AI-Powered Fraud Detection & Audit Analytics
Overview
Fraud schemes are increasingly sophisticated, and traditional sample-based checks miss too much. This course shows auditors and fraud professionals how to use data analytics and AI to test full populations, detect anomalies, prioritise investigations and strengthen continuous monitoring.
Who should attend
Internal and external auditors, fraud investigators, compliance officers, and risk professionals.
Training methodology
Interactive presentations, live demonstrations of AI tools, hands-on exercises using participants' own work examples, case studies, group discussion, and a personal action plan.
Objectives
By the end of this course, participants will be able to:
- Apply analytics to full transaction populations
- Use AI to detect anomalies and fraud patterns
- Design continuous monitoring routines
- Prioritise and document investigation leads
- Understand the limits and evidential value of AI findings
Course outline
Day 1Analytics in Audit and Fraud
- From sampling to full-population testing
- Common fraud schemes
- Data sources for detection
- Analytics maturity
- Fraud triangle and red flags
- Legal considerations
Day 2Detection Techniques
- Rule-based tests
- Statistical red flags
- Duplicate and split transactions
- Hands-on tests
- Benford's law
- Vendor and payroll tests
Day 3AI and Machine Learning
- Anomaly detection models
- Text analytics on emails and documents
- Network analysis of relationships
- Case study
- Generative AI for audit work papers
- Model limitations
Day 4Continuous Monitoring
- Designing monitoring dashboards
- Thresholds and alerts
- Reducing false positives
- Workshop
- Integrating with risk assessment
- Roles of audit and management
Day 5From Findings to Action
- Prioritising leads
- Documentation and evidence
- Reporting to management
- Action plan
- Interviewing and follow-up
- Building an analytics capability
