Data Science in Occupational Pension Schemes

Overview

This report examines the application of data science in occupational pensions. The study highlights various aspects of integrating data science into occupational pension systems, including the legal framework, potential data sources, and their classification. It focuses on the use of machine learning and data analytics to address challenges such as the lack of retirement provision and the impact of data science on retirement, health, and work performance. Furthermore, the report provides practical examples and proposes a taxonomy for relevant data in occupational pensions, encompassing primary, secondary, and supplementary data sources.

It emphasizes the need for actuaries to balance technical innovations with compliance with regulations such as the GDPR. At the same time, it points out that historical data can be used for predictive analytics and to improve administrative processes. Future applications include optimizing pension fund management, supporting communication between employers and employees, and improving the accuracy of biometric calculation bases.

The results report does not claim to be exhaustive, but provides an introduction to the topic to facilitate further exploration and application of data science in the field of occupational pensions.