Machine Learning for Fraud Detection in Government Auditing

• Understand how machine learning enhances fraud detection in public sector auditing
• Equip auditors and analysts with practical tools for identifying anomalies and high-risk activities
• Strengthen data-driven decision-making and risk assessment processes
• Improve transparency, accountability, and efficiency in government financial oversight

Course Outline

Introduction to Machine Learning in Government Auditing

• Overview of machine learning concepts and relevance to government auditing
• Types of fraud in public sector finance, procurement, and payroll
• Benefits of ML-driven auditing compared to traditional audit methods
• Case examples of government agencies using ML for fraud detection

Data Foundations for Fraud Analytics

• Identifying and preparing audit-relevant datasets
• Data quality, integrity, and preprocessing techniques
• Feature engineering for fraud risk indicators
• Understanding structured vs. unstructured government data

Machine Learning Techniques for Fraud Detection

• Supervised learning models for classification of fraudulent activities
• Unsupervised learning for anomaly and pattern detection
• Predictive analytics for fraud scoring and risk prioritization
• Model evaluation, accuracy, and validation techniques

Building and Deploying Fraud Detection Models

• Steps in developing ML-powered fraud detection systems
• Using dashboards and visualization tools for audit insights
• Integrating ML outputs into audit workflows and reports
• Automating detection pipelines for continuous monitoring

Interpreting Results and Enhancing Audit Decisions

• Turning ML predictions into actionable audit findings
• Identifying false positives and refining detection rules
• Collaborative interpretation between auditors and data scientists
• Communicating insights to oversight bodies and stakeholders

Ethics, Compliance, and Governance in ML Auditing

• Ensuring transparency, fairness, and accountability in ML models
• Data privacy, security, and legal considerations in public auditing
• Avoiding bias and ensuring ethical use of algorithms
• Governance frameworks for sustainable ML implementation