PhD Researcher in Actuarial Science at UCLouvain
non-life insurance · Bayesian modelling · machine learning · fairness · uncertainty
PhD research at ISBA (UCLouvain) in actuarial science under the supervision of Prof. Donatien Hainaut, focusing on statistical and machine learning methods for non-life insurance. My PhD research was funded through the Excellence of Science (EoS) programme, a joint research funding initiative of the F.R.S.-FNRS (Fédération Wallonie-Bruxelles) and the FWO (Flanders Research Foundation). Thesis committee: Michel Denuit (UCLouvain), Olivier Caelen (UCLouvain), Katrien Antonio (KU Leuven), and Marie-Pier Côté (Université Laval).
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My research investigated how modern statistical and machine learning methods can be used in non-life insurance while maintaining interpretability, actuarial consistency, and appropriate treatment of predictive uncertainty.
Skills developed include:
- Statistical and actuarial modelling: developing models for insurance claim frequency and risk prediction.
- Bayesian modelling: working with probabilistic models and methods for quantifying epistemic and predictive uncertainty.
- Machine learning: implementing, analysing, and comparing machine learning models with classical actuarial approaches.
- Fairness and responsible modelling: investigating how fairness considerations can be incorporated into predictive insurance models and how they affect model behaviour and uncertainty.
- Model interpretability: analysing and communicating model predictions and the influence of individual risk factors.
- Computational research: implementing statistical and machine learning methodologies and conducting empirical studies using real-world and simulated data.
- Academic writing: producing research papers, presenting results at conferences and seminars, and communicating technical findings to academic audiences.
- Research collaboration: working with researchers across statistics, machine learning, and actuarial science.
