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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 annual financial statement of a pension fund shows all important parameters of the liabilities as well as all types of reserves. Deep Learning in Finance helps to forecast for portfolio returns, inflation as well as yield curves and to understand how the investment fluctuation reserve might develop. Based on this analysis, the annual financial statement presentation could be prepared for the members of the Board of Trustees, helping them to make final decisions on the expected benefit payments and to understand how this kind of analysis could be done.
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