Lecture 11 - Audit Practice

Question 1

Data Analytics

4 Categories

Answer 1

1. Descriptive - What has happened?
2. Diagnostic - Why did it happen?
3. Predictive - What could happen?
4. Prescriptive - What should happen?
   - recommendation on what should be done in the future

1-4: from low value & difficulty to high

Question 2

Data Analytics

Benefits

Answer 2

1. Documents to be prepared
   - reduction of prepared-by-client doc
   - employees can concentrate on business tasks
2. Effective and Efficient audit
   - 100% coverage transaction volume
   - Focus on exception/outliers
3. Business Insights
   - information on utilization and quality of SAP
   - Overview of transaction flows and controls
4. Fraud analyses
   - Focused audit of general ledger journal entreis and segregation of duty possible
5. Compliance
   - violations detected can be reported to those charges w/ governance

Question 3

Data Analytics

Evolution of the Auditor Role

Answer 3

• Shift from routine to non-routine activities
• Shift from manual work to data analyses and positions with increased degree of estimation and jusdgment, etc
• Other/new skills required
• Change from ‘supervisor’ to trusted but independent ‘insider’
• greater collaboratin and knowledge sharing
• Up to date with the latest tech and agile in the use of various tools
• High level of process understanding
• Increasing involvement during the financial year and no longer mainly at the end of year

Question 4

Data Analytics

Technology integration in Audit (3 ways)

Answer 4

1. Audit in IT
   (general IT controls, Data migration, cyber security, etc.)
2. Audit with IT
   (process audit analytics, ERP security analysis, workflow collaboration, process mining, etc)
3. Insights
   (centralization catalysts, increased audit quality, continuous insights, innovation, benchmarking)

Question 5

Data Analytics

Feasibility Criteria

Answer 5

1. Client: IT system landscape, willingness, ability to provide data, data volume & quality, General IT controls environment
2. Auditor: skilled execution of the tools, time constraints, involvement of data analytics experts
3. Legal requirement: audit standards, data protection
4. Tools: assumed data quality, complexity, license fees, server capacity/cloud

Question 6

Data Analytics

4 Layer model

Answer 6

(Bottom-Up)
4. IT infrastructure (hardware, netowrks, etc)
3. IT base systems (SAP, Oracle, IT controls related to application)
2. IT applications (automated systems, controls)
1. Business Processes (Manual controls)

Question 7

Data Analytics

Prerequisiite IT controls

Answer 7

Question 8

Data Analytics

Deployment

Answer 8

Question 9

Data Analytics

Extraction Risks

Answer 9

Question 10

Data Analytics

Cleansing

Answer 10

Question 11

Data Analytics

Outliers and Exceptions

Answer 11

Question 12

Data Analytics

False Positives / Negatives

Answer 12

Question 13

Data Analytics

Visualization (1/2)

Answer 13

Question 14

Data Analytics

Visualization (2/2)

Answer 14