ALRAQABA . ISSUE 20 49 Conclusion The integration of ML into the auditing profession signifies a pivotal shift towards a more efficient, accurate, and data-driven future. While challenges and ethical considerations persist, the potential benefits are undeniable. By embracing ML and addressing its complexities, auditors can enhance their ability to detect fraud, mitigate risks, and provide greater value to stakeholders. The future of auditing envisions a collaborative partnership between humans and machines. Auditors will focus on higher-level analysis, judgment, and communication, while ML handles routine tasks and data processing. This augmented approach will lead to more efficient audits, improved decisionmaking, and enhanced risk assessment. To fully realize the potential of ML, the auditing profession must invest in continuous education, training, and research. By fostering a culture of innovation and ethical responsibility, auditors can position themselves as trusted advisors in the digital age. The rapid adoption of ML in auditing, with estimates suggesting a rise from 10% of auditors using ML in 2019 to a projected 80% by 2027/2028, underscores the transformative power of this technology. As ML continues to evolve, its impact on the auditing profession will only grow. By embracing ML and addressing its challenges, auditors can ensure a future where technology empowers them to deliver more efficient, accurate, and insightful audits. References: 1. Al-Ebrahim, M. (2023), “The Next Generation of Supreme Audit Institutions: Looking Ahead”. Supreme Audit Institution(SAI)ofThailand.KingdomofThailand.Retrievedfrom:https://prezi.com/view/95j8cM8rcLfJ886D2W8L/ 2. Al-Ebrahim, M. and Ransing, R. (2020), “Manufacturing Process Causal Knowledge Discovery Using a Modified Random Forest-based Predictive Model”. Thesis Research. Swansea University. Swansea, Wales, United Kingdom. Retrieved from: https://cronfa.swan.ac.uk/Record/cronfa59728 3. PricewaterhouseCoopers (PwC). (2019), “Audit Quality Report”. PwC. Retrieved from: https://www.pwc.com/gx/ en/about/pdf/pwc-us-transparency-report-2019.pdf. 4. KPMG. (2022), “Using AI and Machine Learning to Reduce Fraud and Improve Supply Chain Integrity”. KPMG. Retrievedfrom: https://assets.kpmg.com/content/dam/kpmg/au/pdf/2022/aged-care-market-analysis-2022.pdf 5. Brody, P. (2023), “Seize the Day: Public Blockchain is on the Horizon. Examine the Limitations of Private Blockchain and Benefits of Public Blockchain to Make the Most of this Technology Opportunity”. Ernst & Young (EY). Retrieved from: https://assets.ey.com/content/dam/ey-sites/ey-com/en_gl/topics/blockchain/ey-publicblockchain-opportunity-snapshot.pdf 6. Ernst & Young (EY). (2023), “EY Value Realized 2023: Reporting Progress on Global Impact”. EY Global. Retrieved from: https://assets.ey.com/content/dam/ey-sites/ey-com/en_gl/topics/global-review/2023/ey-valuerealized-2023-reporting-progress-on-global-impact-v3.pdf 7. Deloitte. (2023), “The Use of Cryptocurrency in Business: Why Companies Should Consider Using Cryptocurrency”. Deloitte. Retrieved from: https://www2.deloitte.com/content/dam/Deloitte/us/Documents/ audit/us-corporates-using-crypto-pov.pdf 8. Deloitte. (2023), “Navigating the Insurance Sector through a Fraud Risk Lens”. Deloitte US. Retrieved from: https://www2.deloitte.com/content/dam/Deloitte/in/Documents/financial-services/in-insurance-fraud-survey2023-noexp.pdf Computers Audit and
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