| October 1, 2020
Project Overview
Payment fraud detection systems were first implemented by manually writing rules. Today, we have powerful automatic learning mechanisms. We can create more sophisticated detection systems. For example, models based on random forests perform very well. These models built by machine learning are also very opaque. Bank operators would like to continue to use rule-based systems for their human readability, but benefit from the contributions of machine learning systems.
The goal of the project will therefore be to evaluate for different learning methods automatic, the ability to retranslate one of their models in the form of rules. This places the subject in the field of neuro-symbolic research with regard to approaches based on neural networks. We will of course also be interested in the possible loss of accuracy of the model, since the size of the resulting set of rules will have to remain within reasonable limits.