Deep Learning for Financial Decision-Making at Scale: Causality, Synthetic Data, and Dynamic Risk Monitoring

As computers get more powerful and more data becomes available, machine learning has become more popular. However most machine learning techniques are good at finding correlations in data, but not causal relationships. Causal machine learning techniques try to tackle this problem. The goal of this PhD project is to investigate how causal machine learning can be used in the context of finance and insurance. A possible application would be to find causal relationships in insurance data.