The aim of this study was to explore how artificial intelligence (AI) impacts the quality of financial reporting, providing insights into new opportunities in this field for the Saudi context. This study employed the UTAUT theory to examine the adoption of AI technology in auditing practices. This study also utilized bibliometric analysis techniques through an academic literature review and content analyses of the documentary evidence. The implication of this study is that non-Big 4 audit firms should adopt AI-powered drones, which consequently enhance decision making, decrease audit fees, and enhance the quality of financial reports, and the efficiency and accuracy of audits. Furthermore, this paper recommends that non-Big 4 audit firms adopting AI should foster a culture of change to ensure quality audits and consistency, overcome resistance to the change, and support the integration of technologies such as AI-driven audit automation. Our study also indicated the importance of integrating AI with the IFRS, developing a new framework for AI in auditing practices, incorporating AI into auditing courses, and modernizing auditing using AI. These implications lead to financial reports of enhanced quality. The results indicated four clusters, with artificial intelligence being the most significant keyword occurrence. This study has limitations, such as the lack of consideration of cyber-attack risks on drones, which may reduce the reliability of financial reports. Based on the findings of this research, audit companies and regulatory agencies in Saudi Arabia, like the Saudi Capital Market Authority (CMA), may evaluate the integration of AI to improve the quality of financial reporting. Implementing AI is expected to enhance the quality of audits, automate reporting, and support regulatory compliance to foster confidence and transparency in the financial industry.

The purpose of this study is to examine the influences of external auditor firm type, auditor tenure, and external auditor changes on the quality of Saudi Arabian financial reports. In particular, this study examines the quality of financial reports of companies listed on the Saudi Stock Exchange using a widely accepted evaluation model modified by JonesThis study aims to determine whether Big Four and non-Big Four audit firms, auditor tenures of three or more years, and external auditor changes have any impact on the quality of financial reports of Saudi-listed companies. This study uses 175 firm-year observations of 35 companies listed on the Tadawul Saudi Stock Exchange between 2017 and 2021. Using discretionary accruals (DACC) as modified by Jones to measure the quality of financial reports, the findings illustrate that there is a significant negative relationship between Big Four audit firms and DACC. However, the study also shows a significant positive correlation between auditor tenure and DACC. The research revealed that there is no significant relationship between auditor change and DACC. These results have practical implications for policy development. According to the outcomes of this research, there are numerous ramifications for both companies and the government in Saudi Arabia in terms of enhancing the relationship between companies and audit firms and determining the most suitable auditor tenure to improve the quality of financial reports.

This study investigates the influence of drone technology on the quality of Saudi financial reports through the integration of Artificial Intelligence (AI) and big data. The study’s mixed-method approach is based on a bibliometric analysis of previous studies, along with documentary and content analysis. The results show that external auditors benefit from using drones when inspections are integrated with AI and big data technology. Moreover, this integration can reduce costs for audit firms and shorten the duration of audit engagements, resulting in more efficient and effective auditing. Seven clusters were identified, with ‘big data’ being the highest-frequency term. This study does not consider potential cybersecurity threats that could impact data integrity and decrease financial transparency. Furthermore, environmental issues in Saudi Arabia, such as sandstorms, could compromise the effectiveness of drone-based auditing. However, this study contributes to the ESG literature by demonstrating how integrated audit technology transforms traditional sustainability reporting into continuous, AI-enhanced verification processes. These processes improve financial report quality while supporting Saudi Arabia’s Green Initiative and its goal of achieving net-zero carbon emissions by 2060. The adoption of AI and big data technologies in auditing represents a shift toward more automated and intelligent audit practices. These changes provide practical insights for government authorities, such as the Saudi Capital Market Authority (CMA), and may result in higher-quality financial reports and increased investor confidence.

Saudi Arabia’s audit market faces three governance challenges that existing frameworks may not fully address. These challenges concern a potential regulatory gap around autonomous AI accountability, a trust dimension that standard technology-adoption models may not fully capture, and limited mechanisms for independently verified ESG assurance under Vision 2030. This study adopts a conceptual design approach within the design science research tradition and proposes the CMA Agentic AI Platform as a practical response to these challenges. The platform comprises two segments. Segment 1 deploys autonomous drone swarms to verify corporate assets across four audit tasks—asset valuation, ESG compliance, anomaly detection and construction progress—using deep learning, thermal imaging and social-media cross-referencing. Segment 2 continuously monitors discretionary accruals and uses objective earnings-management data to inform auditor assignment and rotation decisions. This approach replaces subjective reputational assessments with transparent, quantifiable governance criteria. The platform is governed through the Triadic Agentic Framework, which extends classical agency theory by distributing authority across the Principal, the Human Agent and the AI Agent. The framework also operationalises Trust Expectancy as the primary adoption condition. The evidence base draws on two complementary streams: a PRISMA-guided systematic review and bibliometric analysis of thirty-nine peer-reviewed studies, and a documentary analysis of four national agentic-AI regulatory frameworks (SDAIA, MDDI/IMDA, NIST and ICO). The study contributes the concept of Algorithmic Accountability as a distinct governance domain, the Triadic Agentic Framework as an operational architecture for autonomous regulatory monitoring, and a reframing of the UTAUT trust construct for agentic-AI adoption in mature professional contexts. The platform converts theoretical governance into a regulatory architecture with direct implications for concentrated capital market regulators.

Purpose. This study proposes a two-stage methodology for using drone-captured inspection imagery as an audit-quality signal in detecting earnings management. The study integrates a computer-vision pipeline that verifies physical inventory with a financial-statement analysis grounded in the discretionary accruals (DACC) literature. Design. Stage 1 applies a pretrained YOLOv8 object-detection model to oblique low-altitude drone imagery of vehicle storage facilities to produce an Inventory Verification Discrepancy Score (IVDS). Stage 2 estimates Modified-Jones discretionary accruals on a panel of 281 firms (1,048 firm-year observations, 2011–2022) and tests how IVDS relates to DACC, with inventory intensity as a theoretical moderator. Robustness checks include firm and year fixed effects and the exclusion of financial firms. Findings. YOLOv8n achieves 95.5% mean vehicle-detection accuracy on oblique drone imagery without domain fine-tuning, establishing the feasibility of off-the-shelf models for inventory verification in audit contexts. In the financial panel, DACC is significantly higher in inventory-intensive firms (t = 6.02, p < 0.001) and in non-Big4-audited firms (t = −3.01, p = 0.003) at the univariate level. Multivariate regressions reveal that, after controlling for inventory intensity, the IVDS × HighInv interaction is positive and marginally significant (p < 0.10), providing tentative support for the moderating role of inventory intensity. Firm- and year-fixed-effects specifications confirm a significant Big4 effect (β = −0.008, p < 0.05) that is otherwise absorbed by industry and size controls. Contribution. To our knowledge this is the first study to specify a complete pipeline linking drone-based asset verification to accrual-based earnings management. We provide a methodological foundation for integrating unstructured visual audit evidence with traditional financial-statement analysis, together with empirical evidence on the feasibility of off-the-shelf computer-vision models for low-altitude oblique drone deployment scenarios.