EAA Web Session: "Machine Learning Finance for Pension Funds with Examples"

Einleitung/Dauer

In general, Machine Learning (ML) is the study of algorithms that improve through experience. These algorithms or models can make systematic, repeatable, validated decisions based on historical data. ML has come a long way in recent years, which is reflected in the methods available for time series forecasting (they are also important for assessing parameters for different kinds of liability provisions).

Therefore, this type of analysis can help actuaries and members of pension fund boards of trustees to accurately assess different kinds of pension fund parameters for assets and liabilities and to prepare any kind of forecasts. Visualizing the evolution of pension fund parameters and forecasting them will help the board of trustees explain how to adjust them in the actuarial provision or what to expect in their future evolution.

For this workshop, several examples for analyzing and providing such assumptions will be prepared and explained. Many useful visualization techniques will be presented with practical examples (via Python).

Vorgehensweise und Ziele

The annual financial statement of a pension fund shows all very important parameters of the liabilities as well as all types of reserves. Machine Learning Finance helps to verify all levels of reserves and to prepare the annual financial statement presentation for the members of the Board of Trustees - this helps them to make their final decisions.

Teilnehmer

The web session is suited for pension fund actuaries and actuarial professionals, IT-developers of pension fund software tools that are directly or indirectly involved in actuarial and investment consulting for pension funds and collective foundations with occupational provisions. Additionally, these topics could be useful for members of pension fund board of trustees, pension fund managers, and pension fund auditors.

Technical Requirements
Please check with your IT department if your firewall and computer settings support web session participation (the programme Zoom will be used for this online training). Please also make sure to join the web session with a stable internet connection.

Dozierende

Dr Ljudmila Bertschi
Ljudmila is a qualified member of the Swiss actuarial association (SAV/SAA) and an accredited pension actuary of the Swiss chamber of pension fund experts (SKPE). She has a PhD in phys.-math. from the MSU and has worked in pension fund consulting for about 20 years in different Swiss and international consulting firms and insurance companies. She conducted a research study for the Federal Office of Social Security (2015), prepared many publications and presentations for international conferences as well as made training presentations for Swiss chamber of pension fund experts (liability forecasting with Markov chains incl.).

Dr Mauro Triulzi
Mauro is a qualified member of the Swiss actuarial association (SAV) and has a Dr. math. ETHZ. He has worked for about 20 years as a developer of actuarial tools and implemented the nested stochastic modelling for pension fund liabilities including mortality rates for ALM studies. Currently he develops different actuarial tools for local and international accounting valuations as well as pension fund administration services. He prepared presentations for international conferences together with Ljudmila.

Sprache/Kurztitel

The language of the web session will be English.