DIGITAL TRANSFORMATION
IN AUDITING
STUDENTS NAME:
REEMA ALHASSAN
JUMANA ALOTAIBI
DALIA NASSER
REEMA ALOTHMAN
YARA ALOTAIBI
STUDENTS NUMBERS:
445005226
445005214
445005206
444005614
445005199
Group Number 4
“TRENDS, CHALLENGES, AND INNOVATIONS IN MODERN AUDITING”
SECTION: 6A1
INSTRUCTOR: D. AZZA SAAD ALZAHRANI
DIGITAL TRANSFORMATION
IN AUDITING
STUDENTS NAME:
REEMA ALHASSAN
JUMANA ALOTAIBI
DALIA NASSER
REEMA ALOTHMAN
YARA ALOTAIBI
STUDENTS NUMBERS:
445005226
445005214
445005206
444005614
445005199
Group Number 4
“TRENDS, CHALLENGES, AND INNOVATIONS IN MODERN AUDITING”
SECTION: 6A1
INSTRUCTOR: D. AZZA SAAD ALZAHRANI
Digital transformation is reshaping the auditing profession by making audits faster, smarter, and more accurate. Auditors now use advanced technologies such as artificial intelligence (AI), data analytics, and cloud systems instead of relying only on manual processes.
These technologies help auditors review large amounts of financial data efficiently, monitor transactions in real time, and detect errors or fraud more quickly. As a result, auditing has become more efficient, reliable, and technology-driven.
HOW DIGITAL TRANSFORMATION IS RESHAPING AUDITING
CORRECT ANSWER
ACTIVITY
Sort the following words into the correct category: traditional auditing or digital auditing
LETS MEET SARA
200 contracts
less then 24hours
02
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DIGITAL SKILLS
04
05
06
Text Mining
continuous learning
Soft Skills & Professional Judgment
Cybersecurity & Data Analytics
ARTIFICIAL INTELLIGENCE
(AI)
How does it help the auditor?
Improving auditors' ability to detect anomalies and allocate resources efficiently when analyzing the entire dataset rather than relying on sampling-based methods.
Enabling auditors to evaluate all company data, automate routine tasks, and identify high-risk and high-value areas.
Achieving 87% lead identification accuracy using the predictive model.
Real example
Deloitte has developed a tool called Argus that uses artificial intelligence to read contracts and automatically detect unusual clauses.
Johri et al. (2026)
CORRECT ANSWER
MACHINE LEARNING
(ML)
How does it help the auditor?
Examining massive amounts of journal entry data in accounting information systems and ERP systems
Detecting fraud by identifying deviations from normal journal entry patterns
Reducing false positives and false negatives in audits
Continuously learning from the flow of new journal entry data while retaining previously acquired knowledge
Real example
Banks use ML to monitor credit card transactions, and in the same way, auditors can use it to monitor fraudulent invoices or duplicate payments in a company.
Hemati et al. (2022)
TEXT MINING
How does it help the auditor?
Identify inconsistencies in financial narratives through automated text matching
Ensure compliance with complex regulatory frameworks through automated verification
Identify types of errors and specific erroneous entries within financial statements
Propose adjustments to address identified errors, citing accounting standards as supporting evidence.
Real example
If the CEO sends emails containing suspicious phrases, Text Mining can automatically detect these linguistic patterns.
Wang et al. (2025)
01
Cybersecurity
Auditors need cybersecurity skills to protect financial information from hacking and data leaks.
These skills help identify cyber risks and improve data security in companies.
02
Data Analytics
Data analytics helps auditors analyze large amounts of financial data quickly and accurately.
It also helps detect errors, unusual transactions, and possible fraud.
03
Real-Life Example
Using audit software to review transactions automatically.
CYBERSECURITY & DATA ANALYTICS
Auditors need digital skills to protect financial data and analyze large information quickly and accurately. These skills improve audit quality and help detect risks and fraud.
New Skills Required for Auditors in a Digital Environment
Soft Skills & Professional Judgment
In the digital age, auditors need more than technical skills because modern auditing also depends on soft skills and professional judgment. Soft skills help auditors communicate audit findings clearly and work effectively with clients and management.
Critical thinking and professional judgment are essential for helping auditors analyze complex digital information and accurately assess potential risks and unusual transactions.
Continuous Learning
Continuous learning has become essential in the auditing profession because technology is continuously evolving.
As digital systems become more advanced auditors need to regularly update their knowledge and develop new skills in areas such as artificial intelligence, cybersecurity, and data analytics. This helps auditors adapt effectively to modern digital environments and improves the quality and efficiency of the audit process.
Conclusion
digital transformation has significantly changed the auditing profession and highlighted the importance of developing both technical and professional skills for future auditors
THANK YOU
FOR YOUR TIME
ANY QUESTIONS?
REFERENCE:
Johri, A., Sayal, A., Chong, K. M., Khoja, M., Chaithra, N., Jha, J., & Tyagi, N. (2026). Enhancing audit quality and reducing costs: The impact of AI in banking and financial services. Frontiers in Artificial Intelligence, 8, 1718854. Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
Hemati, H., Schreyer, M., & Borth, D. (2022). Continual learning for unsupervised anomaly detection in continuous auditing of financial accounting data. AAAI 2022 Workshop on AI in Financial Services: Adaptiveness, Resilience & Governance. https://arxiv.org/abs/2112.13215
Wang, R., Liu, J., Zhao, W., Li, S., & Zhang, D. (2025). Automating financial statement audits with large language models. arXiv preprint arXiv:2506.17282. https://arxiv.org/abs/2506.17282