Pensions and Retirement Training Course on Data Analytics for Pension Operations

Module 1: Introduction to Data Analytics in Pension Operations
Role of data analytics in decision-making and policy formulation
Overview of pension data sources and system architecture
Data-driven governance and transparency in pensions
Key challenges in data collection, processing, and utilization
Case study: How analytics improved member retention in a DC plan

Module 2: Pension Data Types and Management Frameworks
Understanding member, contribution, and benefits data
Structuring and cleaning datasets for analysis
Data lifecycle management and data governance principles
Integrating data from payroll, HR, and fund administration systems
Practical exercise: Designing a pension data dictionary

Module 3: Descriptive Analytics and Performance Measurement
Using descriptive statistics to summarize pension operations
Measuring administrative efficiency, contribution timeliness, and claim turnaround
Developing and interpreting pension KPIs and metrics
Benchmarking fund performance and administrative productivity
Workshop: Building a pension operations dashboard

Module 4: Diagnostic Analytics – Identifying Trends and Anomalies
Root-cause analysis of operational inefficiencies
Detecting anomalies in contributions, withdrawals, and benefit payments
Data visualization for pattern recognition and variance analysis
Correlation and trend analysis across datasets
Simulation: Identifying compliance gaps from real pension data

Module 5: Predictive Analytics in Pension Planning
Forecasting contribution inflows and benefit outflows
Modeling demographic changes and longevity risk impacts
Predicting member withdrawals and contribution lapses
Using regression and time-series models for pension forecasting
Hands-on: Building a predictive contribution forecast model

Module 6: Prescriptive Analytics and Decision Optimization
Moving from analysis to action: recommendations from data
Scenario planning using “what-if” modeling
Optimization for resource allocation and administrative planning
Decision trees and Monte Carlo simulations in pension strategy
Case study: Applying prescriptive analytics to reduce processing delays

Module 7: Data Visualization and Dashboard Design
Visualization principles for pension analytics reporting
Building interactive dashboards with BI tools (Power BI, Tableau, etc.)
Effective visual communication for executives and trustees
Designing scorecards for performance tracking and compliance monitoring
Practical: Creating a visual pension performance report

Module 8: Data Analytics for Risk Management and Compliance
Using analytics to detect fraud, errors, and non-compliance
Risk scoring models for employers and administrators
Integration of audit and compliance dashboards
Linking analytics to ESG and regulatory reporting requirements
Exercise: Building a risk-based compliance monitoring dashboard

Module 9: Advanced Tools, Automation, and Artificial Intelligence
Machine learning and AI applications in pension operations
Automating data workflows and real-time analytics
Leveraging APIs and robotic process automation (RPA)
Big data infrastructure and cloud-based pension analytics
Demonstration: Automating contribution verification using analytics

Module 10: Implementing a Data Analytics Strategy in Pension Institutions
Building data analytics capabilities and culture
Aligning analytics projects with organizational strategy
Developing governance, privacy, and ethics frameworks
Measuring ROI of analytics initiatives
Group project: Designing a data analytics roadmap for a pension fund