern digital behaviors lead to psychological exhaustion through cognitive strain mechanisms. It also provides valuable insights for educators and policymakers seeking to reduce digital burnout by promoting healthier social media usage patterns and effectively managing cognitive demands within digital learning environments.

Digital technologies and their rapid evolution have greatly impacted the financial sector, thus making digital transformation increasingly more important strategically paired with analytical capacities to foster FinTech based financial innovation. This study explores the associations between digital transformation capability, data analytics capability and FinTech adoption in financial institutions. In particular, the study investigates the impacts of digital transformation capability on data analytics capability, data analytics capability on FinTech adoption and examines the mediating role or effect of Data Analytics Capability in the influence between Digital Transformation Capability in relation to FinTech Adoption. It was a quantitative study with a cross-section survey design. A structured questionnaire was completed by employees and managers working in financial institutions and FinTech-related sectors. The study used Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze the proposed research model through Smart-PLS-4 software. The results show that digital transformation capability significantly and positively affects data analytics capability, and in turn, data analytics capability is a significant positive driver of FinTech Adoption. Additionally, the findings indicated that data analytics capability mediated the association between digital transformation capability and FinTech adoption. This study expands the literature on digital transformation and financial innovation by combining organizational digital capabilities with different perspectives of FinTech adoption in a single conceptual framework. The results also have some valuable implications for practitioners to facilitate greater digital competitiveness, harness better analytical approaches and make FinTech implementation strategies more effective.

The cosmetics industry is proliferating, triggered by the recognition of the significance of caring for appearance. This study uses the Theory of Planned Behavior (TPB) approach to examine how consumers of cosmetics in Bali view their behavioral control over the virtual try-on function, and how ZMOT (zero moment of truth) from omnichannel influences their purchasing decisions. Participating individuals are Generation Z women in Bali who have purchased cosmetics online and have tried and know the virtual try-on feature. A quantitative method was employed to gather data from approximately 150 participants using a purposive sampling technique. The analytical method employed is the Partial Least Squares - Structural Equation Modeling (PLS-SEM) using the Smart PLS software. The analysis's findings indicated that ZMOT significantly and favorably influences Bali consumers' decisions to buy cosmetics because of perceived behavioral control. This result suggests that the association between ZMOT and purchase decisions is mediated by the perceived behavioral control relationship. The research implies that cosmetics industry stakeholders need to formulate marketing strategies to minimize barriers that affect consumers' perceived behavioral control in choosing products.

Preeclampsia is the most common medical disorder in pregnancy that is steadily increasing over time. Evidence indicates that early identification of women at high risk, followed by timely preventive interventions such as Aspirin and close surveillance, can significantly reduce disease incidence and severity, highlighting the importance of accurate early prediction. At the molecular level, preeclampsia is primarily a placental disorder associated with dysregulated microRNA (miRNA) expression. DICER, a key enzyme in miRNA biogenesis, and its single nucleotide polymorphisms (SNPs) have been shown to influence placental miRNA profiles and disease susceptibility. In this study, artificial intelligence-driven data mining and machine learning classifiers integrating genetic (DICER SNPs) and clinical features were developed and evaluated in a sample of Jordanian population. Six different classifiers were applied to predict the occurrence of preeclampsia, stratify disease severity, and distinguish early- from late-onset disease. This study included 224 pregnant women, with 137 controls and 87 women who had developed preeclampsia. Genotypes of rs3742330 and rs14035 SNPs in DICER and RAN genes respectively, were determined using polymerase chain reaction techniques, and clinical data was recruited. Six machine learning classifiers were used to build the prediction models. Model performance was estimated using repeated stratified k-fold cross-validation to provide robust, unbiased estimates given the limited sample size. The results showed that ensemble methods Random Forest and XGBoost outperformed other models with the highest accuracy, sensitivity, precision, F1-scores, and G-means. For occurrence prediction, Random Forest achieved a cross-validated accuracy of 0.973 ± 0.040, sensitivity of 0.953 ± 0.058 and F1-score of 0.965 ± 0.050, while XGBoost attained an accuracy of 0.978 ± 0.022, indicating that combining genetic with clinical factors provides strong discriminative ability compared to models relying solely on traditional risk factors. Thus, integrating genetic information, particularly the DICER rs3742330 polymorphism, with routine early pregnancy clinical features using data mining and machine learning techniques yields strong predictive performance for preeclampsia in the Jordanian population. These findings suggest that such AI-driven models could be translated into practical clinical applications to support early risk stratification.

The rapid adoption of cloud computing has revolutionized the accounting and auditing sectors by offering unprecedented scalability and operational efficiency. In the context of Saudi Arabia's Vision 2030, the shift to cloud is a cornerstone of national digital transformation. Yet, this transition exposes sensitive financial data to sophisticated threats such as ransomware and phishing. This led to critical vulnerabilities in data integrity and regulatory compliance. This study examines the impact of cybersecurity governance on cloud accounting risk (CAR) reduction in Saudi-listed companies by evaluating five independent variables: awareness and training (CRAT), audit management (CRAM), risk management (CRM), powers and responsibilities (CPR), and cybersecurity strategies (CS). The research provides a critical pathway for developing secure infrastructures by aligning internal controls with global NIST and ISO standards to ensure financial credibility. Using Partial Least Squares Structural Equation Modelling (PLS-SEM), the study validated five core hypotheses. The finding of a statistically significant positive relationship between governance implementation and risk reduction (p=0.000). The strongest influence was observed in Cybersecurity Powers and Responsibilities (β=0.795), with the model demonstrating moderate to strong predictive power across all dimensions. The study concludes that effective governance is inseparable from successful risk reduction and recommends that Saudi organizations formally clarify cybersecurity roles, enhance internal audits as mediating factors, and prioritize employee training. Ultimately, the continuous evolution of these governance structures is essential for maintaining stakeholder trust and ensuring the long-term integrity of the Saudi financial sector.

Banking is now going through a rapid digital transformation which is generating a highly data driven marketing landscape, owing to the presence of Artificial Intelligence (AI). Although banks make significant investments in AI-powered Business Intelligence (BI) systems, many of them face challenges with turning data resources into actionable marketing intelligence. The current study aims to explore the drivers of organizational and technological development in terms of embedding the capability of using artificial intelligence in BI among the Jordanian commercial banks. The study focuses on four hypothesized drivers, which are grounded in the Resource Based View (RBV) and Dynamic Capabilities Theory (DCT): Analytics-Driven Culture, AI Readiness, Executive Support, and AI Analytics Maturity. A structured online questionnaire was used to collect the data from the commercial banks in Amman, Irbid, Zarqa and Aqaba which consisted of marketing managers, analytics professionals and IT decision makers. The results of the valid answers were analyzed by using the Structural Equation Modeling (SEM) technique with IBM SPSS Amos 24 software. Findings: The measurement model showed good reliability and validity. The structural model showed an acceptable fit (χ²/df = 2.41, CFI = 0.952, TLI = 0.946, GFI = 0.918, AGFI = 0.901, RMSEA = 0.048, SRMR = 0.041). All four drivers had significant and positive relationships with AI enabled BI capability. AI Analytics Maturity was the strongest predictor (β = 0.369, p < 0.001), followed by Analytics-Driven Culture (β = 0.323, p < 0.001), AI Readiness (β = 0.289, p < 0.001), and Executive Support (β = 0.226, p < 0.001). The four drivers accounted for 64.7% of the variance in AI-driven BI capability (R² = 0.647). Conclusions: The technological infrastructure, organizational culture, managerial engagement and analytics maturity must all work together to create AI-driven marketing intelligence as a strategic tool for the organization. The findings contribute to the existing body of knowledge on RBV/DCT in a context that has not been explored in the banking sector literature, and offer practical advice to bank managers who are driving digital transformation efforts.

The integration of ChatGPT in higher education offers substantial potential to enhance student learning experiences and academic outcomes by supporting both individual and group activities. This study investigates the impact of ChatGPT on university student workgroups, focusing on its effects on productivity, collaboration, and the quality of group writing. A quantitative survey was conducted with 561 students in the United Arab Emirates to assess how ChatGPT facilitates communication, streamlines task management, and improves the overall quality of academic outputs. The findings indicate that ChatGPT significantly enhances group performance by enabling more efficient collaboration, refining writing quality, and supporting coordinated workflow, ultimately contributing to higher academic achievement. These results underscore the value of AI tools in promoting effective teamwork and academic success in contemporary educational settings.

The quick transfer of academic processes to cloud-based systems has increased the cyber-attack area of academic institutions significantly by introducing systemic weaknesses into the learning management systems (LMS), enterprise resource planning (ERP) tools, as well as databases housing student data. Traditional security controls, relying mostly on fixed signature databases and rule-based intrusion detection, are no longer effective against polymorphic malware, zero-day attacks, and advanced insider threats that define contemporary adversarial environments. Nevertheless, even with the emergence of cloud-native security tools, educational organisations are experiencing a high level of false positives, slower response to threats, and an almost complete inability to predict new vectors of attacks. Lacking contextually sensitive self-updating intelligence pipes exposes campus networks to cascading failures that threaten the privacy of students, integrity of research data, and compliance with regulations. This paper introduces Adaptive Threat Intelligence Learning Algorithm (ATILA), a five-layer security architecture, which combines transformer-based anomaly detection, reinforcement-learning-based policy coordination and federated collaborative defence in geographically distributed institutional nodes. Three benchmark datasets were used to evaluate ATILA: UNSW-NB15, CICIDS-2018 and a simulated corpus of campus cloud logs simulating a federated multi-campus environment. They were compared to rule-based, intrusion detection systems, and non-adaptive deep learning models and rule-based classifiers based on the static random forest. ATILA had 98.3% and 1.4% detection and false positive rates, respectively, which is 4.6 and 8.3 % points better than the most successful non-adaptive baseline. The recall of the zero-day detection went to 91.7 and the mean time of the threat response came down to 6.1 ms. University LMS environments, multi-campus cloud federations, and online examination systems all receive deployment ready design specifications that provide a repeatable pathway towards institutions wishing to operationalise adaptive AI-driven security at scale.

Deep learning-based credit scoring systems face three interrelated challenges typically addressed in isolation: unavoidable class imbalance, model opacity, and demographic inequalities. This paper proposes the Explainable Fairness-Aware Deep Learning (EFADL) framework, which is a unified end-to-end pipeline designed to mitigate these risks simultaneously. The EFADL framework integrates three novel components: FC-SMOTE, a fairness-constrained oversampling module that preserves intra-group demographic balance; a multi-objective joint training loss combining focal imbalance correction with differentiable multi-attribute fairness penalties; and a dual-level SHAP module providing both instance-level adverse action explanations and group-level fairness attribution. Extensive experiments on the German Credit, Taiwan Credit, and LendingClub datasets demonstrate that EFADL achieves a better accuracy-fairness trade-off surface. Results indicate a 79% reduction in statistical parity and equal opportunity differences and a 70.5% decrease in maximum intersectional disparity, with a negligible AUC-ROC cost of only 1.2 percentage points. Furthermore, the framework reduces the fairness attribution gap by 71%, which provides evidence that it achieves fairness by suppressing reliance on demographic proxies rather than post-hoc calibration. By delivering stable, economically interpretable explanations, the EFADL framework aligns with the transparency requirements of the EU AI Act and US CFPB guidance and offers a deployable solution for regulatorily-compliant algorithmic lending.