AI IN AUDITING, FRAUD DETECTION & RISK MANAGEMENT
Course Description
This practical three-day programme equips audit, risk, compliance, finance and control professionals to apply Artificial Intelligence (AI) responsibly in auditing, fraud detection and enterprise risk management. Participants will learn how AI strengthens continuous auditing, identifies unusual transactions and fraud patterns, improves risk assessment, and supports more timely, evidence-based decisions. The programme combines concepts, relevant tools, case exercises and governance considerations.
Training Objectives
At the end of the programme, participants will be able to:
- Understand the role and value of AI in audit, fraud detection and risk management.
- Identify suitable audit and risk processes for AI-enabled improvement.
- Apply AI concepts to transaction testing, anomaly detection and continuous monitoring.
- Recognise common fraud indicators, patterns and behavioural red flags using data.
- Use AI-supported approaches to assess, prioritise and report risks.
- Understand data quality, privacy, ethics and governance requirements.
- Evaluate AI-generated insights critically and retain professional judgement.
- Develop an action plan for implementing AI responsibly within their organisation.
Learning Outcomes
Participants will be able to:
- Explain key AI, machine learning, analytics and generative AI concepts in an audit context.
- Design basic AI-enabled audit tests and continuous-monitoring routines.
- Identify potential fraud anomalies in transactional and operational data.
- Develop risk indicators, dashboards and alerts for emerging risks.
- Use AI tools to support audit planning, documentation and report drafting.
- Assess AI-related risks, including model bias, inaccurate outputs, privacy and cyber risks.
- Establish appropriate human review, controls and governance around AI use.
- Present practical recommendations for AI adoption in audit and risk functions.
COURSE CONTENT
Day One: AI Foundations for Auditing
Understanding AI in the Audit Function
- Evolution from traditional auditing to AI-enabled auditing
- Overview of Artificial Intelligence, machine learning, data analytics and generative AI
- The AI-enabled audit lifecycle
- Benefits of AI for audit quality, speed, coverage and insight
- Identifying high-value AI use cases in internal and external audit
- Data requirements, data quality and data readiness
- Using AI to support audit planning, working papers and report drafting
AI-Enabled Audit Planning and Continuous Auditing
- Risk-based audit planning using data insights
- Moving from sample testing to full-population testing
- Continuous auditing and continuous control monitoring
- Identifying exceptions, unusual trends and control failures
- AI-supported control testing and exception analysis
- Developing effective audit questions and prompts for AI tools
- Maintaining professional judgement when using AI-generated outputs
Practical Exercise
- Mapping the audit process
- Identifying suitable AI opportunities
- Developing an AI-assisted audit plan and exception-testing checklist
Day Two: AI in Fraud Detection and Investigation
AI for Fraud Detection
- Understanding the Fraud Triangle and Fraud Diamond
- Common fraud schemes and red flags
- Transactional, behavioural and operational fraud indicators
- Introduction to anomaly detection and pattern recognition
- Detecting duplicate payments and inflated invoices
- Identifying ghost workers and payroll irregularities
- Detecting procurement fraud, expense fraud and revenue manipulation
- Using AI to prioritise suspicious transactions for review
AI-Supported Investigation and Evidence Review
- Using AI to review emails, invoices, contracts and other documents
- Extracting key information from large volumes of unstructured data
- Link analysis between people, suppliers, transactions and accounts
- Suspicious activity scoring and investigation prioritisation
- Preserving evidence and maintaining investigation integrity
- Documenting findings and investigation decisions
- Understanding the limitations of AI in making fraud allegations
- Human review, corroboration and due process
Practical Exercise
- Case study: identifying fraud indicators from a transaction scenario
- Developing a fraud-risk indicator matrix
- Creating an escalation process for suspicious activities
Day Three: AI in Risk Management, Governance and Implementation
AI in Enterprise Risk Management
- AI-supported risk identification and assessment
- Using AI to analyse emerging, operational and strategic risks
- Risk scoring, heat maps and prioritisation
- Developing Key Risk Indicators (KRIs)
- Predictive risk monitoring and early-warning alerts
- Scenario analysis and stress testing
- AI-enabled risk dashboards and management reporting
- Monitoring cyber, technology, third-party and regulatory risks
AI Governance, Ethics and Implementation Roadmap
- Responsible AI principles for audit and risk functions
- Data privacy, confidentiality and information security
- Managing model bias, inaccurate outputs and hallucinations
- Explainability, transparency and audit trails
- Human oversight and accountability
- AI policies, controls and approval processes
- Change management and staff capability development
- Developing a practical AI adoption roadmap
Capstone Exercise
- Designing an AI-enabled risk dashboard
- Defining KRIs, thresholds and escalation triggers
- Preparing a 90-day responsible AI adoption roadmap for the audit, fraud or risk management function