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AI for Internal Audit Training Course is a comprehensive and practical professional programme designed to equip internal auditors, audit managers, risk professionals, compliance officers, accountants, forensic investigators, and governance specialists with the knowledge and practical skills required to apply Artificial Intelligence (AI), machine learning, generative AI, automation, and advanced data analytics to modern internal audit functions. The course explores how AI can transform audit planning, risk assessment, audit testing, continuous auditing, fraud detection, control monitoring, audit evidence analysis, reporting, and assurance activities. Participants will learn how AI-powered audit tools can process large volumes of structured and unstructured data, identify patterns and anomalies, automate repetitive audit procedures, and generate timely insights while maintaining appropriate human oversight and professional judgment.
The programme provides extensive coverage of AI applications in internal audit, machine learning for audit analytics, intelligent process automation, natural language processing, generative AI for audit documentation, predictive risk analytics, anomaly detection, continuous monitoring, automated control testing, and AI-assisted audit reporting. Participants will examine how AI can be applied to general ledger analysis, accounts payable, accounts receivable, payroll, procurement, revenue, expenses, journal entries, contracts, emails, policies, and other organizational information. Particular emphasis is placed on responsible AI for internal audit, including data quality, model governance, explainability, privacy, cybersecurity, bias, access controls, auditability, human-in-the-loop review, and ethical use of AI technologies.
Participants will develop practical skills in identifying audit processes suitable for AI automation, preparing data for AI analysis, designing audit analytics, interpreting AI-generated outputs, investigating anomalies, assessing risks, and validating AI-supported audit evidence. The course also examines AI-assisted fraud detection, predictive risk scoring, intelligent document review, contract analytics, natural-language analysis, automated working papers, audit report drafting, and continuous control monitoring. Relevant general case studies will enable participants to evaluate AI-generated audit findings, identify unusual financial transactions, detect procurement irregularities, analyse control failures, assess fraud indicators, and develop appropriate audit responses while distinguishing reliable analytical evidence from outputs requiring further professional validation.
By the end of the training, participants will be able to integrate AI responsibly into internal audit activities, improve audit efficiency and coverage, strengthen fraud and risk detection, automate repetitive audit procedures, and enhance data-driven assurance. The programme is suitable for organizations seeking to modernize internal audit, strengthen governance and risk management, improve continuous monitoring, and prepare audit teams for AI-enabled finance and business environments. The course can be customized for banks, government institutions, NGOs, corporations, development organizations, insurance companies, audit firms, financial institutions, and other entities using ERP, accounting, procurement, payroll, compliance, or enterprise information systems.
1. Understand the fundamentals of Artificial Intelligence, machine learning, generative AI, and automation in internal audit.
2. Identify practical applications of AI across internal audit planning, risk assessment, testing, monitoring, and reporting.
3. Apply AI-powered data analytics to large volumes of financial, operational, and transactional information.
4. Use AI techniques to identify anomalies, fraud indicators, control weaknesses, and emerging organizational risks.
5. Apply natural language processing and generative AI to audit documents, policies, contracts, reports, and other unstructured information.
6. Develop AI-assisted continuous auditing and continuous control monitoring procedures.
7. Evaluate AI-generated audit outputs, assess data quality, validate results, and apply professional judgment.
8. Understand AI governance, cybersecurity, data privacy, model risk, bias, explainability, and responsible AI principles.
9. Improve audit documentation, working papers, reporting, recommendations, and communication through appropriate AI applications.
10. Develop an AI-enabled internal audit transformation roadmap that improves audit efficiency, coverage, quality, and assurance effectiveness.
1. Modernizes internal audit through artificial intelligence, automation, advanced analytics, and intelligent assurance.
2. Improves audit efficiency by automating repetitive and time-consuming audit procedures.
3. Enables analysis of larger volumes of financial and operational data than traditional manual approaches.
4. Strengthens early detection of fraud, anomalies, control weaknesses, and emerging risks.
5. Enhances continuous auditing and continuous control monitoring capabilities.
6. Improves audit planning and risk assessment through predictive and data-driven insights.
7. Strengthens the quality and timeliness of audit evidence, working papers, findings, and management reports.
8. Improves collaboration between internal audit, finance, IT, cybersecurity, risk, compliance, and management teams.
9. Supports stronger governance, risk management, internal controls, and regulatory compliance.
10. Builds sustainable organizational capacity for responsible AI adoption, digital auditing, and future-ready assurance.
The course is designed for Chief Audit Executives (CAEs), Internal Auditors, Audit Managers, Senior Auditors, IT Auditors, Information Systems Auditors, Risk Managers, Compliance Officers, Accountants, Financial Controllers, Forensic Accountants, Fraud Examiners, Data Analysts, Financial Analysts, Business Analysts, Internal Control Specialists, Governance Professionals, Enterprise Risk Management specialists, ERP professionals, and managers responsible for audit, assurance, risk management, compliance, financial controls, data analytics, and digital transformation.
1. Introduction to Artificial Intelligence, machine learning, generative AI, intelligent automation, and AI-enabled internal auditing.
2. Evolution of internal audit from traditional manual auditing to data-driven, automated, and AI-powered assurance.
3. Applications of AI across audit planning, risk assessment, control testing, substantive testing, fraud detection, and audit reporting.
4. Identifying repetitive, data-intensive, high-volume, and judgment-supported audit processes suitable for AI enhancement.
5. AI capabilities, limitations, human oversight, professional judgment, audit evidence, and responsible AI adoption.
6. General Case Study: Assessing a traditional internal audit function and identifying priority opportunities for AI-powered audit transformation.
1. AI-assisted audit data analytics, data preparation, data profiling, anomaly detection, and intelligent transaction analysis.
2. Analysing general ledger, accounts payable, accounts receivable, payroll, procurement, revenue, expenses, and journal entry data.
3. Machine learning applications for risk scoring, pattern recognition, outlier detection, classification, and predictive audit analytics.
4. AI-supported audit risk assessment, emerging risk identification, risk prioritization, and audit universe analysis.
5. Developing AI-enabled audit selection models, risk indicators, thresholds, alerts, and continuous risk monitoring.
6. General Case Study: Using AI-assisted analytics to assess a large transaction population and identify high-risk areas for the internal audit plan.
1. AI-powered fraud analytics, anomaly detection, suspicious transaction identification, and financial crime indicators.
2. Detecting duplicate payments, fictitious suppliers, ghost employees, unusual journal entries, unauthorized transactions, and procurement irregularities.
3. Machine learning approaches for identifying unusual patterns and improving fraud risk detection.
4. AI-enabled continuous auditing, continuous control monitoring, automated testing, alerts, and exception management.
5. Investigating AI-generated exceptions, validating findings, reducing false positives, and documenting audit evidence.
6. General Case Study: Implementing an AI-supported continuous monitoring process to detect suspicious payments, supplier anomalies, and segregation-of-duties conflicts.
1. Applications of generative AI in internal audit planning, audit programmes, working papers, summaries, and report preparation.
2. Natural language processing for analysing policies, contracts, regulations, meeting minutes, emails, complaints, and other unstructured data.
3. AI-assisted contract review, compliance testing, policy analysis, obligation identification, and exception detection.
4. Using generative AI to support audit interviews, issue summaries, recommendation development, management responses, and executive reporting.
5. Prompt design, output validation, hallucination risks, source verification, confidentiality, and human review of AI-generated audit content.
6. General Case Study: Using generative AI and natural language analysis to review organizational policies and contracts and identify compliance gaps requiring audit investigation.
1. AI governance frameworks, accountability, model governance, human oversight, and responsible AI principles.
2. Data quality, data privacy, cybersecurity, access controls, confidentiality, and protection of sensitive audit information.
3. AI model risks including bias, explainability, reliability, hallucinations, model drift, false positives, and inappropriate automation.
4. Auditing AI systems, assessing AI controls, model validation, algorithmic risk, data lineage, and AI-related compliance.
5. Developing internal audit procedures for reviewing AI governance, AI-enabled business processes, and automated decision-making systems.
6. General Case Study: Conducting an internal audit of an organization's AI-enabled credit or fraud detection system and identifying governance, data, model, and control risks.
1. Developing an AI internal audit strategy, transformation roadmap, governance model, priorities, resources, and implementation milestones.
2. Selecting AI and audit analytics technologies based on functionality, security, integration, scalability, cost, and audit requirements.
3. Integrating AI with ERP systems, accounting platforms, Power BI, audit management systems, databases, and enterprise data environments.
4. Building AI-enabled audit workflows for planning, testing, continuous monitoring, evidence analysis, reporting, and follow-up.
5. Measuring AI audit effectiveness through audit coverage, productivity, risk reduction, exception rates, quality, and assurance outcomes.
6. General Case Study: Developing an end-to-end AI internal audit transformation roadmap covering risk assessment, audit analytics, fraud detection, continuous monitoring, reporting, governance, and staff capability development.
1. Customized Training: All our courses can be tailored to meet the specific needs of participants.
2. Language Proficiency: Participants should have a good command of the English language.
3. Comprehensive Learning: Our training includes well-structured presentations, practical exercises, web-based tutorials, and collaborative group work. Our facilitators are seasoned experts with over a decade of experience.
4. Certification: Upon successful completion of training, participants will receive a certificate from Foscore Development Center (FDC-K).
5. Training Locations: Training sessions are conducted at Foscore Development Center (FDC-K) centers. We also offer options for in-house and online training, customized to the client's schedule.
6. Flexible Duration: Course durations are adaptable, and content can be adjusted to fit the required number of days.
7. Onsite Training Inclusions: The course fee for onsite training covers facilitation, training materials, two coffee breaks, a buffet lunch, and a Certificate of Successful Completion. Participants are responsible for their travel expenses, airport transfers, visa applications, dinners, health/accident insurance, and personal expenses.
8. Additional Services: Accommodation, pickup services, flight booking, and visa processing arrangements are available upon request at discounted rates.
9. Equipment: Tablets and laptops can be provided to participants at an additional cost.
10. Post-Training Support: We offer one year of free consultation and coaching after the course.
11. Group Discounts: Register as a group of more than two and enjoy a discount ranging from 10% to 50%.
12. Payment Terms: Payment should be made before the commencement of the training or as mutually agreed upon, to the Foscore Development Center account. This ensures better preparation for your training.
13. Contact Us: For any inquiries, please reach out to us at training@fdc-k.org or call us at +254712260031.
14. Website: Visit our website at www.fdc-k.org for more information.