Centram Centre for Applied Artificial Intelligence

AI & MACHINE LEARNING APPLICATION FOR RETIREMENT FUND REGULATION

AI & Machine Learning Applications for Retirement Fund Regulation course is designed for regulators, compliance officers, pension fund managers, actuarial professionals, and data teams working with retirement systems.

Course title

AI & Machine Learning Applications for Retirement Fund Regulation

Course description

This course explains how AI and machine learning can be applied responsibly to problems in retirement fund administration, supervision, regulation, and oversight. Topics include predictive modeling (contributions, lapses, longevity), fraud and AML detection, portfolio analytics, stress testing and scenario generation, model governance, explainability, fairness, privacy, and operationalizing models (MLOps). Emphasis is on regulatory risks and controls, documentation and model auditability, and practical hands‑on exercises using common tools.

Target audience

Regulatory staff, compliance officers, pension administrators, actuarial analysts, data scientists in retirement sector, policy makers.

Learning objectives

By the end of the course, participants will be able to:

  1. Identify AI/ML use cases and value for retirement funds and regulators 
  2. Build and evaluate basic predictive models for pension use cases (forecasting contributions, predicting forfeitures/lapses, identifying anomalies) 
  3. Understand and apply explainability, fairness, privacy techniques and regulatory controls to models 
  4. Design and assess model governance, validation, monitoring, and documentation practices for regulated retirement funds 
  5. Recommend implementation roadmaps that balance innovation and supervisory safeguards

Course duration and Delivery format

4 weeks intensive physical training OR 2 weeks physical Training and 3 weeks online training

Recommended tools & libraries (for labs)

  1. Python, Jupyter notebooks 
  2. Pandas, scikit-learn, XGBoost, LightGBM 
  3. TensorFlow or PyTorch
  4.  SHAP, LIME, ELI5 for explainability
  5. Fairlearn or AIF360 for fairness testing 
  6. MLflow or DVC for model tracking; Docker for reproducible deployments 
  7. SQL for data querying; basic BI tools (PowerBI/Tableau) for visualization

 Course Content

 Introduction to AI/ML for regulated environments

  1.  Supervised vs unsupervised vs reinforcement approaches 
  2. End‑to‑end ML lifecycle (data → model → deploy → monitor) 
  3. Case studies: predictive contributions, lapse forecasting, fraud detection, investment analytics
  4. Analytics lifecycle & maturity model- Four analytics levels: descriptive, diagnostic, predictive, prescriptive 
  5. End-to-end lifecycle: problem definition, data, modeling, deployment, monitoring, feedback 
  6. Practical: map an agency process to an analytics maturity path

Data engineering & governance for retirement funds

  1. Data quality, lineage, master data management, typical data schemas for pension records 
  2. Data anonymization, de‑identification techniques, provenance documentation 
  3. Practical: exploratory data analysis on a synthetic pension dataset

Forecasting & time series for contribution and cash flow modelling

  1. Time series basics, ARIMA, exponential smoothing, state space models, and ML alternatives (XGBoost, LSTM) 
  2. Forecasting contributions, cash flows, reserves and stress scenarios 
  3. Lab: build and evaluate a contribution forecast model

Member risk models: longevity, lapse, and claims

  1. Survival analysis, Cox models, parametric survival, competing risks 
  2. Predicting lapses/forfeitures and early exit; segmentation with clustering 
  3. Lab: survival modelling and subgroup analysis

Fraud, AML, and anomaly detection

  1. Patterns of fraud in pension administration (ghost employees, false claims, contribution circumvention) 
  2. Unsupervised anomaly detection: isolation forest, autoencoders, density-based methods, graph analytics for networked fraud 
  3. Lab: anomaly detection on transaction data; incident triage workflow

Investment analytics & portfolio ML

  1. Factor models, risk premia discovery, clustering of assets, ML for asset allocation and risk forecasting (volatility models)
  2. Stress testing and scenario generation using generative models 
  3. Lab: scenario generation and portfolio stress test prototype

Explainability, transparency and fairness

  1. Why explainability matters in pensions (decisions affecting benefits)
  2. Local/global explainability (SHAP, LIME), rule extraction, counterfactuals 
  3. Fairness concepts (statistical parity, equal opportunity), bias sources and mitigation strategies 
  4. Lab: apply SHAP and fairness checks to a benefit eligibility model

Privacy, security and data-sharing techniques

  1. GDPR and equivalent privacy principles, data minimization for regulators 
  2. Differential privacy, federated learning, secure multi‑party computation (SMPC) for cross‑institutional analytics 
  3. Practical considerations: de‑identified supervisory datasets, secure enclaves

Model governance, validation and regulatory compliance

  1. Model risk management frameworks adapted to retirement funds (model inventory, tiering, validation playbook)
  2. Documentation standards (data dictionary, model cards, decision logs) and audit trails 
  3. Practical: create a model governance checklist and model card for a sample model

MLOps, monitoring and incident response

  1. Deployment patterns, CI/CD for ML, model registries, drift detection, alerting and KPI dashboards
  2. Incident response and human-in-the-loop processes for model failures 
  3. Lab: simple pipeline with model monitoring simulation

Supervisory uses of AI: automated surveillance, prioritization and policy design

  1.  How regulators can use ML to supervise (risk scoring funds, early warning systems, audit selection) 
  2. Monitoring asset concentration, liquidity risk, counterparty exposures, and performance benchmarking 
  3. Scenario and stress testing fundamentals; integrating macroeconomic scenarios with fund cashflow models 
  4. Lab: design a stress test and supervisory thresholds
  5. Limitations and risks: opacity, gaming, systemic concentration risk 
  6. Workshop: design a regulator dashboard and surveillance workflow

Capstone presentations, ethics and next steps

  1. Participant project presentations (end-to-end: problem, data, model, governance plan, risks) 
  2. Policy implications, procurement and vendor management, vendor risk assessment for AI solutions 
  3. Final exam or peer review and course wrap-up

Assessments & assignments

  1. Weekly labs (practical notebooks) — 40% 
  2. Midterm assignment: build & validate a predictive model with explainability report — 20% 
  3. Capstone project: team project with model, governance documentation, and presentation — 30% 
  4. Participation/short quizzes — 10%

Capstone project ideas

  1. Early-lapse prediction and intervention optimization for defined contribution plans 
  2. Anomaly detection system for pension disbursement fraud (prototype & triage flow) 
  3. Contribution forecasting and cash-flow stress testing under macro scenarios 
  4. Model selection and governance playbook for vendor-supplied actuarial models 
  5. Federated analytics design: cross-fund longevity modeling while preserving privacy

Evaluation criteria for models (example rubric)

  1. Predictive performance (AUC, RMSE as applicable) — 25%
  2. Robustness and validation (backtesting, stress tests) — 20% 
  3. Explainability and fairness analysis — 20% 
  4. Data governance and documentation quality — 20% 
  5. Operationalization plan and risk controls — 15%

Recommended readings & resources

  1. “Interpretable Machine Learning” — Christoph Molnar (online) 
  2.  “AI in Finance/Regulation” whitepapers from BIS, IMF, OECD (search relevant guidance) 
  3. Papers on fairness, explainability and differential privacy (AIF360 docs, Google DP library) 
  4. Regulatory model risk guidance: e.g., SR 11-7 (US OCC) as a conceptual template (adapt to local jurisdiction) 

Tool docs:

  1. scikit-learn, SHAP, Fairlearn, MLflow

Regulatory & ethical checklist

  1. Purpose/benefit of model clearly defined and documented 
  2. Data sources authorized and privacy-assessed 
  3. Model tiering and risk classification (high/medium/low) 
  4. Validation plan (pre-deployment), periodic review schedule 
  5. Explainability for stakeholders and impacted members 
  6. Fairness testing and mitigation documented 
  7. Logging, audit trails, and human-in-the-loop safeguards
  8. Incident response & rollback criteria 
  9. Vendor model review procedures and contractual SLAs