Centram Centre for Applied Artificial Intelligence

AI & MACHINE LEARNING COURSE FOR TAX ADMINSTRATION

Course Title

AI & Machine Learning for Tax Administration

Target Audience: Tax Policy/Administration Officer, Tax Analysts, Tax Compliance Officers

Course description

Practical introduction to AI/ML methods applied to tax administration problems: compliance risk scoring, fraud and evasion detection, taxpayer segmentation and service personalization, revenue forecasting, document processing, and policy evaluation. Covers data management, model development, interpretability, legal/ethical constraints, deployment, and monitoring in public-sector settings.

Learning objectives

  1. Translate tax administration problems into ML tasks and select appropriate methods.
  2. Acquire, clean, link, and protect tax-related data for modeling.
  3. Build, evaluate, and interpret models for compliance scoring, anomaly detection, forecasting, and NLP tasks.
  4. Understand legal, ethical, privacy and operational constraints in government ML systems.
  5. Deploy and monitor models responsibly and design human-in-the-loop workflows.

Assessment

  1. Weekly labs / problem sets: 30%
  2.  Midterm applied project (proposal + interim results): 20%
  3. Final project (report + code + presentation): 40%
  4. Participation / case study discussions: 10%

Software & tools

  1. Python stack: pandas, scikit-learn, xgboost/lightgbm, tensorflow/pytorch (optional), nltk/spacy, transformers, networkx, pyod, SHAP, aif360/fairlearn, MLflow.
  2. Big data / production: Spark, BigQuery, PostgreSQL, Docker, Kubernetes, MLflow/Kubeflow.
  3.  R alternatives: tidyverse, caret, GRF.
  4. Privacy/secure computation: diffprivlib, PySyft (intro).
  5. Public-sector platforms: SAS/H2O if your agency uses them.

Datasets & Data sources

  1. Public stats: IRS SOI tables, national tax statistical releases, World Bank & IMF macro series, customs trade stats.
  2. Simulated/anonymized tax return datasets (recommended for labs).
  3. Open datasets for network/fraud/transaction modeling (credit card fraud datasets, POS logs) for technique practice.
  4. Guidance: Use sanitized or synthetic extracts from agency data for hands-on labs; otherwise use public proxies.

Course content

Introduction: Tax admin challenges & ML opportunities

  1. Topics: Tax workflows, common problems (non-filing, under-reporting, fraud), ML use-cases, success stories and failures, responsible use.
  2. Lab: Environment setup; explore example tax dataset; baseline descriptive analysis.

Data cleaning, record linkage and de-duplication

  1. Topics: Data pipelines, joining records across sources (income, VAT, customs, third-party), entity resolution, feature engineering.
  2. Lab: Deduplicate and link simulated taxpayer records; create features from transaction logs.

Supervised learning for compliance scoring

  1. Topics: Binary classification, class imbalance, sampling strategies, evaluation metrics (precision@k, recall, AUC, lift), calibration.
  2. Lab: Build and evaluate risk scores for audit selection; optimize for precision at top k.

Anomaly detection & unsupervised methods

  1. Topics: Unsupervised anomaly detection, clustering for segmentation, novelty detection, graph-based suspicious network detection.
  2. Lab: Apply isolation forest, autoencoders, and graph methods to detect suspicious filings and networks.

Network analysis & link-based fraud detection

  1. Topics: Network representations of taxpayers/transactions, community detection, link prediction, money flow analysis.
  2. Lab: Construct taxpayer-transaction networks; identify suspicious clusters and influential nodes.

Time series & revenue forecasting

  1. Topics: Aggregate revenue forecasting, seasonal decomposition, ML regressors for forecasting, feature-based and hybrid approaches, evaluation under policy change.
  2. Lab: Forecast monthly tax revenue using classical and ML methods; backtest and scenario analysis.

NLP for documents & automated data extraction

  1.  Topics: OCR, information extraction from forms/invoices, named entity recognition, semantic search, document classification.
  2. Lab: Extract fields from scanned receipts/invoices; classify correspondence and route to correct teams.

Causal inference & evaluation of interventions

  1. Topics: A/B testing, quasi-experimental designs, difference-in-differences, propensity scores, uplift modeling for treatment targeting (e.g., audit vs education).
  2. Lab: Evaluate effect of a reminder campaign on filing/compliance using quasi-experimental methods and uplift models.

Fairness, privacy, governance & legal constraints

  1. Topics: Data protection (GDPR), privacy-preserving methods (differential privacy, secure multiparty computation), fairness and non-discrimination, transparency and appealability.
  2.  Lab: Audit a model for disparate impact; apply simple differential privacy mechanism to aggregate reporting.

 Operationalization, MLOps & human-in-the-loop workflows

  1. Topics: Model deployment, CI/CD for ML, monitoring and drift detection, retraining policies, audit trails, explainability for caseworkers.
  2. Lab: Package a risk-score model; simulate monitoring metrics and alerts; produce explainability outputs for top flagged cases.

Adversarial risks, robustness & red-teaming

  1. Topics: Strategic Behavior by taxpayers, data poisoning risks, robustness testing, secure features, defense strategies.
  2. Lab: Simulate simple strategic manipulations and test robustness of scoring rules.

Presentations, policy implications & future directions

  1. Activities: Final project presentations; discuss integration with business processes, change management, and policy trade-offs.

Labs & practical

  1. Use of realistic synthetic datasets for hands-on work.
  2. Reproducible code notebook + one-page write-up with policy recommendations.
  3. Emphasis on interpretable outputs for auditors and managers, not just predictive metrics.

Final project ideas

  1. Build an audit selection system: risk score, interpretability, simulation of audit outcomes and revenue impact.
  2. NLP pipeline to extract and reconcile invoice data for VAT gap estimation.
  3. Network-based detection of VAT carousel fraud using simulated transactions.
  4. Forecasting model for monthly revenue with scenario analysis under policy change.
  5. Deployment plan for a chatbot to answer taxpayer queries with fall back to human agents and fall-back logging.

Evaluation metrics specific to tax use-cases

  1. Precision@k and lift for audit selection (maximize yield per audit).
  2. Expected revenue uplift (cost-benefit): revenue recovered minus audit cost.
  3. False positive rate and workload implications for caseworkers.
  4. Fairness metrics across protected groups and geographic regions.
  5. Model robustness to manipulation and distributional shift.

Governance, Ethics & Best practices

  1. Clear documented business rules and human-in-the-loop decision points.
  2. Explainability requirements for flagged taxpayers and appeal mechanisms.
  3. Data minimization, retention policies, and secure access controls.
  4. Impact monitoring and periodic audits of model performance and fairness.
  5. Cross-functional review: legal, policy, ethics, and operations.

Selected readings & resources

  1. Varian, H. (2014) — Big Data & econometrics overview.
  2. Mullainathan & Spiess (2017) — ML for social science.
  3. Athey & Imbens — ML and causal inference.
  4. Practical tool docs: scikit-learn, SHAP, AIF360, pyOD.
  5. Gov-tech / public-sector case studies from OECD, World Bank on digital government and data governance.