10 Mar 2021
Machine Learning in Finance and Insurance
Artificial intelligence is currently on everybody’s lips and seems to be vital for industry to be successful at the market. Researchers and practitioners are learning the basic techniques of machine learning to develop new products and improve analyses including forecasting, among others. We are highly interested in improving processes and applications in companies, in particular to make better decisions and developing high-end products. More and more researchers from different disciplines use deep learning techniques to understand and explain phenomena and relationships better. This web session aims at building machine learning techniques with the focus on neural networks and the sound application on several practical problems in finance and insurance. Hereby, we introduce how to implement neural networks in Python, with the goal to put the participants in the position to solve any specific problem of interest.
Organised by the EAA - European Actuarial Academy GmbH.
The web session is open to all interested persons. In particular, this session is not limited to actuaries: Practitioners working in (financial) industry as well as students and researchers with quantitative background are welcomed to join.
Technical requirementsPlease check with your IT department if your firewall and computer settings support web session participation (the programme GotoTraining/GotoWebinar is used for the web session). Please also make sure that you are joining the web session with a stable internet connection.
Purpose and Nature
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.