2026· International Journal of Trade, Economics and Finance· pp. 162-169· 0 citations· 18 references
TL;DR
Findings show AI provides broader coverage, timeliness, and granularity while preserving professional skepticism while preserving professional skepticism, and it is crucial to position AI as a tool that complements, rather than substitutes, human auditors.
Abstract
— This study investigates how Artificial Intelligence (AI) can transform auditing by improving fraud detection and audit quality. However, traditional methods, limited by sampling and manual inspection, failed to detect complex frauds such as Luckin Coffee. Using the Technology – Organization – Environment (TOE) framework and Socio-Technical Systems (STS) theory, the research adopted a qualitative case study design. Audit failures were reconstructed through triangulated evidence and counterfactual reasoning, assessing how Machine Learning (ML) and Natural Language Processing (NLP) could have detected anomalies in real time. Findings show AI provides broader coverage, timeliness, and granularity while preserving professional skepticism. The study contributes by situating AI adoption within socio-technical and organizational contexts and proposing a framework for responsible implementation, and it is crucial to position AI as a tool that complements, rather than substitutes, human auditors.
Artificial Intelligence (AI) has emerged as one of the most transformative technologies influencing organizational governance, financial oversight, and risk management practices worldwide. The integration of AI into internal audit functions has significantly altered the traditional audit landscape by enhancing operational efficiency, improving fraud detection capabilities, strengthening risk assessment procedures, and enabling real-time auditing practices. This research paper examines the transformative role of AI technologies such as machine learning, neural networks, natural language processing, robotic process automation, and predictive analytics in reshaping internal audit operations. The study explores how AI-driven systems automate repetitive audit tasks, analyze large volumes of structured and unstructured data, and improve audit accuracy while reducing operational costs. Furthermore, the paper evaluates the challenges associated with AI adoption, including ethical concerns, cybersecurity risks, data privacy issues, technological dependence, and skill gaps among auditors. A comparative analysis between traditional and AI-enabled audit practices is also presented to assess the effectiveness and efficiency of AI-based auditing systems. The study concludes that AI is not replacing internal auditors but transforming their roles into more strategic, analytical, and advisory-oriented functions. Organizations that successfully integrate AI into their audit frameworks can achieve greater transparency, stronger governance, and improved organizational resilience in an increasingly digital business environment.
F. Raidah, M. Jobair, Md. Halimuzzaman et al.· American Journal of Financia...· 0 citations
Artificial intelligence (AI) is increasingly used to support financial reporting fraud detection, yet algorithmic accuracy alone does not determine how effective AI proves to be in practice. This study develops and tests an integrated model in which Auditor Trust in AI Systems mediates the effects of AI Predictive Capability, Explainable AI, Data Quality, and AI Governance and Internal Control Quality on Perceived Financial Reporting Fraud Detection Effectiveness. The model draws on human–AI trust theory, the automation–augmentation perspective, and agency theory. Data from 450 U.S. auditing and financial reporting professionals were collected by structured questionnaire and analysed using Partial Least Squares Structural Equation Modelling in SmartPLS 4. All four antecedents significantly predicted Auditor Trust in AI Systems (R² = .697), which in turn strongly predicted fraud detection effectiveness (R² = .524; β = .724, p < .001) and significantly mediated the effects of all four antecedents. The findings show that AI-enabled fraud detection is a socio-technical outcome that delivers value only when systems are explainable, built on sound data, embedded in appropriate governance structures and trusted by their users. The study contributes an integrated framework linking technological, informational, organisational and behavioural conditions, with practical guidance for audit firms and regulators.
JEL classification numbers: M41; M42; O33; G30.
Keywords: artificial intelligence, financial reporting fraud detection, auditor trust, explainable AI, data quality, AI governance.
The findings indicate that XAI is mainly applied to fraud detection, credit assessment, financial auditing, and decision-support processes, with a predominance of techniques such as SHAP and LIME.
Iván Patricio Arias-González, Gabriela Serrano-Torres, Eduardo Ramiro Dávalos-Mayorga et al.· Frontiers in Artificial Inte...· 0 citations
Artificial intelligence (AI) is quietly transforming accounting and financial reporting, by letting organizations automate those repeatable tasks, scan huge piles of financial plus non-financial information, spotlight strange transactions, and in general help audit and assurance chores. As many organizations shift from the old rule-based accounting setups toward machine learning, natural language processing predictive analytics, and other AI enabled tools, a not so small research question shows up: does the sheer technological sophistication actually end up meaning better financial reporting. In this study we look at the link between AI and financial reporting quality, while paying close attention to things like accuracy and reliability, relevance, timeliness, transparency, fraud detection, error reduction, professional judgement internal controls, and audit quality. The discussion leans on information processing theory and the technology organization environment perspective, and it builds a conceptual model where AI capability is expected to affect financial reporting quality through automated processing anomaly detection continuous monitoring analytical capacity and decision support. But this influence is not free governance data quality human expertise and organizational readiness are treated as conditions that shape how well those mechanisms work sort of like the engine needs fuel and steering at the same time. The paper uses a quantitative research design for the empirical part and suggests using structured questionnaires given to accounting professionals, auditors, finance managers and reporting specialists and where possible adding archival indicators of reporting quality. For the analysis descriptive statistics are more suitable than reliability and validity checks, correlation analysis and multiple.
References
Ahmed, S., & Asif, M. (2026). The impact of hybrid working on employee well-being with the moderating role of organizational performance: A case study of IT sector in Pakistan. Qualitative Research Journal for Social Studies, 3(2), 1006-1030. https://doi.org/10.63878/qrjs1173
Ashraf, M. (2025). Does automation improve financial reporting? Evidence from internal controls. Review of Accounting Studies, 30, 436–479. https://doi.org/10.1007/S11142-024-09822-Y
Asif, M. (2022). Integration of information technology in financial services and its adoption by the financial sector in Pakistan. Inverge Journal of Social Sciences, 1(2), 23–35. https://doi.org/10.63544/ijss.v1i2.31
Asif, M. (2025). Financial performance of startups linked to universities: Evidence from developing economies. Journal of Applied Linguistics and TESOL, 8(3), 2736–2763. https://doi.org/10.63878/jalt2003
Asif, M., Ali, A., & Shaheen, F. A. (2025). Assessing the effects of artificial intelligence in revolutionizing human resource management: A systematic review. Social Science Review Archives, 3(4), 2887–2908. https://doi.org/10.70670/sra.v3i3.1055
Asif, M., & Asghar, R. J. (2025). Managerial accounting as a driver of financial performance and sustainability in small and medium enterprises in Pakistan. Center for Management Science Research, 3(7), 150–163. https://doi.org/10.5281/zenodo.17596478
Asif, M., & Bashir, M. (2026). Augmentation or anxiety? The mediating role of employee trust in the relationship between generative AI implementation, job crafting, and productivity. The Critical Review of Social Sciences Studies, 4(1), 4550–4583. https://doi.org/10.59075/mrqkn978
Asif, M., Khan, A., & Pasha, M. A. (2019). Psychological capital of employees' engagement: Moderating impact of conflict management in the financial sector of Pakistan. Global Social Sciences Review, 4(3), 160–172. https://doi.org/10.31703/gssr.2019(IV-III).15
Asif, M., Shah, H., & Asim, H. A. H. (2025). Cybersecurity and audit resilience in digital finance: Global insights and the Pakistani context. Journal of Asian Development Studies, 14(3), 560–573. https://doi.org/10.62345/jads.2025.14.3.47
Batool, S. F., & Asif, M. (2026). Impact of Hybrid Work on Employee Engagement: The Moderating Role of Work-Life Balance Among Academic and Administrative Staff in Universities of Pakistan. Journal of Business Insight and Innovation, 5(6), 28–45. https://doi.org/10.63544/jbii.v5i6.99
Bhattacharya, I., & Mickovic, A. (2024). Accounting fraud detection using contextual language learning. International Journal of Accounting Information Systems, 53, Article 100682. https://doi.org/10.1016/j.accinf.2024.100682
Dash, A., Samantray, B. S., Reddy, K. H. K., Amin, F., & Mishra, S. K. (2026). A secure framework for smart waste management using IoT, machine learning, and blockchain. In 2026 1st International Conference on Emerging Trends in Advancements and Applications of Computational Intelligence Techniques (ETAACT) (pp. 1-7). IEEE. https://doi.org/10.1109/ETAACT69135.2026.11541497
Financial Accounting Standards Board. (2010). Statement of financial accounting concepts No. 8: Conceptual framework for financial reporting. FASB.
Galbraith, J. R. (1973). Designing complex organizations. Addison-Wesley.
Hussain, M., & Asif, M. (2026). Impact of Compensation on Employee Performance with the Moderating Role of Integrating Style of Conflict Management in the Banking Sector of Pakistan. Journal of Business Insight and Innovation, 5(7), 184–197. https://doi.org/10.63544/jbii.v5i7.101
International Accounting Standards Board. (2018). Conceptual framework for financial reporting. IFRS Foundation.
International Auditing and Assurance Standards Board. (2024). The IAASB's new technology position: 8 actions to embrace technology and innovation.
International Auditing and Assurance Standards Board. (2025). Technology quality management workstream.
Khan, A., Amin, F., & Imtiaz, U. (2026). SecurePulse-GRID: Trust-aware predictive load balancing and rapid cyber containment in distributed smart microgrids. The Asian Bulletin of Big Data Management, 6(1), 697-708. https://doi.org/10.62019/smxj7m37
Khan, A., Imtiaz, U., & Amin, F. (2025). Honeytoken-augmented AI model for insider threat detection using cyber deception and isolation forest. Kashf Journal of Multidisciplinary Research, 2(9), 294–313. https://doi.org/10.71146/kjmr625
Mushtaq, C. M. A., Asif, M., & Farhat, A. B. (2026). The moderating role of conflict management in the relationship between transformational leadership and employee commitment in the IT sector of Pakistan. TAMSAAL, 4(7), 297–318. https://doi.org/10.59075/tamsaal.v4i7.73
Qatawneh, A. M. (2024). The role of artificial intelligence in auditing and fraud detection in accounting information systems: Moderating role of natural language processing. International Journal of Organizational Analysis, 33(6), 1391–1409. https://doi.org/10.1108/IJOA-03-2024-4389
Rahman, E., & Asif, M. (2026). The Moderating Role of Transformational Leadership in the Relationship Between Organizational Mindfulness and Employee Well-Being in Pakistan’s Educational Sector. Journal of Business Insight and Innovation, 5(5), 26–42. https://doi.org/10.63544/jbii.v5i5.98
Safdar, M. M., Asif, M., & Amjad, M. F. (2026). Strategic human resource management in the digital era: A quantitative analysis of perceived EVP, E-HRM practices, organizational trust, and employee retention in Pakistan's fashion retail sector. TAMSAAL, 4(8), 46–76. https://doi.org/10.59075/tamsaal.v4i8.108
Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington Books.
Fraz Ahmed Shaikh· Inverge Journal of Social Sc...· 0 citations
The integration of artificial intelligence (AI) into auditing has created a paradigm shift, presenting both unprecedented opportunities to enhance audit quality and significant policy challenges that threaten the foundations of professional judgment. This systematic literature review analyses peer-reviewed articles to synthesize the current landscape of AI in auditing and identify the primary policy challenges confronting the profession. Our analysis reveals a fundamental tension between the automation of audit tasks and the preservation of professional skepticism and judgment. Key themes emerging from the literature include the paradox of professional judgment in an automated environment, the double-edged sword of AI in enhancing audit quality while introducing new risks, the critical need for transparency and explainability in AI systems, the pervasive threat of algorithmic bias, and the significant gaps in regulatory frameworks and professional standards. The findings indicate that while AI offers powerful tools for data analysis, fraud detection, and risk assessment, its adoption is hampered by a complex web of ethical, technical, and organizational barriers. The primary policy challenges identified include regulatory lag, the erosion of professional identity, new quality assurance demands, evolving competency standards, the need for robust ethical frameworks, unresolved liability issues, and a lack of standardization. This review concludes that the auditing profession is at a critical juncture, requiring a concerted effort from regulators, standard-setters, firms, and educators to navigate the transformative impact of AI. I propose a research agenda focused on the long-term effects of AI on professional judgment, the effectiveness of governance models, and the development of new audit methodologies that effectively integrate human and machine intelligence.
Geoffrey Odoch· International journal of com...· 0 citations
This study examines artificial intelligence (AI) in external auditing by synthesizing existing evidence, clarifying key concepts, identifying theoretical and methodological gaps, and outlining future research directions. A systematic literature review and bibliometric analysis were conducted on 130 peer-reviewed articles retrieved from Scopus and Web of Science databases. The review followed the PRISMA 2020 guidelines, while VOSviewer was used to map research trends, and thematic clusters. Research on AI in auditing has grown substantially, with the United States, China, and the United Kingdom leading scholarly contributions. The analysis identified three dominant research streams: machine learning and fraud detection, audit analytics and big data, and AI adoption and governance. Commonly applied AI techniques include machine learning, neural networks, natural language processing, robotic process automation, and expert systems. The study suggested that AI enhances fraud detection, risk assessment, and audit quality, while raising concerns regarding algorithmic bias, transparency, and professional skepticism. The study develops an integrated framework linking AI applications to audit quality and provides a research agenda to guide future inquiry. The findings offer practical insights for auditors, regulators, and organizations seeking to implement AI responsibly and effectively in audit processes.
