Advanced Auditing: Techniques and Tools for the Modern Auditor

Mohammed Muftah Ahmed Milad

doi:10.55544/sjmars.1.1.9

Authors

Mohammed Muftah Ahmed Milad Department of Accounting, Collage of Economic and Political Science Bani Waleed University, LIBYA.

DOI:

https://doi.org/10.55544/sjmars.1.1.9

Keywords:

Auditing techniques, Modern Auditor, robotic process automation, data analytics, efficiency

Abstract

This paper examines the integration of advanced auditing techniques and tools that are transforming the audit profession in response to the increasing complexity of today’s business landscape. Focusing on data analytics, robotic process automation (RPA), blockchain, and cybersecurity solutions, this study highlights how these technologies enhance audit accuracy, efficiency, and risk management. By enabling comprehensive data analysis, real-time monitoring, and automation of repetitive tasks, advanced auditing tools significantly streamline the audit process, allowing auditors to focus on critical insights and strategic recommendations. Case studies illustrate the practical application of these tools across industries, demonstrating improved audit outcomes such as increased exception detection, faster reconciliation processes, enhanced data security, and reduced costs. However, the adoption of these technologies also presents notable challenges, including high implementation costs, data privacy concerns, integration issues, and skill gaps within audit teams. Addressing these challenges is essential for organizations aiming to fully realize the potential of modern auditing tools. The findings underscore the importance of equipping auditors with both technological skills and traditional expertise to meet the demands of a dynamic, data-intensive environment. Embracing these advancements enables organizations to foster transparency, improve accountability, and support more informed decision-making through robust and efficient audit processes.

References

Braun, Virginia, and Victoria Clarke. 2021. Thematic Analysis: A Practical Guide. Washington, DC: SAGE Publications.

Agustí, María A., and Manuel Orta-Pérez. 2023. Big data and artificial intelligence in the fields of accounting and auditing: A bibliometric analysis. Revista Espanola de Financiacion y Contabilidad 52: 412–38.

Afsay, Akram, Arash Tahriri, and Zabihollah Rezaee. 2023. A meta-analysis of factors affecting acceptance of information technology in auditing. International Journal of Accounting Information Systems 49: 1–27.

Cornacchia, Giandomenico, Vito Anelli, Giovanni Biancofiore, Fedelucio Narducci, Claudio Pomo, Azzurra Ragone, and Eugenio Di Sciascio. 2023. Auditing fairness under unawareness through counterfactual reasoning. Information Processing and Management 60: 1–27.

Fedyk, Anastassia, James Hodson, Natalya Khimich, and Tatiana Fedyk. 2022. Is artificial intelligence improving the audit process? Review of Accounting Studies 27: 938–85.

Han, Hongdan, Radha K. Shiwakoti, Robin Jarvis, Chima Mordi, and David Botchie. 2023. Accounting and auditing with blockchain technology and artificial Intelligence: A literature review. International Journal of Accounting Information Systems 48: 100598.

Huson, Yazan Abu, Laura Sierra-García, and María Antonia Garcia-Benau. 2024. A bibliometric review of information technology, artificial intelligence, and blockchain on auditing. Total Quality Management & Business Excellence 35: 91–113.

Khuong, Nguyen Vinh, Le Huu Tuan Anh, Le Thi Ngoc Ha, Le Van Cuong, Nguyen Phan Thanh Ngan, Hoang Thi Minh Thu, and Le Kim Minh. 2023. Factors Affecting Decision to Adopt Artificial Intelligence During COVID-19 Pandemic Period: Evidence from PLS-SEM and fsQCA. Vision, 1–15.

Laux, Johann, Sandra Wachter, and Brent Mittelstadt. 2024. Trustworthy artificial intelligence and the European Union AI act: On the conflation of trustworthiness and acceptability of risk. Regulation & Governance 18: 3–32.

Munoko, Ivy, Helen L. Brown-Liburd, and Miklos Vasarhelyi. 2020. The Ethical Implications of Using Artificial Intelligence in Auditing. Journal of Business Ethics 167: 209–34.

Prokofieva, Maria. 2023. Integrating data analytics in teaching audit with machine learning and artificial intelligence. Education and Information Technologies 28: 7317–53.

Rodgers, Waymond, Sinan Al-Shaikh, and Mohamed Khalil. 2023. Protocol Analysis Data Collection Technique Implemented for Artificial Intelligence Design. IEEE Transactions on Engineering Management 71: 1–12.

Rodrigues, Luis, José Pereira, Amélia Ferreira da Silva, and Humberto Ribeiro. 2023. The impact of artificial intelligence on audit profession. Journal of Information Systems Engineering and Management 8: 1–7.

Seethamraju, Ravi, and Angela Hecimovic. 2023. Adoption of artificial intelligence in auditing: An exploratory study. Australian Journal of Management 48: 780–800.

Wassie, Fekadu Agmas, and László Péter Lakatos. 2024. Artificial intelligence and the future of the internal audit function. Humanities and Social Sciences Communications 11: 1–13.