1. Business Case for Data Analytics (DA) for Auditors and Accountants
1.1. What is data analytics and why is it useful for you
1.2. Current and future trends in analytics
1.3. Opportunities and challenges of data analytics
1.4. Availability of DA Tools/Techniques (e.g. Excel, Google Sheet, Caseware IDEA, ACL)
2. Data Analytics Approach to Business Processes
2.1. DA Framework
2.1.1. Identifying and obtaining data
2.1.2. Extract-Transform-Load
2.1.3. Data storage/staging
2.1.4. Analysis
2.1.5. Reporting/Visualisation
3. Identifying and Obtaining Data
3.1. Data availability
3.2. Data integrity
3.3. Data confidentiality
4. Extract-Transform-Load
4.1. Export/importing data
4.2. Preparation for analysis
4.3. Loading to storage/staging
5. DA Approaches
5.1. Descriptive analytics
5.1.1. Statistical descriptors (e.g. mean, media, mode, deviation)
5.1.2. Control totals
5.1.3. Field types
5.2. Diagnostic analytics
5.2.1. Types of comparisions
5.2.1.1. Across time (this period/last period)
5.2.1.2. Between functional units/departments
5.2.1.3. Against reasonableness (e.g. 365 days a year, overtime hours)
5.2.1.4. Matching across different databases
5.2.2. Time related data
5.2.2.1. Aging analysis
5.2.2.2. Stratification by time period
5.2.2.3. Date of the week / public holidays
5.2.3. Value related data
5.2.3.1. Statistical, arithmetic and computational accuracy
5.2.3.2. Top/Bottom analysis
5.2.3.3. Pareto analysis (80/20)
5.2.3.4. Thresholds and percentages
5.2.4. Text related analysis
5.2.4.1. Keyword search functions
5.2.4.2. Keyword frequency
5.2.5. Sample selection
5.3. Predictive Analytics
5.3.1. Benford’s law
5.3.2. Correlation and multi-variate analysis
5.3.3. Caveats
5.4. DA for Accounting and Related Processes
5.4.1. Purchases and Payments
5.4.2. Payroll
5.4.3. Fixed Assets and inventory
5.4.4. Receipts and Revenue
5.4.5. Due Diligence, Conflicts of Interest Checks
6. Fraud and Investigative Analytics
6.1. Framework and approach
6.2. Useful analytical techniques
6.2.1. Trend and pattern detection
6.2.2. Matching across white and black lists (e.g. ACRA, Anti-Money Laundering)
6.2.3. Text search
7. How to build your own analytics capabilities for your team
7.1. People
7.2. Process
7.3. Technology
7.4. Pilot Projects / Proof-of-Concept
The workshop would include interactive elements such as hands-on activities with Excel/Google Sheet and online quizzes and polls (using online tools e.g. Slido or equivalent) to facilitate reflection and encourage participants to learn from one another’s experiences in their respective areas of work.
Training Methodology
Lecture style with exercises / case studies
Closing Date for Registration
1 week before programme or until full enrolment.
