Actuary and member of the Institute of Actuaries in Belgium (IABE), graduated from UCLouvain (MSc in Actuarial Sciences). I combine strong mathematical foundations with a business-oriented mindset to turn complex risk questions into clear, actionable insights.

My areas of work include risk modelling, pricing, technical reserving, and reporting under Solvency II and IFRS 17. I am equally comfortable working on the first line (data-driven pricing, claims and policy analysis, simulations and provisions, KPI reporting) and on the second line (Business Risk & Controls, operational risk, control inventories).

I work with Excel/VBA, Python, R, SQL and Power BI, and I value clear communication: translating technical outputs into decisions that stakeholders whether business teams, management or regulators, can trust and act upon.

Based in Belgium, open to opportunities in actuarial science, risk management and data-driven insurance roles.

When I'm not working with data, I enjoy building personal analytics projects, exploring open datasets to uncover patterns that connect statistics with real-world behaviour. I'm also passionate about strategic board games and probabilistic puzzles, which keep my probabilistic intuition sharp in a playful way. I love sharing knowledge through content creation on actuarial topics, making risk and statistics accessible and engaging for broader audiences. And I still get that "aha!" moment every time data reveals something new and useful.

• Project A:, Tarification stochastique de rentes viagères différées — Modélisation conjointe du risque de taux et de mortalité (corrélation négative) via Monte Carlo, avec recommandations opérationnelles pour assureurs sous Solvabilité II. Python, UCLouvain 2024.
• Project B: Tarification d'un traité XL Motor Third Party Liability — Burning Cost avec clause de stabilité (indexation par paiement), validation Chain-Ladder des charges ultimes, modélisation Pareto/GPD de la sévérité, construction de la prime commerciale (chargement de sécurité k·σ + coût du capital + frais). Livrables : scripts Python, classeur Excel récapitulatif et rapport Word académique. Python, UCLouvain 2024.
• Project C: Closing actuariel & suivi des risques techniques — Pipeline complet de clôture actuarielle non-vie sur un portefeuille MTPL simulé : provisionnement stochastique, reporting et suivi des risques. Livrables : scripts Python (pipeline, reserving, tests), classeur Excel de pilotage et présentation. Python, 2025.
• Project D: Scoring de Confiance — Système complet de scoring de confiance : génération de données synthétiques, modélisation ML (Random Forest, XGBoost), benchmark multi-modèles, calibration des seuils et tarification commerciale. Livrables : scripts Python modulaires, noyau de scoring et rapport. Python, 2026.
• Content Creation: Vulgarisation de sujets actuariels (risque, provisionnement, statistiques) pour rendre ces notions accessibles à un public plus large.

• Modélisation IFRS 17 et reporting avancé sous Solvabilité II.
• Techniques de machine learning appliquées à l'actuariat (Python / Scikit-learn).