Engineer features from tax return, payment history and third-party data that predict audit-worthy non-compliance.
Digital Transformation & Artificial Intelligence
Risk-Based Audit Case Selection with Machine Learning in Tax Authorities
Apply machine learning to rank taxpayer risk and target audit resources where non-compliance is most likely, while keeping selection defensible and fair.
🗓️Duration5 training days
📚Content4 modules · 8 sessions
🎓On completionAccredited attendance certificate
About the programme
Course Overview
Tax authorities can never audit every return, so the quality of case selection determines how much non-compliance is caught and how much audit effort is wasted on compliant taxpayers. Machine learning models trained on historical audit outcomes can rank returns by likelihood of material error or evasion far more consistently than manual rule sets, but they introduce new risks: models can encode past selection bias, become opaque to auditors who must justify a case, and attract legal challenge if a taxpayer believes they were targeted unfairly. This course teaches tax administration analysts and auditors to build, validate and govern a risk-based case selection model from historical audit and return data, test it for accuracy and for disparate impact across taxpayer groups, and integrate its output into an audit workflow where human auditors retain final judgement. Sessions include feature engineering from return and third-party data, model evaluation against realised audit yield, and the documentation needed to defend a selection decision under legal or parliamentary scrutiny. Participants leave with a working case-selection model, a fairness testing protocol, and a governance plan for keeping the model accurate as taxpayer behaviour and tax law change.
Expected Learning Outcomes
Train and validate a risk-scoring model against realised audit outcomes rather than proxy measures alone.
Test a case-selection model for disparate impact across taxpayer segments before it is deployed.
Integrate model risk scores into an audit workflow where auditors retain discretion over final case selection.
Document model logic and evidence in a form that withstands legal challenge or parliamentary scrutiny.
Monitor model performance and audit yield over time to detect drift as taxpayer behaviour changes.
Balance random and risk-based selection to preserve deterrence value and unbiased compliance measurement.
Who Should Attend
Data scientists and analysts working within a tax or revenue authority's compliance function.
Audit selection managers responsible for allocating limited audit resources across taxpayer segments.
Compliance risk officers accountable for the fairness and legal defensibility of selection methods.
IT teams integrating a risk-scoring model into existing case management systems.
Legal and policy advisors who must defend audit selection methods to courts or oversight bodies.
Senior tax administration leaders setting strategy for data-driven compliance programmes.
Course Modules
Select any module to see its sessions and points.
01Framing Audit Case Selection as a Risk-Scoring Problem
2 sessions · 8 points
Session 1Defining the Prediction Target and Available Data
- Define the outcome a model should predict, such as material adjustment amount, rather than audit selection itself.
- Inventory return, payment history and third-party data sources available for building a risk-scoring model.
- Identify data quality issues, such as inconsistent industry codes, that would undermine model reliability.
- Separate historical audit outcomes driven by past selection bias from genuine indicators of non-compliance.
Session 2Engineering Predictive Features from Tax and Third-Party Data
- Derive features from return line items, filing history and payment behaviour that correlate with past adjustments.
- Incorporate third-party data, such as property records or trade data, to flag inconsistencies with declared income.
- Build network features that link related entities to detect coordinated evasion schemes.
- Avoid features that act as proxies for protected characteristics even when not explicitly included in the model.
02Building and Validating the Risk-Scoring Model
2 sessions · 8 points
Session 1Model Selection, Training and Performance Evaluation
- Compare interpretable models against more complex machine learning models for the accuracy and explainability trade-off required.
- Validate model accuracy against realised audit yield using a held-out sample rather than training data alone.
- Set a threshold or ranking cut-off that matches available audit capacity to model-identified risk.
- Compare model-based selection against the prior manual or rule-based method on yield per audit hour.
Session 2Testing for Fairness and Disparate Impact
- Test model outputs for disparate selection rates across taxpayer segments defined by legally relevant characteristics.
- Distinguish a fairness concern caused by genuine risk differences from one caused by biased training data.
- Apply mitigation techniques, such as reweighting or threshold adjustment, when disparate impact is found.
- Document the fairness testing process so it can be reproduced and defended if challenged.
03Integrating Model Output into the Audit Workflow
2 sessions · 8 points
Session 1Combining Model Scores with Auditor Judgement
- Design a workflow where model risk scores inform, rather than replace, auditor case selection decisions.
- Present risk scores alongside the underlying factors that drove them so auditors can sense-check each case.
- Preserve a random selection stream alongside risk-based selection to measure true underlying compliance rates.
- Capture auditor overrides of model recommendations to identify where the model is systematically wrong.
Session 2Case Management System Integration and Auditor Training
- Integrate risk scores into the case management system auditors already use for scheduling and tracking.
- Train auditors to interpret model output correctly, including its confidence and known limitations.
- Establish escalation routes for cases where an auditor disputes a model's risk assessment.
- Pilot the integrated workflow with a subset of auditors before organisation-wide rollout.
04Governance, Legal Defensibility and Continuous Monitoring
2 sessions · 8 points
Session 1Documenting the Model for Legal and Oversight Scrutiny
- Prepare model documentation covering data sources, features, validation results and fairness testing for oversight review.
- Prepare a plain-language explanation of case selection logic suitable for a taxpayer appeal or tribunal hearing.
- Coordinate with legal counsel to confirm the model's use aligns with the authority's statutory audit powers.
- Establish a record-keeping policy that preserves model versions used for each historical selection decision.
Session 2Monitoring Drift and Sustaining Model Accuracy
- Track model accuracy and audit yield on a rolling basis to detect drift as taxpayer behaviour changes.
- Retrain or recalibrate the model on a fixed schedule using the most recent audit outcomes.
- Review model features periodically for continued relevance as tax law and reporting requirements change.
- Report model performance and fairness metrics to compliance leadership and oversight bodies on a regular cycle.
What the participant receives
4 course modules
A structured syllabus
8 training sessions
across 5 days
32 detailed points
Applied, detailed content
Accredited attendance certificate
On completing the programme
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