Day 1: Analytical Questions and Data Quality

Linking Analysis to Audit and Risk

• Define decision the analysis must support
• Frame testable hypotheses using available data
• Select necessary fields; avoid collecting everything
• Translate risk into an analytical question

Data Acquisition and Validation

• Define source population and analysis period
• Check completeness duplicates and missing values
• Reconcile totals with an independent source
• Prepare a data-quality worksheet

Data Preparation and Classification

• Standardize formats dates and identifiers
• Treat outliers carefully; avoid automatic deletion
• Derive test fields and calculation logic
• Prepare a dataset suitable for analysis

Day 2: Testing and Exception Detection

Rules and Threshold Tests

• Identify transactions exceeding authority or limits
• Detect illogical dates and sequence breaks
• Identify possible overrides of automated controls
• Build a sequence of rule-based tests

Pattern and Anomaly Analysis

• Test duplicates splitting and extreme values
• Compare behavior across periods/business units
• Separate seasonal patterns from unusual deviation
• Interpret results and select verification matters

From Exception to Audit Evidence

• Choose follow-up samples from analytical results
• Verify conditions against source documents/systems
• Estimate prevalence and define conclusion limitations
• Write analytical result with clear caveats

Day 3: Visualization, Continuous Monitoring, and Reporting

Data Visualization for Reviewers

• Choose charts to trend or distribution
• Avoid visuals distorting scale/proportions
• Connect each graphic with analytical question
• Redesign a misleading chart

Turning Tests into Continuous Monitoring

• Identify tests suitable for periodic reruns
• Set alert thresholds and review ownership
• Prevent unresolved alerts from accumulating indefinitely
• Design a continuous monitoring rule

Analysis File and Communication

• Document sources logic steps and results
• Enable independent reperformance by another reviewer
• Explain exceptions in business language
• Present analytical dashboard and its limitations

• Training datasets simulating realistic transactions and controls
• Pattern and exception tests linked with explicit risks
• Data-quality review before professional conclusions are drawn
• Analytical dashboards showing results limitations and verification paths