AUI3704 Assignment 4
(COMPLETE ANSWERS)
Semester 2 2025 - DUE
25 September 2025
[Document subtitle]
[School]
[Course title]
,AUI3704 Assignment 4 (COMPLETE ANSWERS) Semester 2 2025 - DUE 25 September 2025
Course
Managing the Internal Audit Activity (AUI3704)
Institution
University Of South Africa (Unisa)
Book
Wiley CIA Exam Review, Internal Audit Activity\'s Role in Governance, Risk, and Control .
AUI3704 Assignment 4 (COMPLETE ANSWERS) Semester 2 2025 - DUE 25 September 2025;
100% TRUSTED Complete, trusted solutions and explanations
Criteria for evaluation: rubric for formative assessment Criteria Excellent Good Fair Needs
improvement Content quality (25) 20–25 15–20 10–15 0–10 The student demonstrates a deep
understanding of the chosen topic, You are required to answer Question 1 and select 1 of the 4
remaining topics (provided below) and answer the two questions relevant to the specific topic
that you selected. You may use your Unisa learning material, textbooks, the internet and
artificial intelligence (AI) to research the relevant topics. You must consult and cite adequate
sources and provide a reference list at the end of each answer. Refer to the rubric included on
how marks will be awarded. Your answers should reflect an understanding of internal auditing
practices, with a particular focus on how social media, AI and cybersecurity interact in the field
of internal auditing. You are encouraged to critically engage with the questions and to
demonstrate theoretical knowledge, information from the literature, AI and practical insight.
AI is reshaping the role of internal auditing by automating routine tasks, enhancing data
analysis, and enabling a more proactive approach to risk management. It's not about replacing
auditors, but about augmenting their capabilities, allowing them to focus on higher-value
activities that require professional judgment and critical thinking.
Key Roles and Applications of AI in Internal Auditing
Automation of Repetitive Tasks: AI and related technologies like Robotic Process
Automation (RPA) can handle tedious, time-consuming tasks such as data extraction,
document review, and reconciliation. This frees up auditors to perform more strategic
analysis and advisory functions.
, Enhanced Data Analysis: AI algorithms can analyze vast datasets in a fraction of the
time it would take a human. This allows auditors to examine entire populations of
transactions, rather than relying on sampling. AI can identify patterns, trends, and
anomalies that would be difficult or impossible for a human to spot, leading to more
thorough and accurate audits.
Fraud Detection and Risk Assessment: AI is particularly effective in identifying
suspicious activities and potential fraud. By continuously monitoring transactions and
comparing them against historical data and known fraud patterns, AI can flag irregular
behavior in real time. This shifts the audit from a reactive to a proactive stance, allowing
for early intervention.
Continuous Auditing and Monitoring: Traditional internal audits are often periodic. AI
enables continuous auditing, where systems constantly monitor data and controls,
providing real-time alerts for deviations or control failures. This ensures that risks are
managed as they emerge, rather than being addressed retrospectively.
Predictive Analytics: Using machine learning, AI can analyze historical data to predict
future risks and potential control weaknesses. This helps internal audit departments to
prioritize their efforts and allocate resources to the areas of highest risk, making the
audit plan more dynamic and effective.
Challenges and Considerations
While the benefits are significant, the adoption of AI in internal auditing isn't without its
challenges. These include:
Data Quality: AI models are only as good as the data they are trained on. Poor quality,
biased, or incomplete data can lead to flawed analysis and inaccurate conclusions.
"Black Box" Problem: Some advanced AI algorithms, particularly those in deep learning,
can be difficult to interpret. This "black box" nature can pose a challenge for auditors
who need to understand and explain how a specific conclusion was reached.
Skill Gaps: Internal auditors need to develop new skills in data science, AI ethics, and
data governance to effectively use and audit AI systems.
Ethical and Privacy Concerns: The use of AI to analyze sensitive data raises significant
ethical and privacy issues that must be addressed with robust governance frameworks.
(COMPLETE ANSWERS)
Semester 2 2025 - DUE
25 September 2025
[Document subtitle]
[School]
[Course title]
,AUI3704 Assignment 4 (COMPLETE ANSWERS) Semester 2 2025 - DUE 25 September 2025
Course
Managing the Internal Audit Activity (AUI3704)
Institution
University Of South Africa (Unisa)
Book
Wiley CIA Exam Review, Internal Audit Activity\'s Role in Governance, Risk, and Control .
AUI3704 Assignment 4 (COMPLETE ANSWERS) Semester 2 2025 - DUE 25 September 2025;
100% TRUSTED Complete, trusted solutions and explanations
Criteria for evaluation: rubric for formative assessment Criteria Excellent Good Fair Needs
improvement Content quality (25) 20–25 15–20 10–15 0–10 The student demonstrates a deep
understanding of the chosen topic, You are required to answer Question 1 and select 1 of the 4
remaining topics (provided below) and answer the two questions relevant to the specific topic
that you selected. You may use your Unisa learning material, textbooks, the internet and
artificial intelligence (AI) to research the relevant topics. You must consult and cite adequate
sources and provide a reference list at the end of each answer. Refer to the rubric included on
how marks will be awarded. Your answers should reflect an understanding of internal auditing
practices, with a particular focus on how social media, AI and cybersecurity interact in the field
of internal auditing. You are encouraged to critically engage with the questions and to
demonstrate theoretical knowledge, information from the literature, AI and practical insight.
AI is reshaping the role of internal auditing by automating routine tasks, enhancing data
analysis, and enabling a more proactive approach to risk management. It's not about replacing
auditors, but about augmenting their capabilities, allowing them to focus on higher-value
activities that require professional judgment and critical thinking.
Key Roles and Applications of AI in Internal Auditing
Automation of Repetitive Tasks: AI and related technologies like Robotic Process
Automation (RPA) can handle tedious, time-consuming tasks such as data extraction,
document review, and reconciliation. This frees up auditors to perform more strategic
analysis and advisory functions.
, Enhanced Data Analysis: AI algorithms can analyze vast datasets in a fraction of the
time it would take a human. This allows auditors to examine entire populations of
transactions, rather than relying on sampling. AI can identify patterns, trends, and
anomalies that would be difficult or impossible for a human to spot, leading to more
thorough and accurate audits.
Fraud Detection and Risk Assessment: AI is particularly effective in identifying
suspicious activities and potential fraud. By continuously monitoring transactions and
comparing them against historical data and known fraud patterns, AI can flag irregular
behavior in real time. This shifts the audit from a reactive to a proactive stance, allowing
for early intervention.
Continuous Auditing and Monitoring: Traditional internal audits are often periodic. AI
enables continuous auditing, where systems constantly monitor data and controls,
providing real-time alerts for deviations or control failures. This ensures that risks are
managed as they emerge, rather than being addressed retrospectively.
Predictive Analytics: Using machine learning, AI can analyze historical data to predict
future risks and potential control weaknesses. This helps internal audit departments to
prioritize their efforts and allocate resources to the areas of highest risk, making the
audit plan more dynamic and effective.
Challenges and Considerations
While the benefits are significant, the adoption of AI in internal auditing isn't without its
challenges. These include:
Data Quality: AI models are only as good as the data they are trained on. Poor quality,
biased, or incomplete data can lead to flawed analysis and inaccurate conclusions.
"Black Box" Problem: Some advanced AI algorithms, particularly those in deep learning,
can be difficult to interpret. This "black box" nature can pose a challenge for auditors
who need to understand and explain how a specific conclusion was reached.
Skill Gaps: Internal auditors need to develop new skills in data science, AI ethics, and
data governance to effectively use and audit AI systems.
Ethical and Privacy Concerns: The use of AI to analyze sensitive data raises significant
ethical and privacy issues that must be addressed with robust governance frameworks.
