The goal of this PhD project is to automate the learning of a decision model by new combinations of statistical and knowledge based models applied to fraud detection and in high energy physics. Real time decision making combines today’s analytics and knowledge based models for fraud detection, notably in banking. Payment platforms detect in real time fraudulent transactions by combining recognition of human created patterns articulated on their banking symbolic knowledge model, and predictive models run to discover emerging fraud patterns by detecting new trends and anomalies from the data. In this thesis the student will work on new combinations of statistical and knowledge based models for a better decision automation in fraud detection and in high energy physics, for the recognition of human-created (fraud) and non-human-created (particle collision) patterns. While machine learning has been highly popular during the last years, their black box approach raises interpretability and explainability challenges. On the other hand, symbolic models, including rules, have been successful in making decisions more interpretable. Nevertheless they require to capture an existing knowledge or theory. In the context of real time decision automation, the student involved in this project will test the proposed numeric to symbolic model inferences to detect anomalies, patterns and anti-patterns, combining the efficiency of the numerical machine learning and the explainability of the symbolic approach. They will inject theory (knowledge from the Standard Model in physics, fraud detection patterns in financial transactions) and combine it with predictive models to classify observations and add an interpretability layer. ESR2 will explore different angles in how we intend to combine numerical and knowledge models.
Host country: France
Host beneficiary: IBM France
PhD-awarding institution: Sorbonne
Planned collaborations: CNRS