Upon completion of this course, participants will be able to:

• Design and execute data extraction and cleaning processes suitable for continuous auditing.
• Apply statistical sampling techniques and identify transactions for 100% population testing.
• Utilize data analytics tools (e.g., ACL, Python) to detect unusual patterns and outliers indicative of fraud or control failure.
• Automate control testing procedures and implement continuous monitoring dashboards.
• Develop persuasive data visualizations and dashboards to effectively communicate audit findings and risk exposure.
• Perform advanced analysis on procure-to-pay and order-to-cash cycles to identify process inefficiencies.
• Integrate data analytics results into the core risk assessment and audit planning methodology.
• Understand the governance and security required for handling audit-sensitive data.

• Internal Auditors and External Auditors
• Compliance Officers and Specialists
• Risk Management Professionals
• Forensic Accountants and Fraud Investigators
• Financial Controllers and Analysts
• IT Audit Specialists

The methodology is hands-on, focusing on practical application of audit analytics tools. **Case studies** involve examining real (anonymized) financial ledger data to identify control breakdowns in a typical purchase order system. **Individual exercises** require participants to write scripts to clean and prepare data for testing, apply Benford's Law, and create a compliance score. **Group activities** focus on developing a Continuous Control Monitoring (CCM) plan for a specific high-risk process. **Syndicate discussions** debate the challenges of gaining data access and the ethical considerations of using advanced analytics in sensitive internal investigations.

UNIT 1: Analytics in the Modern Audit Function

• The Evolution of Audit: Continuous Auditing and Monitoring
• Defining the Audit Data Model and Key Data Sources
• Data Acquisition and Transformation (ETL) for Audit Readiness
• Understanding Data Quality and its Impact on Audit Reliability
• Introduction to Audit-Specific Software (e.g., ACL, IDEA)

UNIT 2: Techniques for Fraud and Anomaly Detection

• Statistical and Predictive Modeling for Risk Scoring
• Identifying Duplicate Payments, Split Transactions, and Outliers
• Using Benford's Law for analyzing invoice and financial data distribution
• Implementing simple machine learning models for anomaly identification
• Analyzing T&E (Travel & Expense) and Payroll data for non-compliance

UNIT 3: Process Analytics and Control Testing

• Applying Analytics to the Procure-to-Pay Cycle (e.g., Vendor Master File Analysis)
• Data Analysis in the Order-to-Cash Cycle and Revenue Assurance
• Automating IT General Control (ITGC) Testing
• Continuous Control Monitoring (CCM) setup and alerting thresholds
• Identifying Segregation of Duties (SOD) violations through data analysis

UNIT 4: Visualization and Reporting of Findings

• Principles of Effective Data Visualization for Audit Reports
• Designing Executive Dashboards for Risk Oversight
• Structuring the Audit Report using data-driven evidence
• Techniques for Storytelling and Persuasion in Audit Communication
• Documenting and Validating Analytical Procedures

UNIT 5: Governance and Future of Audit Analytics

• Data Security, Privacy, and Ethical Considerations for Audit Data
• Establishing a Data Analytics Center of Excellence within Internal Audit
• Integrating Data Analytics into the Annual Audit Plan and Scoping
• Exploring Advanced Techniques: Text Mining and Predictive Auditing
• Measuring the ROI and Value Creation of the Data-Driven Audit Function