The objective of this web session is that participants should become familiar with neural networks used to solve practical problems in finance, banking and insurance. To achieve this we begin from the scratch and introduce machine learning techniques step by step:
To start with, we give an overview of this interesting field with the primary focus on neural networks. Motivated by our way of thinking and the human brain we learn how we are able to construct powerful algorithms to solve several problems. The key for an efficient application is the way of training neural networks and thus we focus our attention on this optimization as well. In a second step we strengthen our learned knowledge by focusing on several case studies: We consider an example within the Solvency II context such as implementing an internal model to calculate the Solvency Capital Requirement (SCR), but also applications to financial market such as option pricing by Monte Carlo methods or trading strategies.
During our complete web session we learn how the introduced algorithms can be implemented so that the participants are able to build up their own use cases in Python at the end.