Data Analytics for Tax Compliance Risk Training Course

Course Overview

Learning Objectives

• Explain how compliance risk management uses data to identify, assess, and treat taxpayer risks.
• Design a taxpayer data model linking returns, payments, e-invoices, and third-party sources.
• Assess data quality and resolve taxpayer identities with exact and fuzzy matching.
• Estimate compliance gaps using top-down and bottom-up methods.
• Build rule-based risk indicators and combine them into transparent compliance risk scores.
• Develop and validate statistical and machine learning models that predict noncompliance.
• Apply anomaly detection and network analysis to uncover invoice fraud and related-party schemes.
• Match returns against e-invoice, withholding, bank, and customs data to quantify discrepancies.
• Select audit cases that balance expected yield, risk coverage, and available audit capacity.
• Design dashboards that track compliance, audit outcomes, and model performance.
• Establish model governance covering explainability, confidentiality, and taxpayer rights.

Who Should Attend

Learning Outcomes

• By the end of this course, participants will be able to:
• Explain how data analytics supports each stage of the compliance risk management cycle.
• Map taxpayer and third-party data sources to specific compliance risks.
• Link records across data sources and measure match rates and data quality.
• Estimate compliance gaps by tax type, taxpayer segment, and obligation.
• Construct risk indicators and combine them into a defensible, transparent risk score.
• Develop, validate, and compare predictive models for noncompliance.
• Identify invoice fraud and related-party patterns through data matching and network analysis.
• Quantify discrepancies between declarations and e-invoice, bank, and customs data.
• Prioritize audit cases against expected yield and available audit capacity.
• Design dashboards that report compliance results, audit outcomes, and model performance.
• Propose governance arrangements that keep risk models explainable, fair, and confidential.

Course Outline