AI for Internal Audit: Continuous Auditing and Control Testing

Course overview

Course outline

1. 1Day 1: AI-Enabled Internal Auditing and Continuous Assurance

   • Examining how artificial intelligence, machine learning, generative AI, and data analytics support risk-based internal audit activities

   • Distinguishing continuous auditing, continuous controls monitoring, and conventional periodic audit engagements

   • Identifying opportunities for AI-assisted assurance across financial reporting, procurement, payroll, inventory, and operational processes

   • Mapping business risks to key controls, audit assertions, transaction data, and measurable control-testing criteria

   • Evaluating data availability, system access, audit trails, data quality, and technology readiness for continuous auditing

   • Practical exercise: Developing a continuous auditing use-case map linking audit risks, key controls, required datasets, and expected assurance outcomes

2. 2Day 2: Audit Data Preparation and Automated Control Testing

   • Extracting and combining audit data from ERP applications, accounting systems, procurement records, payroll databases, and transaction logs

   • Preparing audit datasets using SQL, Excel, and Power Query to address duplicates, missing values, inconsistent formats, and incomplete records

   • Designing automated tests for approval limits, segregation of duties, duplicate payments, unauthorized transactions, and policy compliance

   • Applying full-population testing, reconciliation procedures, rule-based analysis, and exception identification to financial and operational controls

   • Establishing repeatable testing workflows with documented logic, data validation checks, and traceable audit evidence

   • Practical workshop: Building automated control tests for accounts payable and procurement transactions, including exception identification and supporting evidence

3. 3Day 3: Machine Learning, Anomaly Detection and Process Mining

   • Understanding supervised and unsupervised machine learning applications for audit risk identification and transaction analysis

   • Applying statistical outlier detection, clustering concepts, and pattern analysis to identify unusual financial and operational activity

   • Investigating duplicate vendors, abnormal payment behaviour, unusual journal entries, transaction splitting, and potential fraud indicators

   • Using process mining concepts to analyze event logs, identify process deviations, and examine approval and authorization workflows

   • Evaluating model outputs through false-positive analysis, explainability, validation, and professional assessment of potential control failures

   • Case study: Analyzing a procurement transaction dataset to identify anomalies, investigate exceptions, and distinguish control weaknesses from legitimate business activity

4. 4Day 4: Continuous Auditing Dashboards and AI-Assisted Audit Workflows

   • Designing continuous auditing routines with defined testing frequency, data refresh requirements, control thresholds, and exception escalation procedures

   • Developing Power BI dashboards to monitor control exceptions, transaction anomalies, risk indicators, and remediation status

   • Establishing risk-based alert thresholds and exception prioritization methods to reduce unnecessary investigations

   • Applying generative AI to audit planning, risk-control matrix development, working-paper summaries, and draft audit observations

   • Integrating analytical results into audit management workflows while maintaining reviewer approval, evidence traceability, and appropriate access controls

   • Practical workshop: Creating a continuous control-testing dashboard and exception management workflow for a selected financial or operational process

5. 5Day 5: AI Audit Governance, Assurance Quality and Implementation

   • Assessing AI-related audit risks involving data confidentiality, access permissions, algorithmic bias, model reliability, and cybersecurity

   • Establishing validation procedures for AI-generated findings, analytical models, automated tests, and supporting audit evidence

   • Defining responsibilities between internal audit, IT, data owners, compliance teams, and management for continuous auditing operations

   • Measuring continuous auditing effectiveness through control coverage, exception resolution time, testing efficiency, detection timeliness, and remediation performance

   • Developing a phased implementation roadmap covering use-case selection, technology requirements, staff capabilities, pilot testing, governance, and operational deployment

   • Capstone exercise: Designing and presenting an AI-Enabled Continuous Auditing and Control Testing Framework covering risk assessment, automated tests, anomaly detection, reporting dashboards, evidence validation, and implementation priorities