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 introducing and building machine learning techniques with the focus on regression and classification problems. We consider the sound application on several practical problems in finance and insurance. Hereby, we introduce how to implement machine learning techniques in Python and execute some group work, 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 welcome to join.

Technical requirements and test session
Please check with your IT department if your firewall and computer settings support web session participations (the programme Zoom is used for this online training). Please also make sure that you are joining the web session with a stable internet connection.
In case you want to run and adapt the provided Python code, please install an Python environment; Jupyter notebooks will be sent out for testing purposes in advance.

The objective of this web session is that participants should become familiar with machine learning techniques 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 several techniques such as neural networks, among others. The key for an efficient application is the way of training machine learning algorithms and thus we focus our attention on this optimization as well. 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. A vital part of this web session is the group work to get familiar with the implementation.

The language of the web session will be English.

Dr Christian Jonen
Christian is leading the internal model validation unit at Generali Deutschland AG. He holds a PhD in mathematical finance and is a member of the German Association of Actuaries (Aktuar DAV). Before his time at Generali, he worked as an IT project manager in the department Change Delivery at HSBC.