Why Attend
Internal audit functions are increasingly expected to provide timely assurance over complex business processes, digital transactions, financial controls, and emerging risks. Traditional audit approaches based on periodic reviews and limited transaction samples may fail to identify control failures, unusual activities, and emerging risk patterns early enough for effective management action.
The AI for Internal Audit: Continuous Auditing and Control Testing course develops practical capabilities for applying artificial intelligence, machine learning, process mining, data analytics, and intelligent automation to internal audit activities. Participants learn how to analyze complete transaction populations, develop continuous auditing routines, identify control exceptions, evaluate anomalies, and use AI-assisted techniques to strengthen audit planning, fieldwork, reporting, and follow-up.
The programme emphasizes practical applications across finance, procurement, payroll, inventory, and enterprise systems while addressing data quality, false positives, explainability, confidentiality, and the professional judgment required when using AI-generated audit evidence.
Professional Problems This Course Is Designed to Solve
Delayed identification of control failures: Using continuous auditing and automated testing to detect unusual transactions, policy violations, and control exceptions between scheduled audit engagements.
Limited audit coverage: Moving beyond small transaction samples by applying data analytics to complete populations of financial and operational records.
Manual and repetitive control testing: Reducing time spent extracting data, reconciling records, checking approvals, and identifying exceptions through repeatable analytical procedures.
Difficulty detecting sophisticated anomalies: Applying statistical analysis and machine learning to identify unusual patterns, duplicate payments, suspicious transactions, and potential control weaknesses.
Fragmented audit data: Combining information from ERP systems, financial applications, procurement platforms, payroll systems, and operational databases for more effective assurance.
Unreliable AI-generated audit findings: Managing false positives, incomplete data, model limitations, unsupported conclusions, and insufficient audit evidence.
Weak follow-up on control deficiencies: Developing dashboards, exception management workflows, and continuous monitoring indicators that support timely remediation and management accountability.
Course Methodology
The programme combines instructor-led technical sessions, audit analytics demonstrations, realistic transaction datasets, control-testing workshops, AI-assisted audit exercises, case studies, and practical simulations. Participants explore workflows using Excel, Power BI, SQL, Python-based analytical techniques, and generative AI assistants where appropriate.
Exercises focus on audit objectives, data requirements, testing logic, exception analysis, evidence validation, and the communication of audit findings. Participants progressively develop a continuous auditing framework and an AI-supported control-testing approach for selected business processes.
Course Objectives
By the end of this course, participants will be able to:
Understand the applications and limitations of AI in internal auditing
Distinguish continuous auditing from continuous monitoring and periodic assurance
Identify business processes suitable for automated control testing
Extract, prepare, and validate data from financial and operational systems
Develop analytical tests for key financial and operational controls
Apply anomaly detection techniques to identify unusual transactions and potential control failures
Use process mining concepts to examine workflow deviations and control effectiveness
Apply generative AI to selected audit planning, documentation, and reporting activities
Design dashboards and exception reporting mechanisms for continuous auditing
Evaluate AI-generated results and maintain sufficient, reliable audit evidence
Address privacy, cybersecurity, model risk, and governance considerations
Develop an implementation roadmap for AI-enabled continuous auditing
Target Audience
This course is suitable for:
Internal auditors and senior internal auditors
Internal audit managers and heads of internal audit
IT auditors and information systems auditors
Risk management and compliance professionals
Internal control specialists
Fraud examination and investigation professionals
Financial controllers and assurance professionals
Audit analytics and business intelligence specialists
Governance, risk, and compliance managers
Professionals responsible for continuous auditing and control monitoring
Target Competencies
Participants will develop competencies in:
AI-enabled internal auditing
Continuous auditing
Automated control testing
Audit data analytics
Transaction anomaly detection
Process mining
Risk-based audit analytics
Audit evidence validation
Continuous assurance dashboards
AI governance and audit quality
