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Deep Learning supports Solvency Requirements for Pension Funds and ensures the Guarantee of Benefit Payments at any time. It is particularly well-suited for time series forecasting related to inflation, yield curves and asset allocation returns in order to prepare and review these. We use the Long short-term memory (LSTM) method, a type of recurrent neural network (RNN), as well as other neural networks.
Deep learning is a type of machine learning that uses multi-layered neural networks. The libraries programmed with Python are fascinating areas of research because they help to verify time series forecasts and to understand how long it might take for the pension fund to reach the target value of the investment fluctuation reserve based on the current situation.
RNN-based models, particularly LSTMs, are increasingly being used to capture complex spatio–temporal dependencies, while hybrid architectures combine convolutional and recurrent components (i.e., CNN-LSTM). Researchers have developed hybrid models that further improve prediction accuracy, which is very important for financial forecasting.
The web session is suited for pension fund actuaries and actuarial professionals, IT-developers of pension fund software tools who 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 as well to members of the pension fund board of trustees, pension fund managers, and pension fund auditors.
Early-bird discount is available for bookings made by 18 May 2026.
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