Payment platforms such as Adyen use technology to detect fraud effectively. This is a challenging task, as both legitimate and fraudulent customer behaviour are constantly evolving and vary across markets. Machine learning plays a key role in this process, but existing methods are susceptible to learning spurious correlations. This project aims to develop machine learning techniques inspired by causal reasoning in order to improve the robustness of fraud detection under changing data conditions.
