Upon completion of this course, participants will be able to:

• Develop a strategic framework for applying data analytics across the entire compliance lifecycle.
• Identify, clean, and integrate diverse data sources for compliance risk modeling.
• Apply statistical techniques (e.g., regression, clustering) to build risk-scoring models.
• Utilize data visualization tools to communicate compliance risks and trends effectively.
• Design targeted campaigns for fraud detection (e.g., VAT fraud, income underreporting).
• Apply principles of machine learning to predict taxpayer non-compliance behavior.
• Evaluate the ethical and legal implications of using advanced analytics in tax enforcement.
• Measure the return on investment (ROI) and effectiveness of data-driven compliance interventions.

• Data Scientists and Statisticians in Revenue Agencies
• Tax Auditors and Investigators (Specialized Units)
• Managers of Compliance Risk and Intelligence Units
• IT Professionals supporting Data Analytics Platforms
• Forensic Accountants and Fraud Examiners
• Policy Analysts focused on Compliance Strategy

• Hands-on lab sessions with anonymized data using analytical tools (e.g., Python/R snippets, specialized tax software).
• Group project: building and validating a risk-scoring model for VAT non-compliance.
• Case studies on the application of network analysis in fraud detection.
• Workshop on designing a risk-monitoring dashboard.
• Debate on the ethical implications of using predictive policing techniques in tax.

Unit 1: Analytics Strategy and Data Foundation

• Moving from descriptive to predictive and prescriptive analytics
• Mapping analytics use cases across the compliance continuum (prevent, detect, respond)
• Institutional requirements for a high-performing analytics function
• The Analytics Pipeline: from data source to compliance action

• Identifying internal data sources (e-filings, payments, audit history)
• Integrating third-party data (banks, customs, land registries) and external data (social media)
• Data cleaning, standardization, and quality control (ETL processes)

Unit 2: Risk Scoring and Predictive Modeling

• Developing a multi-factor risk-scoring index for audit selection
• Applying regression models to identify significant non-compliance variables
• Using cluster analysis and segmentation to identify homogeneous risk groups
• Model validation and managing Type I (false positive) and Type II (false negative) errors

• ML algorithms for compliance: decision trees, random forests, and neural networks
• Supervised vs. unsupervised learning in fraud detection
• The use of ML in detecting tax evasion networks and complex schemes

Unit 3: Fraud Detection and Targeted Campaigns

• Techniques for detecting organized VAT fraud (Missing Trader Intra-Community)
• Identifying income underreporting and hidden wealth indicators
• Using network analysis to uncover fraudulent relationships between taxpayers

• Designing and launching data-driven compliance campaigns for specific risks
• Applying behavioral insights (nudges) based on analytical findings
• Automating low-risk compliance checks using data matching algorithms

Unit 4: Data Visualization and Communication

• Principles of visual analytics for tax compliance monitoring
• Designing dashboards for real-time risk monitoring (Compliance Command Center)
• Communicating complex analytical findings to non-technical policymakers

• Ensuring fairness, avoiding bias, and maintaining transparency in ML models
• Adhering to privacy laws and data protection regulations (e.g., GDPR)
• The legal framework for using intelligence gathered from data analytics in enforcement.

Unit 5: Operationalizing and Measuring Impact

• Integrating risk scores and analytical outputs into the audit selection system
• Establishing a feedback loop between auditors, analysts, and system developers

• Key performance indicators (KPIs) for the analytics function (e.g., audit yield, detection rate)
• Attributing revenue impact directly to analytical interventions